FORM 2
THE PATENTS ACT, 1970 (39 OF 1970) & THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
“METHOD AND SYSTEM FOR LOCALLY GENERATING AND STORING KEY PERFORMANCE INDICATORS”
We, Jio Platforms Limited, an Indian National, of Office - 101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India.
The following specification particularly describes the invention and the manner in which it is to be performed.

METHOD AND SYSTEM FOR LOCALLY GENERATING AND STORING KEY PERFORMANCE INDICATORS
FIELD OF DISCLOSURE
[0001] Embodiments of the present disclosure generally relate to network
performance management systems. More particularly, embodiments of the present disclosure relate to methods and systems for locally generating and storing key performance indicators (KPIs).
BACKGROUND OF THE DISCLOSURE
[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, and not as admissions of the prior art.
[0003] Wireless communication technology has rapidly evolved over the past
few decades, with each generation bringing significant improvements and advancements. The first generation of wireless communication technology was based on analog technology and offered only voice services. However, with the advent of the second-generation (2G) technology, digital communication and data services became possible, and text messaging was introduced. 3G technology marked the introduction of high-speed internet access, mobile video calling, and location-based services. The fourth-generation (4G) technology revolutionized wireless communication with faster data speeds, better network coverage, and improved security. Currently, the fifth-generation (5G) technology is being deployed, promising even faster data speeds, low latency, and the ability to connect multiple devices simultaneously. With each generation, wireless communication

technology has become more advanced, sophisticated, and capable of delivering more services to its users.
[0004] Moreover, one of the primary challenges in managing 5G networks is
tracing the health of network elements, particularly in an event of malfunctioning of network elements and finding exact issue related to malfunctioning of the network element. These network elements may be the nodes implementing Access and Mobility Management Function (AMF) or Session Management Function (SMF), etc. Further, monitoring health data of the network clusters requires a thorough monitoring of each network element in a network cluster, which in real-time is cumbersome and may lead to delays in data collection and analysis. Further, the 5G network elements generate an extremely large amount of data relating to network performance and health, thereby, leading to huge overhead caused by even sophisticated analytics systems designed to handle large data sets.
[0005] The NFs are usually multi-container, multi-process entities and generate
performance counters for different types of procedures, which are then transmitted to a Northbound Monitoring System (NMS) node. The NMS analyses the received counters to generate Key Performance Indicators (KPIs) for the NF, which are used to monitor health of the NF.
[0006] However, a major issue may arise when a specific process within a
container or an entire container malfunctions. Herein the container is referred to as a virtualized element that hosts a part of NF or an entire NF. Further, in such case, the cluster-level KPI may show a degradation, but finding out the source of issue within the NF cluster is difficult. Thus, finding the actual cause of degradation may consume large amount of time in message flow with the external NMS for receiving the KPIs related to the malfunctioning node. As, in general, NF sends individual counters to NMS and NMS node creates KPI for NF but in few cases KPI’s are required be available with NF as a requirement. If those KPIs are not available at

NF, then there is a need to retrieve the same from external node like NMS for required purpose which needs extra message flow to retrieve those KPI from NMS.
[0007] Thus, there exists an imperative need in the art to enhancing network
capability by generating KPIs at node level and/or cluster level, and reducing message flow in the network for retrieving the KPIs, which the present disclosure aims to address.
OBJECTS OF THE DISCLOSURE
[0008] Some of the objects of the present disclosure, which at least one
embodiment disclosed herein satisfies are listed herein below.
[0009] It is an object of the present disclosure to provide a system and a method
for locally generating and storing key performance indicators (KPIs) for reducing message flow in the network.
[0010] It is another object of the present disclosure to provide a solution that
reduces message flow in the network for retrieving KPI data.
[0011] It is yet another object of the present disclosure to provide a solution to
precisely pinpoint the location of faulty node with exact problem with the node.
SUMMARY OF THE DISCLOSURE
[0012] This section is provided to introduce certain aspects of the present
disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.

[0013] An aspect of the present disclosure may relate to a method for locally
generating and storing key performance indicators (KPIs). The method comprises receiving, by a transceiver unit at a Key Performance Indicator (KPI) manager, a set of performance counters related to one or more processes at one or more network nodes in a network function (NF) cluster. Further, the method comprises generating locally, by a computation unit at the KPI manager, a set of KPIs comprising one or more KPIs, based on the set of performance counters. Thereafter, the method comprises storing, by a storage unit at the KPI manager, the one or more KPIs at a cluster level and a node level.
[0014] In an exemplary aspect of the present disclosure, the method further
comprises transmitting, by the transceiver unit at the KPI manager, the one or more KPIs to a north bound monitoring system (NMS).
[0015] In an exemplary aspect of the present disclosure, the one or more KPIs
are made available to a target network node, in an event the at least one of the one or more network nodes in the NF cluster are malfunctioning.
[0016] In an exemplary aspect of the present disclosure, the method further
comprising assessing, by an assessing unit, a KPI degradation issue in the event the at least one of the one or more network nodes in the NF cluster are malfunctioning.
[0017] Another aspect of the present disclosure may relate to a system for locally
generating and storing KPIs. The system comprising a Key Performance Indicator (KPI) manager, the KPI manager further comprising a transceiver unit configured to receive a set of performance counters related to one or more processes at one or more network nodes in a network function (NF) cluster. Further, the KPI manager comprises a computation unit configure to generate locally a set of KPIs comprising one or more KPIs, based on the set of performance counters. In addition, the KPI manager comprises a storage unit configured to store the one or more KPIs at a cluster level and a node level.

[0018] Yet another aspect of the present disclosure may relate to a non-transitory
computer readable storage medium storing instructions for locally generating and storing KPIs, the instructions include executable code which, when executed by one or more units of a system, causes: a transceiver unit to receive a set of performance counters related to one or more processes at one or more network nodes in a network function (NