1
FORM 2
THE PATENTS ACT, 1970
(39 OF 1970)
&
5 THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
10 “METHOD AND SYSTEM FOR COUNTERS AND KEY
PERFORMANCE INDICATORS (KPIs) POLICY MANAGEMENT IN A
NETWORK”
15
We, Jio Platforms Limited, an Indian National, of Office - 101, Saffron, Nr. Centre
Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India.
20
The following specification particularly describes the invention and the manner in
which it is to be performed.
25
2
METHOD AND SYSTEM FOR COUNTERS AND KEY PERFORMANCE
INDICATORS (KPIs) POLICY MANAGEMENT IN A NETWORK
5
FIELD OF THE DISCLOSURE
[0001] Embodiments of the present disclosure generally relate to network
management systems. More particularly, embodiments of the present disclosure
10 relate to counters and key performance indicators (KPIs) policy management in a
network.
BACKGROUND
15 [0002] The following description of the related art is intended to provide
background information pertaining to the field of the disclosure. This section may
include certain aspects of the art that may be related to various features of the
present disclosure. However, it should be appreciated that this section is used only
to enhance the understanding of the reader with respect to the present disclosure,
20 and not as admissions of the prior art.
[0003] Network performance management systems typically track network
elements and data from network monitoring tools and combine and process such
data to determine key performance indicators (KPI) of the network. Integrated
25 performance management systems provide the means to visualize the network
performance data so that network operators and other relevant stakeholders are able
to identify the service quality of the overall network, and individual/ grouped
network elements. By having an overall as well as detailed view of the network
performance, the network operators can detect, diagnose and remedy actual service
30 issues, as well as predict potential service issues or failures in the network and take
precautionary measures accordingly.
3
[0004] Typically, in a mobile network, a network node or network element, such
as a base station, an access point (AP), a router, etc. collects event statistics in the
form of performance counters and sends them to network performance management
5 system for diagnostic purposes. These performance counters may be logged and
maintained by the management system in order to assess the performance of
network nodes. In order to catch the abnormalities, the user would need to check
the reports on regular basis. These results were also prone to human-error. Thus,
there is a need in the art to help the user by reducing the grunt work and automating
10 the tasks which need to be performed after having observed any kind of breaches.
[0005] Also, KPI values act as metrics for some real-world problems. The current
KPI values are analysed and compared with the past values for getting the trend in
terms of the absolute change as well as the percentage change. The people, who
15 perform the monitoring and observation tasks, take note of every kind of changes
happening in the KPIs they are held responsible for. Normally, user would
download an excel report from a dashboard page and perform some calculations in
excel to get the increment or decrement or none type changes for the date he/she
has chosen. Graph can be used to visualize the ups and downs in KPIs.
20
[0006] Thus, there exists an imperative need in the art to provide a system and a
method for providing counter and KPI policy management, which the present
disclosure aims to address. The present disclosure efficiently brings the time spent
in the tedious work to none and allows the user to focus on the task at hand, which
25 is monitoring. This will help the user by reducing the grunt work and automating
the tasks which need to be performed after having observed any kind of breaches.
SUMMARY
30 [0007] This section is provided to introduce certain aspects of the present
disclosure in a simplified form that are further described below in the detailed
4
description. This summary is not intended to identify the key features or the scope
of the claimed subject matter.
[0008] An aspect of the present disclosure may relate to a method for counters and
5 key performance indicators (KPIs) policy management in a network. The method
comprises transmitting, by a transceiver unit, from a cron scheduler a request for
execution of one or more policies at a pre-defined interval to an integrated
performance management (IPM) module. The method further comprises receiving,
by the transceiver unit, at the IPM module, a request for a report comprising a set
10 of counters and a set of KPIs. Further, the method comprises identifying, by an
identification unit, at the IPM module, a set of policies from the one or more policies
comprising the set of counters and the set of KPIs. Furthermore, the method
comprises evaluating, by an evaluation unit, at the IPM module, the set of policies
comprising the set of counters and the set of KPIs based on a set of severity breach
15 thresholds. Hereinafter, the method comprises identifying, by the identification
unit, at the IPM module, a set of breach conditions associated with the set of
counters and the set of KPIs based on the evaluation on the set of severity breach
thresholds. Further, the method comprises generating, by a report generation unit,
at the IPM, one or more reports comprising the set of breach conditions, wherein
20 the breach conditions are calibrated based on the severity breach thresholds. The
method further comprises sending, by the transceiver unit, from the IPM module,
the one or more reports to one or more users based on the set of policies.
[0009] In an exemplary aspect of the present disclosure, prior to transmitting the
25 request for execution of one or more policies from the cron scheduler to the IPM,
the method comprises creating, at a user interface unit, the one or more policies.
Each of the policy from the one or more policies is associated with a data. The
method further comprises transmitting, by the user interface unit to the IPM, the
one or more policies comprising the data. Further, the method comprises storing,
30 by a storage unit, at the IPM, the one or more polices in a database. Furthermore,
5
the method comprises forwarding, by the transceiver unit, from the IPM to the cron
scheduler, a request to schedule the one or more policies based on the data.
[0010] In an exemplary aspect of the present disclosure, the data associated with
5 each of the policy from the one or more policies comprises one or more counters,
one or more KPIs, one or more aggregation levels associated with each KPI from
the one or more KPIs, a schedule associated with each counter from the one or more
counters, a schedule associated with each KPI from the one or more KPIs, one or
more severity breach threshold values associated with each of the KPI from the one
10 or more KPIs, one or more severity breach threshold values associated with each of
the counter from the one or more counters, one or more notification templates and
a user notification group information.
[0011] In an exemplary aspect of the present disclosure, the schedule associated
15 with each counter from the one or more counters and the schedule associated with
each KPI from the one or more KPIs comprises a time interval type and a time
interval size.
[0012] In an exemplary aspect of the present disclosure, the one or more severity
20 breach threshold values associated with each of the KPI from the one or more KPIs
and the one or more severity breach threshold values associated with each of the
counter from the one or more counters is associated with one or more severities.
[0013] In an exemplary aspect of the present disclosure, the set of breach conditions
25 associated with the set of counters and the set of KPIs is identified in an event a
current value of each of the counter from the set of counters and each of the KPI
from the set of KPIs exceeds a corresponding severity breach threshold from the set
of severity breach thresholds.
30 [0014] In an exemplary aspect of the present disclosure, the method further
comprises sending, by the IPM, the set of breach conditions to a learning module.
6
Further, the method comprises calibrating, by a calibration unit, at the learning
module, the severity breach thresholds associated with the set of breach conditions.
The calibration is based on a set of factors comprising at least one of a weather, a
holiday and a disaster. Furthermore, the method comprises modifying, by the
5 calibration unit, the severity breach thresholds for the set of policies. The method
further comprises storing, by the storage unit, by the learning module, the modified
severity breach thresholds for the set of policies in the database.
[0015] In an exemplary aspect of the present disclosure, post receiving, by the
10 transceiver unit, at the IPM, the request for the report comprising the set of counters
and the set of KPIs, the method comprises running, by an execution unit, at the cron
scheduler, a cron for the set of KPIs and the set of counters.
[0016] In an exemplary aspect of the present disclosure, for generating the one or
15 more reports by the report generation unit, at the IPM, the severity breach thresholds
are fetched from the database.
[0017] In an exemplary aspect of the present disclosure, the method further
comprises triggering, by an alert unit, one or more alarms based on the set of breach
20 conditions.
[0018] In an exemplary aspect of the present disclosure, the one or more reports
sent to the one or more users comprises a delta KPI report, wherein the delta relates
to the difference in result between the previously sent reports and the generated one
25 or more reports.
[0019] Another aspect of the present disclosure may relate to a system for counters
and key performance indicators (KPIs) policy management in a network. The
system comprises a transceiver unit. The transceiver unit is configured to transmit,
30 from a cron scheduler, a request for execution of one or more policies at a predefined interval to an integrated performance management (IPM). The transceiver
7
unit is further configured to receive, at the IPM, a request for a report comprising a
set of counters and a set of KPIs. The system further comprises an identification
unit. The identification unit is configured to identify at the IPM, a set of policies
from the one or more policies comprising the set of counters and the set of KPIs.
5 Further, the system comprises an evaluation unit. The evaluation unit is configured
to evaluate at the IPM, the set of policies comprising the set of counters and the set
of KPIs based on a set of severity breach thresholds. The identification unit is
configured to identify at the IPM, a set of breach conditions associated with the set
of counters and the set of KPIs based on the evaluation on a set of severity breach
10 thresholds. The system further comprises a report generation unit. The report
generation unit is configured to generate at the IPM, one or more reports comprising
the set of breach conditions. The breach conditions are calibrated based on the
severity breach thresholds. The transceiver unit is configured to send from the IPM,
the one or more reports to one or more users based on the set of policies.
15
[0020] Yet another aspect of the present disclosure relates to a User Equipment
(UE). The UE comprises a user interface unit. The user interface unit is configured
to create, one or more policies comprising a set of counters and a set of KPIs. The
UE comprises a transceiver unit to send a request to a load balancer to save the one
20 or more policies. The transceiver unit is further configured to send a request, for
fetching a result for the set of counters and the set of KPIs. The transceiver unit is
further configured to receive, a report comprising the result for the set of counters
and the set of KPIs. The result comprises one or more highlights for one or more
breach conditions. The result is generated by a system comprising a transceiver unit,
25 configured to transmit, from a cron scheduler, a request for execution of the one or
more policies at a pre-defined interval to an integrated performance management
(IPM). The transceiver unit is configured to receive, at the IPM, a request for the
report comprising the set of counters and the set of KPIs. The system comprises an
identification unit, configured to identify at the IPM, a set of policies from the one
30 or more policies comprising the set of counters and the set of KPIs. The system
comprises an evaluation unit, configured to evaluate at the IPM, the set of policies
8
comprising the set of counters and the set of KPIs based on a set of severity breach
thresholds. The system further comprises the identification unit, configured to
identify at the IPM, a set of breach conditions associated with the set of counters
and the set of KPIs based on the evaluation on a set of severity breach thresholds.
5 The system further comprises a report generation unit, configured to generate at the
IPM, one or more reports comprising the set of breach conditions, wherein the
breach conditions are calibrated based on the severity breach thresholds. The
transceiver unit of the system is further configured to send from the IPM, the one
or more reports to the user interface unit of the UE based on the set of policies.
10
[0021] Yet another aspect of the present disclosure may relate to a non-transitory
computer readable storage medium storing instructions for counters and key
performance indicator (KPIs) policy management in a network, the instructions
include executable code which, when executed by one or more units of a system
15 cause a transceiver unit to transmit, from a cron scheduler, a request for execution
of one or more policies at a pre-defined interval to an integrated performance
management (IPM). The instructions when executed by the system further cause
the transceiver unit to receive, at the IPM, a request for a report comprising a set of
counters and a set of KPIs. The instructions when executed by the system further
20 cause an identification unit to identify at the IPM, a set of policies from the one or
more policies comprising the set of counters and the set of KPIs. The instructions
when executed by the system further cause an evaluation unit to evaluate at the
IPM, the set of policies comprising the set of counters and the set of KPIs based on
a set of severity breach thresholds. The instructions when executed by the system
25 further cause the identification unit to identify at the IPM, a set of breach conditions
associated with the set of counters and the set of KPIs based on the evaluation on a
set of severity breach thresholds. The instructions when executed by the system
further cause a report generation unit to generate at the IPM, one or more reports
comprising the set of breach conditions, wherein the breach conditions are
30 calibrated based on the severity breach thresholds. The instructions when executed
9
by the system further cause the transceiver unit to send from the IPM, the one or
more reports to one or more users based on the set of policies.
OBJECTS OF THE DISCLOSURE
5
[0022] Some of the objects of the present disclosure, which at least one
embodiment disclosed herein satisfies are listed herein below.
[0023] It is an object of the present disclosure to provide a system and a method for
10 providing counter and policy management for creating and scheduling the policies
for each KPI individually for regular observation.
[0024] It is another object of the present disclosure to reduce the grunt work and
automate the tasks which need to be performed after having observed any kind of
15 breaches.
[0025] It is another object of the present disclosure to provide a solution through
which one single policy for a Counter or KPI gets applied across many of the IPM
modules like in live monitoring, report generation without extra efforts.
20
[0026] It is yet another object of the present disclosure to devise a tool to calibrate
the thresholds of the policies according to the weather, holiday, and disasters to
overcome the unforeseen turn of events.
25 DESCRIPTION OF THE DRAWINGS
[0027] The accompanying drawings, which are incorporated herein, and constitute
a part of this disclosure, illustrate exemplary embodiments of the disclosed methods
and systems in which like reference numerals refer to the same parts throughout the
30 different drawings. Components in the drawings are not necessarily to scale,
emphasis instead being placed upon clearly illustrating the principles of the present
10
disclosure. Also, the embodiments shown in the figures are not to be construed as
limiting the disclosure, but the possible variants of the method and system
according to the disclosure are illustrated herein to highlight the advantages of the
disclosure. It will be appreciated by those skilled in the art that disclosure of such
5 drawings includes disclosure of electrical components or circuitry commonly used
to implement such components.
[0028] FIG. 1A illustrates an exemplary block diagram of a network performance
management system.
10
[0029] FIG. 1B illustrates an exemplary block diagram representation of a
management and orchestration (MANO) architecture/ platform, in accordance with
exemplary implementation of the present disclosure.
15 [0030] FIG. 2 illustrates an exemplary block diagram of a computing device upon
which the features of the present disclosure may be implemented in accordance with
exemplary implementation of the present disclosure.
[0031] FIG. 3 illustrates an exemplary block diagram of a system for counters and
20 key performance indicator (KPIs) policy management in a network, in accordance
with exemplary implementations of the present disclosure.
[0032] FIG. 4 illustrates a method flow diagram for counters and key performance
indicator (KPIs) policy management in a network, in accordance with exemplary
25 implementations of the present disclosure.
[0033] FIG. 5 illustrates an exemplary implementation of the system for counters
and key performance indicator (KPIs) policy management in a network, in
accordance with exemplary implementations of the present disclosure.
30
11
[0034] FIG. 6 illustrates an implementation of an exemplary signal flow diagram
for creating a policy and starting cron scheduling for the selected KPI and policies,
in accordance with exemplary implementations of the present disclosure.
5 [0035] FIG. 7. illustrates an implementation of a signal flow diagram for counters
and key performance indicator (KPIs) policy management in a network, in
accordance with exemplary implementations of the present disclosure.
[0036] FIG. 8 illustrates an implementation of an exemplary signal flow diagram
10 for showing a highlighted result to the user based on the user request for delta and
KPI data, in accordance with exemplary implementations of the present disclosure
is shown.
[0037] The foregoing shall be more apparent from the following more detailed
15 description of the disclosure.
DETAILED DESCRIPTION
[0038] In the following description, for the purposes of explanation, various
20 specific details are set forth in order to provide a thorough understanding of
embodiments of the present disclosure. It will be apparent, however, that
embodiments of the present disclosure may be practiced without these specific
details. Several features described hereafter may each be used independently of one
another or with any combination of other features. An individual feature may not
25 address any of the problems discussed above or might address only some of the
problems discussed above.
[0039] The ensuing description provides exemplary embodiments only, and is not
intended to limit the scope, applicability, or configuration of the disclosure. Rather,
30 the ensuing description of the exemplary embodiments will provide those skilled in
the art with an enabling description for implementing an exemplary embodiment.
12
It should be understood that various changes may be made in the function and
arrangement of elements without departing from the spirit and scope of the
disclosure as set forth.
5 [0040] Specific details are given in the following description to provide a thorough
understanding of the embodiments. However, it will be understood by one of
ordinary skill in the art that the embodiments may be practiced without these
specific details. For example, circuits, systems, processes, and other components
may be shown as components in block diagram form in order not to obscure the
10 embodiments in unnecessary detail.
[0041] Also, it is noted that individual embodiments may be described as a process
which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure
diagram, or a block diagram. Although a flowchart may describe the operations as
15 a sequential process, many of the operations may be performed in parallel or
concurrently. In addition, the order of the operations may be re-arranged. A process
is terminated when its operations are completed but could have additional steps not
included in a figure.
20 [0042] The word “exemplary” and/or “demonstrative” is used herein to mean
serving as an example, instance, or illustration. For the avoidance of doubt, the
subject matter disclosed herein is not limited by such examples. In addition, any
aspect or design described herein as “exemplary” and/or “demonstrative” is not
necessarily to be construed as preferred or advantageous over other aspects or
25 designs, nor is it meant to preclude equivalent exemplary structures and techniques
known to those of ordinary skill in the art. Furthermore, to the extent that the terms
“includes,” “has,” “contains,” and other similar words are used in either the detailed
description or the claims, such terms are intended to be inclusive—in a manner
similar to the term “comprising” as an open transition word—without precluding
30 any additional or other elements.
13
[0043] As used herein, a “processing unit” or “processor” or “operating processor”
includes one or more processors, wherein processor refers to any logic circuitry for
processing instructions. A processor may be a general-purpose processor, a special
purpose processor, a conventional processor, a digital signal processor, a plurality
5 of microprocessors, one or more microprocessors in association with a (Digital
Signal Processing) DSP core, a controller, a microcontroller, Application Specific
Integrated Circuits, Field Programmable Gate Array circuits, any other type of
integrated circuits, etc. The processor may perform signal coding data processing,
input/output processing, and/or any other functionality that enables the working of
10 the system according to the present disclosure. More specifically, the processor or
processing unit is a hardware processor.
[0044] As used herein, “a user equipment”, “a user device”, “a smart-user-device”,
“a smart-device”, “an electronic device”, “a mobile device”, “a handheld device”,
15 “a wireless communication device”, “a mobile communication device”, “a
communication device” may be any electrical, electronic and/or computing device
or equipment, capable of implementing the features of the present disclosure. The
user equipment/device may include, but is not limited to, a mobile phone, smart
phone, laptop, a general-purpose computer, desktop, personal digital assistant,
20 tablet computer, wearable device or any other computing device which is capable
of implementing the features of the present disclosure. Also, the user device may
contain at least one input means configured to receive an input from at least one of
a transceiver unit, a processing unit, a storage unit, a detection unit and any other
such unit(s) which are required to implement the features of the present disclosure.
25
[0045] As used herein, “storage unit” or “memory unit” refers to a machine or
computer-readable medium including any mechanism for storing information in a
form readable by a computer or similar machine. For example, a computer-readable
medium includes read-only memory (“ROM”), random access memory (“RAM”),
30 magnetic disk storage media, optical storage media, flash memory devices or other
types of machine-accessible storage media. The storage unit stores at least the data
14
that may be required by one or more units of the system to perform their respective
functions.
[0046] As used herein “interface” or “user interface” refers to a shared boundary
5 across which two or more separate components of a system exchange information
or data. The interface may also be referred to a set of rules or protocols that define
communication or interaction of one or more modules or one or more units with
each other, which also includes the methods, functions, or procedures that may be
called.
10
[0047] All modules, units, components used herein, unless explicitly excluded
herein, may be software modules or hardware processors, the processors being a
general-purpose processor, a special purpose processor, a conventional processor,
a digital signal processor (DSP), a plurality of microprocessors, one or more
15 microprocessors in association with a DSP core, a controller, a microcontroller,
Application Specific Integrated Circuits (ASIC), Field Programmable Gate Array
circuits (FPGA), any other type of integrated circuits, etc.
[0048] As used herein the transceiver unit include at least one receiver and at least
20 one transmitter configured respectively for receiving and transmitting data, signals,
information or a combination thereof between units/components within the system
and/or connected with the system.
[0049] As discussed in the background section, the current known solutions have
25 several shortcomings. The present disclosure aims to overcome the abovementioned and other existing problems in this field of technology by providing
method and system of counters and key performance indicator (KPIs) policy
management in a network.
30 [0050] Referring to FIG. 1A, an exemplary block diagram of a network
performance management system [100A], in accordance with the exemplary
15
embodiments of the present invention is shown. Referring to Fig. 1A, the network
performance management system [100A] comprises various sub-systems such as:
an integrated performance management system [100a], a normalization layer
[100b], a computation layer [100d], an anomaly detection layer [100o], a streaming
5 engine [100l], a load balancer [100k], an operations and management system
[100p], an API gateway system [100r], an analysis engine [100h], a parallel
computing framework [100i], a forecasting engine [100t], a distributed file system
[100j], a mapping layer [100s], a distributed data lake [100u], a scheduling layer
[100g], a reporting engine [100m], a message broker [100e], a graph layer [100f],
10 a caching layer [100c], a service quality manager [100q] and a correlation
engine[100n]. Exemplary connections between these subsystems are also shown in
FIG. 1A. However, it will be appreciated by those skilled in the art that the present
disclosure is not limited to the connections shown in the diagram, and any other
connections between various subsystems that are needed to realise the effects are
15 within the scope of this disclosure.
[0051] Following are the various components of the system [100A], as shown in
FIG. 1A:
20 - Integrated Performance Management (IPM) system [100a] comprises a 5G
performance engine [100v] and a 5G Key Performance Indicator (KPI) Engine
[100w].
- 5G Performance Management Engine [100v]: The 5G Performance
25 Management engine [100v] is a crucial component of the IPM system [100a],
responsible for collecting, processing, and managing performance counter data
from various data sources within the network. The counter data includes metrics
such as connection speed, latency, data transfer rates, and many others. The
counter data is then processed and aggregated as required, forming a
30 comprehensive overview of network performance. The processed information
is then stored in the Distributed Data Lake [100u]. The Distributed data lake
16
[100u] is a centralized, scalable, and flexible storage solution, allowing for easy
access and further analysis. The 5G Performance Management engine [100v]
also enables the reporting and visualization of the performance counter data,
thus providing network administrators with a real-time, insightful view of the
5 network's operation. Through these visualizations, operators can monitor the
network's performance, identify potential issues, and make informed decisions
to enhance network efficiency and reliability. An operator in the IPM system
[100a] may be an individual, a device, an administrator, and the like who may
interact with or manage the network.
10
- 5G Key Performance Indicator (KPI) Engine [100w]: The 5G Key
Performance Indicator (KPI) Engine [100w] is a dedicated component tasked
with managing the KPIs of all the network elements. The 5G Key Performance
Indicator (KPI) Engine [100w] uses the performance counters, which are
15 collected and processed by the 5G Performance Management engine [100v]
from various data sources. These counters, encapsulating crucial performance
data, are harnessed by the KPI engine [100w] to calculate essential KPIs. These
KPIs may include at least one of: data throughput, latency, packet loss rate, and
more. Once the KPIs are computed, the KPIs are segregated based on the
20 aggregation requirements, offering a multi-layered and detailed understanding
of the network performance. The processed KPI data is then stored in the
Distributed Data Lake [100u], ensuring a highly accessible, centralized, and
scalable data repository for further analysis and utilization. Similar to the 5G
Performance Management engine [100v], the 5G KPI engine [100w] is also
25 responsible for reporting and visualization of KPI data. This functionality
allows network administrators to gain a comprehensive, visual understanding
of the network's performance, thus supporting informed decision-making and
efficient network management.
30 - Ingestion layer: The Ingestion layer (not shown in FIG. 1A) forms a key part
of the IPM system [100a]. The ingestion layer primarily performs the function
17
to establish an environment capable of handling diverse types of incoming data.
This data may include Alarms, Counters, Configuration parameters, Call Detail
Records (CDRs), Infrastructure metrics, Logs, and Inventory data, all of which
are crucial for maintaining and optimizing the network's performance. Upon
5 receiving this data, the Ingestion layer processes the data by validating the data
integrity and correctness to ensure that the data is fit for further use. Following
the validation, the data is routed to various components of the IPM system
[100a], including the Normalization layer [100b], Streaming Engine [100l],
Streaming Analytics, and Message Brokers [100e]. The destination is chosen
10 based on where the data is required for further analytics and processing. By
serving as the first point of contact for incoming data, the Ingestion layer plays
a vital role in managing the data flow within the system, thus supporting
comprehensive and accurate network performance analysis.
15 - Normalization layer [100b]: The Normalization Layer [100b] serves to
standardize, enrich, and store data into the appropriate databases. It takes in data
that has been ingested and adjusts it to a common standard, making it easier to
compare and analyse. This process of "normalization" reduces redundancy and
improves data integrity. Upon completion of normalization, the data is stored in
20 various databases like the Distributed Data Lake [100u], Caching Layer [100c],
and Graph Layer [100f], depending on its intended use. The choice of storage
determines how the data can be accessed and used in the future. Additionally,
the Normalization Layer [100b] produces data for the Message Broker [100e],
a system that enables communication between different parts of the integrated
25 performance management system [100a] through the exchange of data
messages. Moreover, the Normalization Layer [100b] supplies the standardized
data to several other subsystems. These include the Analysis Engine [100h] for
detailed data examination, the Correlation Engine [100n] for detecting
relationships among various data elements, the Service Quality Manager [100q]
30 for maintaining and improving the quality of services, and the Streaming Engine
[100l] for processing real-time data streams. These subsystems depend on the
18
normalized data to perform their operations effectively and accurately,
demonstrating the Normalization Layer's [100b] critical role in the entire
system.
5 - Caching layer [100c]: The Caching Layer [100c] in the IPM system [100a]
plays a significant role in data management and optimization. During the initial
phase, the Normalization Layer [100b] processes incoming raw data to create a
standardized format, enhancing consistency and comparability. The Normalizer
Layer then inserts this normalized data into various databases. One such
10 database is the Caching Layer [100c]. The Caching Layer [100c] is a high-speed
data storage layer which temporarily holds data that is likely to be reused, to
improve speed and performance of data retrieval. By storing frequently
accessed data in the Caching Layer [100c], the system significantly reduces the
time taken to access this data, improving overall system efficiency and
15 performance. Further, the Caching Layer [100c] serves as an intermediate layer
between the data sources and the sub-systems, such as the Analysis Engine,
Correlation Engine [100n], Service Quality Manager, and Streaming Engine.
The Normalization Layer [100b] is responsible for providing these sub-systems
with the necessary data from the Caching Layer [100c].
20
- Computation layer [100d]: The Computation Layer [100d] in the IPM system
[100a] serves as the main hub for complex data processing tasks. In the initial
stages, raw data is gathered, normalized, and enriched by the Normalization
Layer [100b]. The Normalizer Layer [100b] then inserts this standardized data
25 into multiple databases including the Distributed Data Lake [100u], Caching
Layer [100c], and Graph Layer [100f], and also feeds it to the Message Broker
[100e]. Within the Computation Layer [100d], several powerful sub-systems
such as the Analysis Engine [100h], Correlation Engine [100n], Service Quality
Manager [100q], and the Streaming Engine [100l], utilize the normalized data.
30 These systems are designed to execute various data processing tasks. The
Analysis Engine [100h] performs in-depth data analytics to generate insights
19
from the data. The Correlation Engine [100n] identifies and understands the
relations and patterns within the data. The Service Quality Manager [100q]
assesses and ensures the quality of the services. And the Streaming Engine
[100l] processes and analyses the real-time data feeds. In essence, the
5 Computation Layer [100d] is where all major computation and data processing
tasks occur. It uses the normalized data provided by the Normalization Layer
[100b], processing it to generate useful insights, ensure service quality,
understand data patterns, and facilitate real-time data analytics.
10 - Message broker [100e]: The Message Broker [100e], an integral part of the
IPM system [100a], operates as a publish-subscribe messaging system. It
orchestrates and maintains the real-time flow of data from various sources and
applications. At its core, the Message Broker [100e] facilitates communication
between data producers and consumers through message-based topics. This
15 creates an advanced platform for contemporary distributed applications. With
the ability to accommodate a large number of permanent or ad-hoc consumers,
the Message Broker [100e] demonstrates immense flexibility in managing data
streams. Moreover, it leverages the filesystem for storage and caching, boosting
its speed and efficiency. The design of the Message Broker [100e] is centred
20 around reliability. It is engineered to be fault-tolerant and mitigate data loss,
ensuring the integrity and consistency of the data. With its robust design and
capabilities, the Message Broker [100e] forms a critical component in managing
and delivering real-time data in the system.
25 - Graph layer [100f]: The Graph Layer [100f] plays a pivotal role in the IPM
system [100a]. It can model a variety of data types, including alarm, counter,
configuration, CDR data, Infra-metric data, 5G Probe Data, and Inventory data.
Equipped with the capability to establish relationships among diverse types of
data, The Graph Layer [100f] acts as a Relationship Modeler that offers
30 extensive modelling capabilities. For instance, it can model Alarm and Counter
data, probe and Alarm data, elucidating their interrelationships. Moreover, the
20
Relationship Modeler should adapt at processing steps provided in the model
and delivering the results to the system requested, whether it be a Parallel
Computing system, Workflow Engine, Query Engine, Correlation Engine
[100n], 5G Performance Management Engine, or 5G KPI Engine [100w]. With
5 its powerful modelling and processing capabilities, the Graph Layer [100f]
forms an essential part of the system, enabling the processing and analysis of
complex relationships between various types of network data.
- Scheduling layer [100g]: The Scheduling Layer [100g] serves as a key element
10 of the IPM System [100a], endowed with the ability to execute tasks at
predetermined intervals set according to user preferences. A task might be an
activity performing a service call, an API call to another microservice, the
execution of an Elastic Search query, and storing its output in the Distributed
Data Lake [100u] or Distributed File System or sending it to another micro15 service. The micro-service refers to a single system architecture to provide
multiple functions. Some of the microservices in communication are API calls
and remote procedure calls. The versatility of the Scheduling Layer [100g]
extends to facilitating graph traversals via the Mapping Layer to execute tasks.
This crucial capability enables seamless and automated operations within the
20 system, ensuring that various tasks and services are performed on schedule,
without manual intervention, enhancing the system's efficiency and
performance. In sum, the Scheduling Layer [100g] orchestrates the systematic
and periodic execution of tasks, making it an integral part of the efficient
functioning of the entire system.
25
- Analysis Engine [100h]: The Analysis Engine [100h] forms a crucial part of
the IPM System [100a], designed to provide an environment where users can
configure and execute workflows for a wide array of use-cases. This facility
aids in the debugging process and facilitates a better understanding of call flows.
30 With the Analysis Engine [100h], users can perform queries on data sourced
from various subsystems or external gateways. This capability allows for an in-
21
depth overview of data and aids in pinpointing issues. The system's flexibility
allows users to configure specific policies aimed at identifying anomalies within
the data. When these policies detect abnormal behaviour or policy breaches, the
system sends notifications, ensuring swift and responsive action. In essence, the
5 Analysis Engine [100h] provides a robust analytical environment for systematic
data interrogation, facilitating efficient problem identification and resolution,
thereby contributing significantly to the system's overall performance
management.
10 - Parallel Computing Framework [100i]: The Parallel Computing Framework
[100i] is a key aspect of the Integrated Performance Management System
[100a], providing a user-friendly yet advanced platform for executing
computing tasks in parallel. The parallel computing framework [100i]
showcases both scalability and fault tolerance, crucial for managing vast
15 amounts of data. Users can input data via Distributed File System (DFS) [100j]
locations or Distributed Data Lake (DDL) indices. The framework supports the
creation of task chains by interfacing with the Service Configuration
Management (SCM) Sub-System. Each task in a workflow is executed
sequentially, but multiple chains can be executed simultaneously, optimizing
20 processing time. To accommodate varying task requirements, the service
supports the allocation of specific host lists for different computing tasks. The
Parallel Computing Framework [100i] is an essential tool for enhancing
processing speeds and efficiently managing computing resources, significantly
improving the system's performance management capabilities.
25
- Distributed File System [100j]: The Distributed File System (DFS) [100j] is a
critical component of the Integrated Performance Management System [100a],
enabling multiple clients to access and interact with data seamlessly. The
Distributed File system [100j] is designed to manage data files that are
30 partitioned into numerous segments known as chunks. In the context of a
network with vast data, the DFS [100j] effectively allows for the distribution of
22
data across multiple nodes. This architecture enhances both the scalability and
redundancy of the system, ensuring optimal performance even with large data
sets. DFS [100j] also supports diverse operations, facilitating the flexible
interaction with and manipulation of data. This accessibility is paramount for a
5 system that requires constant data input and output, as is the case in a robust
performance management system.
- Load Balancer [100k]: The Load Balancer (LB) [100k] is a vital component
of the Integrated Performance Management System [100a], designed to
10 efficiently distribute incoming network traffic across a multitude of backend
servers or microservices. Its purpose is to ensure the even distribution of data
requests, leading to optimized server resource utilization, reduced latency, and
improved overall system performance. The LB [100k] implements various
routing strategies to manage traffic. The LB [100k] includes round-robin
15 scheduling, header-based request dispatch, and context-based request dispatch.
Round-robin scheduling is a simple method of rotating requests evenly across
available servers. In contrast, header and context-based dispatching allow for
more intelligent, request-specific routing. Header-based dispatching routes
requests based on data contained within the headers of the Hypertext Transfer
20 Protocol (HTTP) requests. Context-based dispatching routes traffic based on
the contextual information about the incoming requests. For example, in an
event-driven architecture, the LB [100k] manages event and event
acknowledgments, forwarding requests or responses to the specific
microservice that has requested the event. This system ensures efficient,
25 reliable, and prompt handling of requests, contributing to the robustness and
resilience of the overall performance management system.
- Streaming Engine [100l]: The Streaming Engine [100l], also referred to as
Stream Analytics, is a critical subsystem in the Integrated Performance
30 Management System [100a]. This engine is specifically designed for high-speed
data pipelining to the User Interface (UI). Its core objective is to ensure real-
23
time data processing and delivery, enhancing the system's ability to respond
promptly to dynamic changes. Data is received from various connected
subsystems and processed in real-time by the Streaming Engine [100l]. After
processing, the data is streamed to the UI, fostering rapid decision-making and
5 responses. The Streaming Engine [100l] cooperates with the Distributed Data
Lake [100u], Message Broker [100e], and Caching Layer [100c] to provide
seamless, real-time data flow. Stream Analytics is designed to perform required
computations on incoming data instantly, ensuring that the most relevant and
up-to-date information is always available at the UI. Furthermore, this system
10 can also retrieve data from the Distributed Data Lake [100u], Message Broker
[100e], and Caching Layer [100c] as per the requirement and deliver it to the
UI in real-time. The streaming engine's [100l] is configured to provide fast,
reliable, and efficient data streaming, contributing to the overall performance of
the Integrated Performance Management System [100a].
15
- Reporting Engine [100m]: The Reporting Engine [100m] is a key subsystem
of the Integrated Performance Management System [100a]. The fundamental
purpose of designing the Reporting Engine [100m] is to dynamically create
report layouts of API data, catered to individual client requirements, and deliver
20 these reports via the Notification Engine. The REM serves as the primary
interface for creating custom reports based on the data visualized through the
client's dashboard. These custom dashboards, created by the client through the
User Interface (UI), provide the basis for the Reporting Engine [100m] to
process and compile data from various interfaces. The main output of the
25 Reporting Engine [100m] is a detailed report generated in Excel format. The
Reporting Engine’s [100m] unique capability to parse data from different
subsystem interfaces, process it according to the client's specifications and
requirements, and generate a comprehensive report makes it an essential
component of this performance management system. Furthermore, the
30 Reporting Engine [100m] integrates seamlessly with the Notification Engine to
ensure timely and efficient delivery of reports to clients via email, ensuring the
24
information is readily accessible and usable, thereby improving overall client
satisfaction and system usability.
- The Correlation Engine [100n]: The correlation engine [100n] provides
5 provisioning support. A correlation model can be provisioned from UI and
associated with single/multiple trigger points to run a particular correlation. It
can be triggered automatically as soon as triggers are received from different
components in the platform across alarm, counter, KPI, CDR, and metric data
against a provisioned source trigger point. The correlation engine [100n] also
10 provides hypothesis validation support for an on-demand execution feature for
different types of correlation, providing an output that can be visualized on UI
or exported from the UI. The correlation engine [100n] may use learning models
and machine learning algorithms to correlate the alarms with the raw data or
clear codes or infrastructure events received from other systems. The
15 correlation engine constantly monitors and compares the collected data with the
baseline behaviour to detect any deviations. On any violation, the pre-defined
remediation action is triggered in order to maintain network consistency
- The Anomaly Detection Layer [100o]: The Anomaly Detection Layer [100o]
20 is another key subsystem of the IPM system [100a]. The fundamental purpose
of the Anomaly detection layer [100o] is to identify and detect anomalies. The
anomaly detection layer [100o] may drill down to the level of the server and
precisely identify the problematic elements in the network.
25 [0052] FIG. 1B illustrates an exemplary block diagram representation of a
management and orchestration (MANO) architecture/ platform [100B], in
accordance with exemplary implementation of the present disclosure. The MANO
architecture [100B] is developed for managing telecom cloud infrastructure
automatically, managing design or deployment design, managing instantiation of
30 network node(s)/ service(s) etc. The MANO architecture [100B] deploys the
network node(s) in the form of Virtual Network Function (VNF) and Cloud-native/
25
Container Network Function (CNF). The system may comprise one or more
components of the MANO architecture [100B]. The MANO architecture [100B] is
used to auto-instantiate the VNFs into the corresponding environment of the present
disclosure so that it could help in onboarding other vendor(s) CNFs and VNFs to
5 the platform.
[0053] As shown in FIG. 1B, the MANO architecture [100B] comprises a user
interface layer, a network function virtualization (NFV) and software defined
network (SDN) design function module [104], a platforms foundation services
10 module [106], a platform core services module [108] and a platform resource
adapters and utilities module [112]. All the components are assumed to be
connected to each other in a manner as obvious to the person skilled in the art for
implementing features of the present disclosure.
15 [0054] The NFV and SDN design function module [104] comprises a VNF
lifecycle manager (compute) [1042], a VNF catalog [1044], a network services
catalog [1046], a network slicing and service chaining manager [1048], a physical
and virtual resource manager [1050] and a CNF lifecycle manager [1052]. The VNF
lifecycle manager (compute) [1042] is responsible for deciding on which server of
20 the communication network, the microservice will be instantiated. The VNF
lifecycle manager (compute) [1042] may manage the overall flow of incoming/
outgoing requests during interaction with the user. The VNF lifecycle manager
(compute) [1042] is responsible for determining which sequence to be followed for
executing the process. For e.g. in an AMF network function of the communication
25 network (such as a 5G network), sequence for execution of processes P1 and P2
etc. The VNF catalog [1044] stores the metadata of all the VNFs (also CNFs in
some cases). The network services catalog [1046] stores the information of the
services that need to be run. The network slicing and service chaining manager
[1048] manages the slicing (an ordered and connected sequence of network service/
30 network functions (NFs)) that must be applied to a specific networked data packet.
The physical and virtual resource manager [1050] stores the logical and physical
26
inventory of the VNFs. Just like the VNF lifecycle manager (compute) [1042], the
CNF lifecycle manager [1052] is used for the CNFs lifecycle management.
[0055] The platforms foundation services module [106] comprises a microservices
5 elastic load balancer [1062], an identity & access manager [1064], a command line
interface (CLI) [1066], a central logging manager [1068], and an event routing
manager [1070]. The microservices elastic load balancer [1062] is used for
maintaining the load balancing of the request for the services. The identity & access
manager [1064] is used for logging purposes. The command line interface (CLI)
10 [1066] is used to provide commands to execute certain processes which require
changes during the run time. The central logging manager [1068] is responsible for
keeping the logs of every service. These logs are generated by the MANO platform
[100B]. These logs are used for debugging purposes. The event routing manager
[1070] is responsible for routing the events i.e., the application programming
15 interface (API) hits to the corresponding services.
[0056] The platforms core services module [108] comprises NFV infrastructure
monitoring manager [1082], an assure manager [1084], a performance manager
[1086], a policy execution engine [1088], a capacity monitoring manager [1090], a
20 release management (mgmt.) repository [1092], a configuration manager & GCT
[1094], an NFV platform decision analytics [1096], a platform NoSQL DB [1098];
a platform schedulers and cron jobs [1100], a VNF backup & upgrade manager
[1102], a micro service auditor (MAUD) [1104], and a platform operations,
administration and maintenance manager [1106]. The NFV infrastructure
25 monitoring manager [1082] monitors the infrastructure part of the NFs. For e.g.,
any metrics such as CPU utilization by the VNF. The assure manager [1084] is
responsible for supervising the alarms the vendor is generating. The performance
manager [1086] is responsible for managing the performance counters. The policy
execution engine (PEGN) [1088] is responsible for all the managing the policies.
30 The capacity monitoring manager (CMM) [1090] is responsible for sending the
request to the PEGN [1088]. The release management (mgmt.) repository (RMR)
27
[1092] is responsible for managing the releases and the images of all the vendor
network node. The configuration manager & (GCT) [1094] manages the
configuration and GCT of all the vendors. The NFV platform decision analytics
(NPDA) [1096] helps in deciding the priority of using the network resources. It is
5 further noted that the policy execution engine (PEGN) [1088], the configuration
manager & GCT [1094] and the NPDA [1096] work together. The platform NoSQL
DB [1098] is a database for storing all the inventory (both physical and logical) as
well as the metadata of the VNFs and CNF. The platform schedulers and cron jobs
[1100] schedules the task such as but not limited to triggering of an event, traverse
10 the network graph etc. The VNF backup & upgrade manager [1102] takes backup
of the images, binaries of the VNFs and the CNFs and produces those backups on
demand in case of server failure. The micro service auditor [1104] audits the
microservices. For e.g., in a hypothetical case, instances not being instantiated by
the MANO architecture [100B] using the network resources then the micro service
15 auditor [1104] audits and informs the same so that resources can be released for
services running in the MANO architecture [100B], thereby assuring the services
only run on the MANO platform [100B]. The platform operations, administration
and maintenance manager [1106] is used for newer instances that are spawning.
20 [0057] The platform resource adapters and utilities module [112] further comprises
a platform external API adaptor and gateway [1122]; a generic decoder and indexer
(XML, CSV, JSON) [1124]; a service adaptor [1126]; an API adapter [1128]; and
a NFV gateway [1130]. The platform external API adaptor and gateway [1122] is
responsible for handling the external services (to the MANO platform [100B]) that
25 requires the network resources. The generic decoder and indexer (XML, CSV,
JSON) [1124] gets directly the data of the vendor system in the XML, CSV, JSON
format. The service adaptor [1126] is the interface provided between the telecom
cloud and the MANO architecture [100B] for communication. The API adapter
[1128] is used to connect with the virtual machines (VMs). The NFV gateway
30 [1130] is responsible for providing the path to each service going to/incoming from
the MANO architecture [100B].
28
[0058] FIG. 2 illustrates an exemplary block diagram of a computing device [200]
upon which the features of the present disclosure may be implemented in
accordance with exemplary implementation of the present disclosure. In an
5 implementation, the computing device [200] may also implement a method for
counters and key performance indicator (KPIs) policy management in a network,
utilising the system. In another implementation, the computing device [200] itself
implements the method for counters and key performance indicator (KPIs) policy
management in a network using one or more units configured within the computing
10 device [200], wherein said one or more units are capable of implementing the
features as disclosed in the present disclosure.
[0059] The computing device [200] may include a bus [202] or other
communication mechanism for communicating information, and a hardware
15 processor [204] coupled with bus [202] for processing information. The hardware
processor [204] may be, for example, a general-purpose microprocessor. The
computing device [200] may also include a main memory [206], such as a random
access memory (RAM), or other dynamic storage device, coupled to the bus [202]
for storing information and instructions to be executed by the processor [204]. The
20 main memory [206] also may be used for storing temporary variables or other
intermediate information during execution of the instructions to be executed by the
processor [204]. Such instructions, when stored in non-transitory storage media
accessible to the processor [204], render the computing device [200] into a specialpurpose machine that is customized to perform the operations specified in the
25 instructions. The computing device [200] further includes a read only memory
(ROM) [208] or other static storage device coupled to the bus [202] for storing
static information and instructions for the processor [204].
[0060] A storage device [210], such as a magnetic disk, optical disk, or solid-state
30 drive is provided and coupled to the bus [202] for storing information and
instructions. The computing device [200] may be coupled via the bus [202] to a
29
display [212], such as a cathode ray tube (CRT), Liquid crystal Display (LCD),
Light Emitting Diode (LED) display, Organic LED (OLED) display, etc. for
displaying information to a computer user. An input device [214], including
alphanumeric and other keys, touch screen input means, etc. may be coupled to the
5 bus [202] for communicating information and command selections to the processor
[204]. Another type of user input device may be a cursor controller [216], such as
a mouse, a trackball, or cursor direction keys, for communicating direction
information and command selections to the processor [204], and for controlling
cursor movement on the display [212]. This input device typically has two degrees
10 of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allow
the device to specify positions in a plane.
[0061] The computing device [200] may implement the techniques described
herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware
15 and/or program logic which in combination with the computing device [200] causes
or programs the computing device [200] to be a special-purpose machine.
According to one implementation, the techniques herein are performed by the
computing device [200] in response to the processor [204] executing one or more
sequences of one or more instructions contained in the main memory [206]. Such
20 instructions may be read into the main memory [206] from another storage medium,
such as the storage device [210]. Execution of the sequences of instructions
contained in the main memory [206] causes the processor [204] to perform the
process steps described herein. In alternative implementations of the present
disclosure, hard-wired circuitry may be used in place of or in combination with
25 software instructions.
[0062] The computing device [200] also may include a communication interface
[218] coupled to the bus [202]. The communication interface [218] provides a twoway data communication coupling to a network link [220] that is connected to a
30 local network [222]. For example, the communication interface [218] may be an
integrated services digital network (ISDN) card, cable modem, satellite modem, or
30
a modem to provide a data communication connection to a corresponding type of
telephone line. As another example, the communication interface [218] may be a
local area network (LAN) card to provide a data communication connection to a
compatible LAN. Wireless links may also be implemented. In any such
5 implementation, the communication interface [218] sends and receives electrical,
electromagnetic or optical signals that carry digital data streams representing
various types of information.
[0063] The computing device [200] can send messages and receive data, including
10 program code, through the network(s), the network link [220] and the
communication interface [218]. In the Internet example, a server [230] might
transmit a requested code for an application program through the Internet [228], the
ISP [226], the local network [222], the host [224] and the communication interface
[218]. The received code may be executed by the processor [204] as it is received,
15 and/or stored in the storage device [210], or other non-volatile storage for later
execution.
[0064] The present disclosure is implemented by a system [300] (as shown in FIG.
3). In an implementation, the system [300] may include the computing device [200]
20 (as shown in FIG. 2). It is further noted that the computing device [200] is able to
perform the steps of a method [400] (as shown in FIG. 4).
[0065] Referring to FIG. 3, an exemplary block diagram of a system [300] for
providing counters and key performance indicators (KPIs) policy management in a
25 network is shown, in accordance with the exemplary implementations of the present
disclosure. The system [300] comprises at least one transceiver unit [302], and at
least one execution unit [322] in at least one cron scheduler [304]. The system [300]
further comprises at least one transceiver unit [306], at least one identification unit
[308], at least one evaluation unit [310], at least one report generation unit [312], at
30 least one storage unit [314] and at least one alarm unit [324] in at least one IPM
[100a]. The system further comprises at least one calibration unit [318] in at least
31
one learning module [320]. Also, all of the components/ units of the system [300]
are assumed to be connected to each other unless otherwise indicated below. As
shown in the FIG. 3, all units shown within the system should also be assumed to
be connected to each other. Also, in FIG. 3 only a few units are shown, however,
5 the system [300] may comprise multiple such units or the system [300] may
comprise any such numbers of said units, as required to implement the features of
the present disclosure. Further, in an implementation, the system [300] may be
present in a user device to implement the features of the present disclosure. The
system [300] may be a part of the user device / or may be independent of but in
10 communication with the user device (may also be referred herein as a UE). In
another implementation, the system [300] may reside in a server or a network entity.
In yet another implementation, the system [300] may reside partly in the server/
network entity and partly in the user device.
15 [0066] The system [300] is configured for counters and key performance indicators
(KPIs) policy management in a network, with the help of the interconnection
between the components/units of the system [300].
[0067] Prior to transmitting a request for execution of one or more policies from
20 the cron scheduler [304] to the IPM [100a], the user interface unit [316] at the UE
is configured to create the one or more policies. Each of the policy from the one or
more policies is associated with a data. The data associated with each of the policy
from the one or more policies includes but may not be limited to one or more
counters, one or more KPIs, one or more aggregation levels associated with each
25 KPI from the one or more KPIs, a schedule associated with each counter from the
one or more counters, a schedule associated with each KPI from the one or more
KPIs, one or more severity breach threshold values associated with each of the KPI
from the one or more KPIs, one or more severity breach threshold values associated
with each of the counter from the one or more counters and an email group to
30 receive a KPI report. The policies can be created and scheduled for each KPI
individually for regular observation.
32
[0068] The one or more counters refers to raw metrics which is collected from
various network entities to detect a specific event. For example, the number of times
a request fails to be delivered or a number of times a response is not received. The
5 one or more KPIs are created from the one or more counters. For example, a KPI
can be created to assess a success rate of request delivery, based on the counters
that collect metrics related to number of requests delivered and number of requests
failed to be delivered. The aggregation levels associated with the one or more KPIs
refers to a network geographical area such circle, blade, instance, cluster, etc. The
10 aggregation levels are defined by users. This determines at what granular level the
user wants to analyse the KPIs. Here, the user may be a system operator, a network
operator, and the like. The schedule is defined as time period to measure the
counters and KPIs. The schedule associated with each counter from the one or more
counters and the schedule associated with each KPI from the one or more KPIs
15 includes but may not be limited to a time interval type and a time interval size.
Moreover, for a single KPI, multiple users can schedule their policies at different
aggregation levels. One can include any number of counters from a network node
in a policy and schedule it at any level. Further, the one or more notification
templates may refer to specific formats in which the user wishes to receive the
20 reports. The user notification group information may comprise an email group to
which the reports need to be delivered. The notification group information is not
limited to emails, but may also comprise phone numbers, IP address, etc. of the
users. Users can choose the email group to which the generated KPI report needs to
be sent.
25
[0069] The one or more severity breach threshold refers to a predefined limit which
defines a value above or below which a breach condition occurs. The values as
defined for each KPI are referred to as severity breach thresholds. For example, for
the success rate KPI, the severity breach thresholds may be defined as follows:
30
33
If Success Rate > 99.5% then “no breach condition”)
If Success Rate >99% and <99.5%, then breach condition is detected with
threshold severity defined as “warning”
If Success Rate <99%, then the breach condition is detected with threshold
5 severity defined as “major”
If Success Rate <80%, then the breach condition is detected with threshold
severity defined as “critical”.
[0070] The user interface unit [316] is further configured to transmit to the IPM
10 [100a], the one or more policies comprising the data. Further, the storage unit [314]
at the IPM [100a] is configured to store the one or more polices in a database. In
one example, the database is the distributed data lake (DDL) [100u] as depicted in
FIG. 1A.
15 [0071] Once, the policies are created and stored at the IPM [100a], the transceiver
unit [302] at the cron scheduler [304] is configured to transmit a request for
execution of one or more policies at a pre-defined interval to the IPM [100a]. The
pre-defined interval is a periodical time period which defines when the policies
should be executed and may be defined by the user. In an implementation, the pre20 defined interval may be 1 hour, where the counter data may be requested by the
user.
[0072] The transceiver unit [306] at the IPM [100a] receives a request for a report.
In one example, the request includes but may not be limited to a set of counters and
25 a set of KPIs for which a policy is to be executed.
[0073] Post receiving the request for the report comprising the set of counters and
the set of KPIs, the execution unit [322] is configured to run at the cron scheduler
[304], a cron for the policy comprising the set of KPIs and the set of counters. The
30 cron refers to a time-based task scheduler. The cron allows the users to schedule
tasks at pre-defined intervals of time. The cron may periodically execute the policy
34
or policies needed to analyse the set of counters and the set of KPIs to generate the
report.
[0074] Based on the received set of counters and the set of KPIs, as requested by
5 the user, the identification unit [308] is configured to identify a set of policies from
the one or more policies which are defined for the received set of counters and the
set of KPIs. The identification refers to selecting a relevant set of policies from the
one or more policies based on the set of counters and the set of KPIs. The policies
can be created and scheduled for each KPI individually for regular observation.
10 Therefore, when the cron scheduler [304] sends a request to execute a policy based
on the counters and KPIs as provided by the user, the IPM [100a] picks the policies
that have been created for the counters and KPIs as requested by the user.
[0075] The evaluation unit [310] at the IPM [100a], is configured to evaluate the
15 set of policies including the set of counters and the set of KPIs based on a set of
severity breach thresholds. The one or more severity breach threshold values
associated with each of the KPI from the one or more KPIs and the one or more
severity breach threshold values associated with each of the counter from the one
or more counters is associated with one or more severities. The breach conditions
20 associated with the set of counters and the set of KPIs is identified in an event a
current value of each of the counter from the set of counters and each of the KPI
from the set of KPIs exceeds/falls below a corresponding severity breach threshold
from the set of severity breach thresholds as defined in the policies. In an
implementation, the one or more severities may be warning, major and critical.
25
[0076] The identification unit [308] is configured to identify at the IPM [100a], a
set of breach conditions associated with the set of counters and the set of KPIs based
on the evaluation on the set of severity breach thresholds. For example, for the
success rate KPI, the severity breach thresholds may be defined as follows:
30
35
If Success Rate > 99.5% then “no breach condition”)
If Success Rate >99% and <99.5%, then breach condition is detected with
threshold severity defined as “warning”
If Success Rate <99%, then the breach condition is detected with threshold
5 severity defined as “major”
If Success Rate <80%, then the breach condition is detected with threshold
severity defined as “critical”.
[0077] The alert unit [324] is configured to trigger one or more alarms based on the
10 identified set of breach conditions. In one example, the one or more alarms may be
one of a warning alarm, a major alarm and a critical alarm. Each of the one or more
alarms is associated with a severity level, indicating the seriousness of the breach.
For example, the warning alarm might be for low severity, the critical alarm may
be for high severity. The values of KPIs and counters falling beyond the thresholds
15 which result in threshold breaches, are highlighted according to the severity. These
severity breaches then can be used for several purposes including but not limited to
notifying the user, raising an alarm.
[0078] Further, the report generation unit [312] is configured to generate at the IPM
20 [100a], one or more reports comprising the set of breach conditions. For generating
the one or more reports by the report generation unit [312], at the IPM [100A], the
severity breach thresholds are fetched from the database.
[0079] Further, the transceiver unit [306] is configured to send from the IPM
25 [100a], the one or more reports to one or more users based on the set of policies.
The one or more reports sent to the one or more users includes but may not be
limited to a delta KPI report. The delta relates to the difference in result between
the previously sent reports and the generated one or more reports. The system [300],
also provides the delta for a user chosen dates, where the system [300] may utilize
30 the stored pre-computed KPI data to perform the real-time calculation and output
delivery. For example, if the user has already received and downloaded a report
36
with details about 2 KPIs, then the IPM [100a], will not send the report comprising
these 2 KPIs, but will only send the difference, that is the report for KPIs which the
user has not received.
5 [0080] In an implementation of the present disclosure, after generation of the
report, the IPM [100a] interacts with a mail server to send the generated report to
the one or more users. The policies created by the user comprise an email group
which should receive the report. The IPM [100a] interacts with the mail server and
sends the generated report to the email group configured in the policy.
10
[0081] Further, the IPM [100a], sends the set of breach conditions identified to a
learning module where the breach conditions are calibrated by the calibration unit
[318] based on the severity breach thresholds. The calibration is based on a set of
factors including but may not be limited to a weather, a holiday and a disaster. As
15 can be understood, these are external factors and therefore may change from time
to time. Based on the changing factors, the calibration unit [318] is further
configured to modify the severity breach thresholds for the set of policies. To
calibrate, the calibration unit [318] may measure the counters and calculate the KPIs
and based on the geographical conditions, time and other factors and accordingly
20 calibrate the threshold values and accordingly modify the severity breach thresholds
for the set of policies. In one example, during day, a success rate KPI threshold
increases, while the success rate KPI threshold may decrease during night. The
storage unit [314] is configured to store the modified severity breach thresholds for
the set of policies in the database.
25
[0082] Referring to FIG. 4, an exemplary method flow diagram [400] for counters
and key performance indicator (KPIs) policy management in a network, in
accordance with exemplary implementations of the present disclosure is shown. In
an implementation the method [400] is performed by the system [300]. Further, in
30 an implementation, the system [300] may be present in a server device to implement
37
the features of the present disclosure. Also, as shown in FIG. 4, the method [400]
starts at step [402].
[0083] At step [404], the method comprises transmitting, by a transceiver unit
5 [302], from a cron scheduler [304], a request for execution of one or more policies
at a pre-defined interval to an integrated performance management (IPM) [100a].
In an implementation, the pre-defined interval may be 1 hour, where the counter
data may be requested by the user.
10 [0084] It is to be noted that prior to transmitting the request for execution of one or
more policies from the cron scheduler [304] to the IPM [100a], the user interface
unit [316] creates the one or more policies. Each of the policy from the one or more
policies is associated with a data. The data associated with each of the policy from
the one or more policies includes but may not be limited to one or more counters,
15 one or more KPIs, one or more aggregation levels associated with each KPI from
the one or more KPIs, a schedule associated with each counter from the one or more
counters, a schedule associated with each KPI from the one or more KPIs, one or
more severity breach threshold values associated with each of the KPI from the one
or more KPIs, one or more severity breach threshold values associated with each of
20 the counter from the one or more counters, one or more notification templates and
a user notification group information. The policies can be created and scheduled for
each KPI individually for regular observation.
[0085] The one or more counters refers to raw metrics which is collected from
25 various network entities to detect a specific event. For example, the number of times
a request fails to be delivered or a number of times a response is not received. The
one or more KPIs are created from the one or more counters. For example, a KPI
can be created to assess a success rate of request delivery, based on the counters
that collect metrics related to number of requests delivered and number of requests
30 failed to be delivered. The aggregation levels associated with the one or more KPIs
refers to a network geographical area such circle, blade, etc. The aggregation levels
38
are defined by users. This determines at what granular level the user wants to
analyse the KPIs. Here, the user may be a system operator, a network operator, and
the like. The schedule is defined as time period to measure the counters and KPIs.
The schedule associated with each counter from the one or more counters and the
5 schedule associated with each KPI from the one or more KPIs includes but may not
be limited to a time interval type and a time interval size. Moreover, for a single
KPI, multiple users can schedule their policies at different aggregation levels. One
can include any number of counters from a network node in a policy and schedule
it at any level. The one or more notification templates may refer to specific formats
10 in which the user wishes to receive the reports. The user notification group
information may comprise an email group to which the reports need to be delivered.
The notification group information is not limited to emails, but may also comprise
phone numbers, IP address, etc. of the users. Users can choose the email group to
which the generated KPI report needs to be sent.
15
[0086] The one or more severity breach threshold refers to a predefined limit which
defines a value above or below which a breach condition occurs. The values as
defined for each KPI are referred to as severity breach thresholds. For example, for
the success rate KPI, the severity breach thresholds may be defined as follows:
20 If Success Rate > 99.5% then “no breach condition”)
If Success Rate >99% and <99.5%, then breach condition is detected with
threshold severity defined as “warning”
If Success Rate <99%, then the breach condition is detected with threshold
severity defined as “major”
25 If Success Rate <80%, then the breach condition is detected with threshold
severity defined as “critical”.
[0087] The storage unit [314] stores the one or more polices in a database. In one
example, the database is the distributed data lake (DDL) [100u] as depicted in FIG.
30 1A.
39
[0088] At step [406], the method comprises receiving, by the transceiver unit [306],
at the IPM [100a], a request for a report comprising a set of counters and a set of
KPIs. Post receiving the request for the report comprising the set of counters and
the set of KPIs, the execution unit [322] is configured to run at the cron scheduler
5 [304], a cron for the set of KPIs and the set of counters. The cron refers to a timebased task scheduler. The cron allows the users to schedule tasks at pre-defined
intervals of time. The cron may periodically execute the tasks needed to gather the
set of counters and the set of KPIs to generate the report.
10 [0089] At step [408], the method comprises identifying, by an identification unit
[308], at the IPM [100a], a set of policies from the one or more policies comprising
the set of counters and the set of KPIs. The identification refers to selecting a
relevant set of policies from the one or more policies based on the set of counters
and the set of KPIs. The policies can be created and scheduled for each KPI
15 individually for regular observation. Therefore, when the cron scheduler [304]
sends a request to execute a policy based on the counters and KPIs as provided by
the user, the IPM [100a] picks the policies that have been created for the counters
and KPIs as requested by the user.
20 [0090] Next at step [410], the method comprises evaluating, by an evaluation unit
[310], at the IPM [100a], the set of policies comprising the set of counters and the
set of KPIs based on a set of severity breach thresholds. The one or more severity
breach threshold values associated with each of the KPI from the one or more KPIs
and the one or more severity breach threshold values associated with each of the
25 counter from the one or more counters is associated with one or more severities.
The set of breach conditions associated with the set of counters and the set of KPIs
is identified in an event a current value of each of the counter from the set of
counters and each of the KPI from the set of KPIs exceeds/falls below a
corresponding severity breach threshold from the set of severity breach thresholds.
30 In an implementation, the one or more severities may be warning, major and
critical.
40
[0091] Next at step [412], the method comprises identifying, by the identification
unit [308], at the IPM [100a], a set of breach conditions associated with the set of
counters and the set of KPIs based on the evaluation on the set of severity breach
5 thresholds. For example, for the success rate KPI, the severity breach thresholds
may be defined as follows:
If Success Rate > 99.5% then “no breach condition”)
If Success Rate >99% and <99.5%, then breach condition is detected with
threshold severity defined as “warning”
10 If Success Rate <99%, then the breach condition is detected with threshold
severity defined as “major”
If Success Rate <80%, then the breach condition is detected with threshold
severity defined as “critical”.
15 [0092] Next at step [414], the method comprises generating, by a report generation
unit [312], at the IPM [100a], one or more reports comprising the set of breach
conditions.
[0093] The alert unit [324] triggers one or more alarms based on the set of breach
20 conditions. In one example, the one or more alarms may be one of a warning alarm,
a major alarm and a critical alarm. Each of the one or more alarms is associated
with a severity level, indicating the seriousness of the breach. For example, the
warning alarm might be for low severity, the critical alarm may be for high severity.
The values of KPIs and counters falling beyond the thresholds which result in
25 threshold breaches, are highlighted according to the severity. These severity
breaches then can be used for several purposes including but not limited to notifying
the user, raising an alarm.
[0094] Further, at step [416], the method comprises sending, by the transceiver unit
30 [306], from the IPM [100a], the one or more reports to one or more users based on
the set of policies. Before sending, the one or more reports may be generated by the
41
report generation unit [312]. The one or more reports sent to the one or more users
includes but may not be limited to a delta KPI report. The delta relates to the
difference in result between the previously sent reports and the generated one or
more reports. The method [400], also provides the delta for a user chosen dates,
5 where the method [400 may utilize the stored pre-computed KPI data to perform
the real-time calculation and output delivery.
[0095] In an implementation of the present disclosure, after generation of the
report, the IPM [100a] interacts with a mail server to send the generated report to
10 the one or more users.
[0096] Further, the IPM [100a], sends the set of breach conditions identified to a
learning module where the breach conditions are calibrated by the calibration unit
[318] based on the severity breach thresholds. The calibration is based on a set of
15 factors including but may not be limited to a weather, a holiday and a disaster. As
can be understood, these are external factors and therefore may change from time
to time. Based on the changing factors, the calibration unit [318] is further
configured to modify the severity breach thresholds for the set of policies. To
calibrate, the calibration unit [318] may measure the counters and calculate the KPIs
20 and based on the geographical conditions, time and other factors and accordingly
calibrate the threshold values and accordingly modify the severity breach thresholds
for the set of policies. In one example, during day, a success rate KPI threshold
increases, while the success rate KPI threshold may decrease during night. The
storage unit [314] is configured to store the modified severity breach thresholds for
25 the set of policies in the database.
[0097] The method [400] thereafter terminates at step [418].
[0098] Referring to FIG.5, an exemplary implementation of the system [500] for
30 counters and key performance indicator (KPIs) policy management in a network,
in accordance with exemplary implementations of the present disclosure is shown.
42
[0099] The implementation system [500] comprises the user interface (UI) unit
[316] at a User Equipment, the load balancer [100k], the integrated performance
management (IPM) [100a], the computational layer [100d], the distributed data lake
5 [100u], the distributed file system [100j], the cron scheduler [304], a mail server
[502] and an artificial intelligence/machine learning layer [504].
[0100] The UI unit [316] may be one of a graphical user interface (GUI), a
command line interface, and the like. The GUI refers to an interface to interact with
10 the system [500] by the user by visual or graphical representation of icons, menu,
etc. The GUI is an interface that may be used within a smartphone, laptop,
computer, etc. The CLI refers to a text-based interface to interact with the system
[500] as by the user. The user may input text lines called as command lines in the
CLI to access the data in the system. The user creates one or more policies related
15 to counters and KPIs at the UI unit [316]. Once the user has finished creating the
policies, the user saves the one or more policies. The request to save the one or
policies is transmitted by the UI unit [316] to the load balancer [100k] to distribute
the one or more policies to one or more instances of the IPM [100a].
20 [0101] The load balancer (LB) [100k] is a component of the IPM architecture
[100A] to efficiently distribute incoming network traffic or requests. The load
balancer [100k] ensures even distribution of requests, leading to optimized server
resource utilization, reduced latency, and improved overall system performance.
The LB [100k] implements various routing strategies to manage traffic. The LB
25 [100k] includes round-robin scheduling, header-based request dispatch, and
context-based request dispatch as defined in FIG. 1A.
[0102] The request to save the one or more policies is further transmitted by the
load balancer [100k] to the IPM [100a]. The IPM [100a] is configured to collect,
30 process, and manage performance counter data from data sources within the
network. The counter data includes metrics such as connection speed, latency, data
43
transfer rates, and many others. The IPM [100a] is further configured to collect the
one or more policies and send it for storage to in the Distributed Data Lake [100u].
The Distributed data lake [100u] is the centralized, scalable, and flexible storage
solution, allowing for easy access and further analysis.
5
[0103] Further, the user can also request for one or more reports for a set of counters
and KPIs which the user wants to analyse and observe. The user sends the request
from the UI unit [316] to the IPM [100a] via the load balancer [100k]. Once the
IPM [100a] receives the request for report generation, the IPM [100a] identifies a
10 set of policies comprising the requested set of counters and KPIs. The IPM [100a]
then evaluates the set of policies based on a set of severity breach thresholds. The
severity breach thresholds are defined in the set of policies for counters and KPIs.
Based on the evaluation, the IPM [100a] identifies a set of breach conditions for the
requested set of counters and KPIs. Further, based on the identifies set of breach
15 conditions, the IPM [100a] generates one or more reports for the user comprising
the set of breach conditions which are calibrated based on the severity breach
thresholds. The breach thresholds are highlighted based on the severities identifies
by the breach conditions. For example, for the success rate KPI, the severity breach
thresholds may be defined as follows:
20 If Success Rate > 99.5% then “no breach condition”)
If Success Rate >99% and <99.5%, then breach condition is detected with
threshold severity defined as “warning”
If Success Rate <99%, then the breach condition is detected with threshold
severity defined as “major”
25 If Success Rate <80%, then the breach condition is detected with threshold
severity defined as “critical”.
[0104] In an implementation, if the identified breach condition falls in the severity
defined as “critical”, then the report will highlight this in dark red color. Similarly,
30 if the identified breach condition falls in the severity defined as “major”, then the
report will highlight this in red color. And, if the identified breach condition falls
44
in the severity defined as “warning”, then the report will highlight this in orange
color.
[0105] After the one or reports are generated by the IPM [100a], the IPM [100a]
5 identifies the mail server [502] for sending the reports to the user. The mail server
[502] is a system responsible for sending, receiving, and storing emails. The mail
server [502] ensures that emails are correctly routed to the users for the one or more
policies.
10 [0106] Further, the IPM [100a], sends the set of breach conditions identified to a
learning module where the breach conditions are calibrated by the calibration unit
[318] based on the severity breach thresholds. The calibration is based on a set of
factors including but may not be limited to a weather, a holiday and a disaster. As
can be understood, these are external factors and therefore may change from time
15 to time. Based on the changing factors, the calibration unit [318] is further
configured to modify the severity breach thresholds for the set of policies. To
calibrate, the calibration unit [318] may measure the counters and calculate the KPIs
and based on the geographical conditions, time and other factors and accordingly
calibrate the threshold values and accordingly modify the severity breach thresholds
20 for the set of policies. In one example, during day, a success rate KPI threshold
increases, while the success rate KPI threshold may decrease during night. The
storage unit [314] is configured to store the modified severity breach thresholds for
the set of policies in the database.
25 [0107] Further, the learning module is an Artificial Intelligence (AI)/Machine
Learning (ML) layer [504] which calibrates the severity breach threshold for the
one or more identified policies based on geographical conditions, time and other
factors.
30 [0108] Once, the IPM [100a] receives the request for one or more reports
comprising the set of counters and KPIs from the user, the cron scheduler [304]
45
runs a cron for the set of counters and KPIs as requested by the user. The cron refers
to a time-based task scheduler. The cron scheduler [304] allows the users to
schedule tasks at pre-defined intervals of time. The cron may periodically execute
the tasks needed to gather the values for the set of counters and KPIs to generate
5 the report. The cron information and its state of execution, like in progress or
terminated, is stored in the DDL [100u].
[0109] The Computation Layer [100d] serves as the main hub for complex data
processing tasks. In essence, the Computation Layer [100d] is where all major
10 computation and data processing tasks occur.
[0110] The Distributed File System (DFS) [100j] is a critical component of the
Integrated Performance Management System [100a] that enables multiple clients to
access and interact with data seamlessly. The Distributed File system [100j] is
15 designed to manage data files that are partitioned into numerous segments known
as chunks.
[0111] Referring to FIG. 6, an exemplary implementation of a signal flow diagram
[600] for creating policies, in accordance with exemplary implementations of the
20 present disclosure is shown.
[0112] At Step 1, a user [602] creates the one or more policies at the UI unit [316].
In one example, after creation of the one or more policies, the user [602] may select
to save the one or more policies.
25
[0113] At Step 2, the UI unit [316] sends a request via the load balancer [100k] to
save the one or more policies at the IPM [100a].
[0114] At Step 3, the load balancer [100k] sends the one or more policies to the
30 IPM [100a] for saving.
46
[0115] Further at Step 4, the IPM [100a] saves the data of the one or more policies
at the distributed data lake [100u].
[0116] Further at Step 5, the IPM [100a] forwards the request to the cron scheduler
5 [304] for running a cron for the one or more policies.
[0117] At Step 6, the state of the cron scheduler [304] is stored at the distributed
data lake [100u].
10 [0118] At Step 7, the cron scheduler [304] sends an acknowledgment for starting
the cron scheduling for the one or more policies to the IPM [100a].
[0119] Next, at Step 8, the IPM [100a] sends a confirmation of the scheduling of
the one or more policies to the UI unit [316].
15
[0120] At step 9, the UI unit [316] displays an update of the one or more polices
being saved successfully, to the user.
[0121] Referring to FIG. 7, an exemplary implementation of a signal flow diagram
20 [700] for counters and KPIs policy management, in accordance with exemplary
implementations of the present disclosure is shown.
[0122] As described with respect to FIG. 6, the user creates one or more policies
and saves them at the DDL [100u]. Later, the user sends a request for one or more
25 reports comprising a set of counters and KPIs which the user wants to analyse and
observe. After the report request from the user is received, a request for execution
of one or more policies at a pre-defined interval is received at the IPM [100a] from
the cron scheduler [304]. As shown in FIG. 7, at Step 1, the cron scheduler [304]
sends the request for execution of one or more policies comprising the requested
30 set of counters and KPIs to the IPM [100a].
47
[0123] At Step 2, the request is transmitted to the computation layer [100d] for
processing if the request is received before the retention period expires for the
computation layer [100d]. This means that the data related to the one or more
policies to be executed is active and is present in the cache, then the data is collected
5 from the computation layer [100d]. Furthermore, at Step 3, the IPM [100a] is
configured to receive an acknowledgement from the computational layer [100d].
The retention period refers to the maximum duration of time for which the
computation layer [100u] stores the data in its cache. In one example, the retention
period may be defined as 10 days. Therefore, the present disclosure utilizes the
10 stored pre-computed KPI data to perform the real-time calculation and output
delivery.
[0124] At Step 4, the computation layer [100d] sends a request to the distributed
file system [100j] to access the stored data to execute the one or more policies.
15
[0125] In response to the request, at Step 5, the distributed file system [100j] sends
the data based on the request.
[0126] At Step 6, the computation layer [100d] performs computations on the data.
20 The computation may identify a set of breach conditions based on the defined set
of severity breach thresholds in the one or more policies. The severity breach
thresholds are defined for the set of counters and the KPIs.
[0127] At Step 7, the computation layer [100d] sends back the KPI data based on
25 the computations to the IPM [100a].
[0128] Further, if the request to execute one or more policies is received after the
retention period expires for the computation layer [100d], then at Step 8, the IPM
[100a] queries the distributed data lake [100u] to fetch the required counter data.
30
48
[0129] Further, at Step 9, the distributed data lake [100u] sends the counter data
based on the query to the IPM [100a].
[0130] Next, at Step 10, based on the received KPI data and the fetched counter
5 data, the IPM [100a] computes the final data to generate the report. This
computation is to identify a set of breach conditions based on the defined set of
severity breach thresholds in the one or more policies. The severity breach
thresholds are defined for the set of counters and the KPIs. The final data computed
is in the form of a report which the user can use to analyse the KPIs. The set of
10 breach conditions associated with the counters and the KPIs are identified in an
event a current value of each of the counter from the set of counters and each of the
KPI from the set of KPIs exceeds a corresponding severity breach threshold from
the set of severity breach thresholds.
15 [0131] At Step 11, the IPM [100a] establishes a connection with the mail server
[502] to send the report to the user [602].
[0132] At Step 12, the IPM [100a] sends a request to calibrate the set of breach
conditions to the AI/ML [504]. The breach conditions are calibrated by the AI/ML
20 [504] based on the severity breach thresholds. The calibration is based on a set of
factors including but may not be limited to a weather, a holiday and a disaster. As
can be understood, these are external factors and therefore may change from time
to time. Based on the changing factors, the AI/ML [504] is further configured to
modify the severity breach thresholds for the set of policies.
25
[0133] At Step 13, the AI/ML [504] sends a request to the DDL [100u], to save the
modified severity breach thresholds in the one or more policies.
[0134] At Step 14, the mail server [502] sends a notification via mail to all users
30 based on the email group information mentioned in the one or more policies.
49
[0135] Referring to FIG. 8, an exemplary implementation of a signal flow diagram
[800] for showing a highlighted result to the user based on user request, in
accordance with exemplary implementations of the present disclosure is shown.
5 [0136] At Step 1, the user [602] sends a request to the UI unit [316] to show the
generated report or the result.
[0137] Further at Step 2, the UI unit [316] sends the request to the load balancer
[100k] to fetch the generated report.
10
[0138] Further at Step 3, the load balancer [100k] forwards the request to the IPM
[100a] to fetch the report.
[0139] At Step 4, the IPM [100a] fetches the severity threshold based on the
15 request, from the distributed data lake [100u].
[0140] Further at Step 5, the IPM [100a] sends the delta KPI data to the load
balancer [100k]. The present disclosure also provides the delta for a user chosen
dates via step 1, which utilizes the stored pre-computed KPI data to perform the
20 real-time calculation and output delivery.
[0141] Further, at Step 6, the load balancer [100k] forwards the data to the UI unit
[316].
25 [0142] At Step 7, the UI unit [316] displays a highlighted report comprising the
delta KPI data to the user [602].
[0143] For example, a KPI can be created to assess a success rate of request
delivery, based on the counters that collect metrics related to number of requests
30 delivered and number of requests failed to be delivered. For the success rate KPI,
the severity breach thresholds may be defined as follows:
50
If Success Rate > 99.5% then “no breach condition”)
If Success Rate >99% and <99.5%, then breach condition is detected with
threshold severity defined as “warning”
If Success Rate <99%, then the breach condition is detected with threshold
5 severity defined as “major”
If Success Rate <80%, then the breach condition is detected with threshold
severity defined as “critical”.
[0144] In an implementation, if the identified breach condition falls in the severity
10 defined as “critical”, then the report will highlight this in dark red color. Similarly,
if the identified breach condition falls in the severity defined as “major”, then the
report will highlight this in red color. And, if the identified breach condition falls
in the severity defined as “warning”, then the report will highlight this in orange
color.
15
[0145] The present disclosure further discloses a User Equipment (UE). The UE
comprises a user interface unit [316]. The user interface unit [316] is configured to
create, one or more policies comprising a set of counters and a set of KPIs. The UE
comprises a transceiver unit to send a request to a load balancer to save the one or
20 more policies. The transceiver unit is further configured to send a request, for
fetching a result for the set of counters and the set of KPIs. The transceiver unit is
further configured to receive, a report comprising the result for the set of counters
and the set of KPIs. The result comprises one or more highlights for one or more
breach conditions. The result is generated by a system [300] comprising a
25 transceiver unit [302], configured to transmit, from a cron scheduler [304], a request
for execution of the one or more policies at a pre-defined interval to an integrated
performance management (IPM) [100a]. The transceiver unit [306], configured to
receive, at the IPM [100a], a request for the report comprising the set of counters
and the set of KPIs. The system [300] comprises an identification unit [308],
30 configured to identify at the IPM [100a], a set of policies from the one or more
policies comprising the set of counters and the set of KPIs. The system [300]
51
comprises an evaluation unit [310], configured to evaluate at the IPM [100a], the
set of policies comprising the set of counters and the set of KPIs based on a set of
severity breach thresholds. The system [300] further comprises the identification
unit [308], configured to identify at the IPM [100a], a set of breach conditions
5 associated with the set of counters and the set of KPIs based on the evaluation on a
set of severity breach thresholds. The system [300] further comprises a report
generation unit [312], configured to generate at the IPM [100a], one or more reports
comprising the set of breach conditions, wherein the breach conditions are
calibrated based on the severity breach thresholds. The transceiver unit [306], is
10 configured to send from the IPM [100a], the one or more reports to the user
interface unit [316] of the UE based on the set of policies.
[0146] The present disclosure further discloses a non-transitory computer readable
storage medium storing instructions for counters and key performance indicator
15 (KPIs) policy management in a network, the instructions include executable code
which, when executed by one or more units of a system, cause a transceiver unit
[302] to transmit, from a cron scheduler [304], a request for execution of one or
more policies at a pre-defined interval to an integrated performance management
(IPM) [100a]. The instructions when executed by the system further cause the
20 transceiver unit to [306] receive, at the IPM [100a], a request for a report
comprising a set of counters and a set of KPIs. The instructions when executed by
the system further cause an identification unit [308] to identify at the IPM [100a],
a set of policies from the one or more policies comprising the set of counters and
the set of KPIs. The instructions when executed by the system further cause an
25 evaluation unit [310] to evaluate at the IPM [100a], the set of policies comprising
the set of counters and the set of KPIs based on a set of severity breach thresholds.
The instructions when executed by the system further cause the identification unit
[308] to identify at the IPM [100a], a set of breach conditions associated with the
set of counters and the set of KPIs based on the evaluation on a set of severity
30 breach thresholds. The instructions when executed by the system further cause a
report generation unit [312] to generate at the IPM [100a], one or more reports
52
comprising the set of breach conditions, wherein the breach conditions are
calibrated based on the severity breach thresholds. The instructions when executed
by the system further cause the transceiver unit [306] to send from the IPM [100a],
the one or more reports to one or more users based on the set of policies.
5
[0147] As is evident from the above, the present disclosure provides a technically
advanced solution for counters and key performance indicator (KPIs) policy
management in a network. The present solution provides a system and a method for
providing counter and policy management for creating and scheduling the policies
10 for each KPI individually for regular observation. The present disclosure reduces
the grunt work and automate the tasks which need to be performed after having
observed any kind of breaches. The present disclosure further provides a solution
through which one single policy for a Counter or KPI gets applied across many of
the IPM modules like in live monitoring, report generation without extra efforts.
15 The present disclosure devises a tool to calibrate the thresholds of the policies
according to the weather, holiday, and disasters to overcome the unforeseen turn of
events.
[0148] While considerable emphasis has been placed herein on the disclosed
20 implementations, it will be appreciated that many implementations can be made
and that many changes can be made to the implementations without departing from
the principles of the present disclosure. These and other changes in the
implementations of the present disclosure will be apparent to those skilled in the
art, whereby it is to be understood that the foregoing descriptive matter to be
25 implemented is illustrative and non-limiting.
[0149] Further, in accordance with the present disclosure, it is to be acknowledged
that the functionality described for the various components/units can be
implemented interchangeably. While specific embodiments may disclose a
30 particular functionality of these units for clarity, it is recognized that various
configurations and combinations thereof are within the scope of the disclosure. The
53
functionality of specific units as disclosed in the disclosure should not be construed
as limiting the scope of the present disclosure. Consequently, alternative
arrangements and substitutions of units, provided they achieve the intended
functionality described herein, are considered to be encompassed within the scope
5 of the present disclosure
54
We Claim:
1. A method [400] for counters and key performance indicator (KPIs) policy
management in a network, the method comprises:
5 - transmitting, by a transceiver unit [302], from a cron scheduler [304]
, a request for execution of one or more policies at a pre-defined
interval to an integrated performance management (IPM) [100a];
- receiving, by the transceiver unit [306], at the IPM [100a], a request
for a report comprising a set of counters and a set of KPIs;
10 - identifying, by an identification unit [308], at the IPM [100a], a set
of policies from the one or more policies comprising the set of
counters and the set of KPIs;
- evaluating, by an evaluation unit [310], at the IPM [100a], the set of
policies comprising the set of counters and the set of KPIs based on
15 a set of severity breach thresholds;
- identifying, by the identification unit [308], at the IPM [100a], a set
of breach conditions associated with the set of counters and the set
of KPIs based on the evaluation on the set of severity breach
thresholds;
20 - generating, by a report generation unit [312], at the IPM [100a], one
or more reports comprising the set of breach conditions, wherein the
breach conditions are calibrated based on the severity breach
thresholds; and
- sending, by the transceiver unit [306], from the IPM [100a], the one
25 or more reports to one or more users based on the set of policies.
2. The method [400] as claimed in the claim 1, wherein prior to transmitting
the request for execution of one or more policies from the cron scheduler
[304] to the IPM [100a], the method comprises:
55
- creating, at a user interface unit [316], the one or more policies,
wherein each of the policy from the one or more policies is
associated with a data;
- transmitting, by the user interface unit [316] to the IPM [100a], the
5 one or more policies comprising the data;
- storing, by a storage unit [314], at the IPM [100a], the one or more
polices in a database; and
- forwarding, by the transceiver unit [306], from the IPM [100a] to
the cron scheduler [304], a request to schedule the one or more
10 policies based on the data.
3. The method [400] as claimed in claim 2, wherein the data associated with
each of the policy from the one or more policies comprises one or more
counters, one or more KPIs, one or more aggregation levels associated with
15 each KPI from the one or more KPIs, a schedule associated with each
counter from the one or more counters, a schedule associated with each KPI
from the one or more KPIs, one or more severity breach threshold values
associated with each of the KPI from the one or more KPIs, one or more
severity breach threshold values associated with each of the counter from
20 the one or more counters, one or more notification templates and a user
notification group information .
4. The method [400] as claimed in claim 3, wherein the schedule associated
with each counter from the one or more counters and the schedule associated
25 with each KPI from the one or more KPIs comprises a time interval type
and a time interval size.
5. The method [400] as claimed in claim 3, wherein the one or more severity
breach threshold values associated with each of the KPI from the one or
30 more KPIs and the one or more severity breach threshold values associated
56
with each of the counter from the one or more counters is associated with
one or more severities.
6. The method [400] as claimed in claim 1 wherein the set of breach conditions
5 associated with the set of counters and the set of KPIs is identified in an
event a current value of each of the counter from the set of counters and
each of the KPI from the set of KPIs exceeds a corresponding severity
breach threshold from the set of severity breach thresholds.
10 7. The method [400] as claimed in claim 1, further comprises:
- sending, by the IPM [100a], the set of breach conditions to a learning
module [320];
- calibrating, by a calibration unit [318], at the learning module [320],
the severity breach thresholds associated with the set of breach
15 conditions, wherein the calibration is based on a set of factors
comprising at least one of a weather, a holiday and a disaster;
- modifying, by the calibration unit [318], the severity breach thresholds
for the set of policies; and
- storing, by the storage unit [314], by the learning module [320], the
20 modified severity breach thresholds for the set of policies in the
database.
8. The method [400] as claimed in claim1, wherein post receiving, by the
transceiver unit, at the IPM [100a], the request for the report comprising the
25 set of counters and the set of KPIs, the method comprises:
- running, by an execution unit [322], at the cron scheduler [304], a cron
for the set of KPIs and the set of counters.
9. The method [400] as claimed in claim 2, wherein for generating the one or
30 more reports by the report generation unit [312], at the IPM [100a], the
severity breach thresholds are fetched from the database.
57
10. The method [400] as claimed in claim 1, wherein the method further
comprises:
triggering, by an alert unit [324], one or more alarms based on the set of
5 breach conditions.
11. The method [400] as claimed in claim 1, wherein the one or more reports
sent to the one or more users comprises a delta KPI report, wherein the delta
relates to the difference in result between the previously sent reports and the
10 generated one or more reports.
12. A system [300] for counters and key performance indicator (KPIs) policy
management in a network, the system comprises:
- a transceiver unit [302], configured to transmit, from a cron
15 scheduler [304], a request for execution of one or more policies at a
pre-defined interval to an integrated performance management
(IPM) [100a];
- the transceiver unit [306], configured to receive, at the IPM [100a],
a request for a report comprising a set of counters and a set of KPIs;
20 - an identification unit [308], configured to identify at the IPM [100a],
a set of policies from the one or more policies comprising the set of
counters and the set of KPIs;
- an evaluation unit [310], configured to evaluate at the IPM [100a],
the set of policies comprising the set of counters and the set of KPIs
25 based on a set of severity breach thresholds;
- the identification unit [308], configured to identify at the IPM
[100a], a set of breach conditions associated with the set of counters
and the set of KPIs based on the evaluation on a set of severity
breach thresholds;
30 - a report generation unit [312], configured to generate at the IPM
[100a], one or more reports comprising the set of breach conditions,
58
wherein the breach conditions are calibrated based on the severity
breach thresholds; and
- the transceiver unit [306], configured to send from the IPM [100a],
the one or more reports to one or more users based on the set of
5 policies.
13. The system [300] as claimed in the claim 12, wherein prior to transmitting
the request for execution of one or more policies from the cron scheduler
[304] to the IPM [100a], the system comprises:
10 - a user interface unit [316], configured to create the one or more
policies, wherein each of the policy from the one or more policies is
associated with a data;
- the user interface unit [316] configured to transmit to the IPM
[100a], the one or more policies comprising a data;
15 - a storage unit [314], configured to store at the IPM [100a], the one
or more polices in a database; and
- the transceiver unit [306], configured to forward from the IPM
[100a] to the cron scheduler [304], a request to schedule the one or
more policies based on the data.
20
14. The system [300] as claimed in claim 13, wherein the data associated with
each of the policy from the one or more policies comprises one or more
counters, one or more KPIs, one or more aggregation levels associated with
each KPI from the one or more KPIs, a schedule associated with each
25 counter from the one or more counters, a schedule associated with each KPI
from the one or more KPIs, one or more severity breach threshold values
associated with each of the KPI from the one or more KPIs, one or more
severity breach threshold values associated with each of the counter from
the one or more counters and an email group to receive a KPI report.
30
59
15. The system [300] as claimed in claim 14, wherein the schedule associated
with each counter from the one or more counters and the schedule associated
with each KPI from the one or more KPIs comprises a time interval type
and a time interval size.
5
16. The system [300] as claimed in claim 14, wherein the one or more severity
breach threshold values associated with each of the KPI from the one or
more KPIs and the one or more severity breach threshold values associated
with each of the counter from the one or more counters is associated with
10 one or more severities.
17. The system [300] as claimed in claim 12 wherein the set of breach
conditions associated with the set of counters and the set of KPIs is
identified in an event a current value of each of the counter from the set of
15 counters and each of the KPI from the set of KPIs exceeds a corresponding
severity breach threshold from the set of severity breach thresholds.
18. The system [300] as claimed in claim 12, further comprises:
- sending, by the IPM [100a], the set of breach conditions to a learning
20 module [320];
- calibrating, by a calibration unit [318], at the learning module [320],
the severity breach thresholds associated with the set of breach
conditions, wherein the calibration is based on a set of factors
comprising at least one of a weather, a holiday and a disaster;
25 - modifying, by the calibration unit [318], the severity breach thresholds
for the set of policies;
- storing, by the storage unit [314], by the learning module [320], the
modified severity breach thresholds for the set of policies in the
database.
30
60
19. The system [300] as claimed in claim 12, wherein post receiving, by the
transceiver unit, at the IPM [100a], the request for the report comprising the
set of counters and the set of KPIs, the method comprises:
- running, by an execution unit [322], at the cron scheduler [304], a cron
5 for the set of KPIs and the set of counters.
20. The system [300] as claimed in claim 13, wherein for generating the one or
more reports by the report generation unit [312], at the IPM [100a], the
severity breach thresholds are fetched from the database.
10
21. The system [300] as claimed in claim 12, wherein the system further
comprises:
an alert unit [324], configured to trigger one or more alarms based on the
set of breach conditions.
15
22. The system [300] as claimed in claim 12, wherein the one or more reports
sent to the one or more users comprises a delta KPI report, wherein the delta
relates to the difference in result between the previously sent reports and the
generated one or more reports.
20
23. A User Equipment (UE) comprising:
a user interface unit [316], configured to:
create, one or more policies comprising a set of counters and a set
of KPIs;
25 a transceiver unit, configured to:
send a request to a load balancer to save the one or more policies;
send a request, for fetching a result for the set of counters and the set of
KPIs;
receive, a report comprising the result for the set of counters and the set of
30 KPIs, wherein the result comprises one or more highlights for one or more
breach conditions and is generated by a system [300] comprising:
61
- a transceiver unit, configured to transmit, from a cron scheduler
[304] , a request for execution of the one or more policies at a predefined interval to an integrated performance management (IPM)
[100a];
5 - the transceiver unit, configured to receive, at the IPM [100a], a
request for the report comprising the set of counters and the set of
KPIs;
- an identification unit [308], configured to identify at the IPM [100a],
a set of policies from the one or more policies comprising the set of
10 counters and the set of KPIs;
- an evaluation unit [310], configured to evaluate at the IPM [100a],
the set of policies comprising the set of counters and the set of KPIs
based on a set of severity breach thresholds;
- the identification unit [308], configured to identify at the IPM
15 [100a], a set of breach conditions associated with the set of counters
and the set of KPIs based on the evaluation on a set of severity
breach thresholds;
- a report generation unit [312], configured to generate at the IPM
[100a], one or more reports comprising the set of breach conditions,
20 wherein the breach conditions are calibrated based on the severity
breach thresholds; and
- the transceiver unit [306], configured to send from the IPM [100a], the one or more reports to the user interface unit [316] of the UE based on the set of policies.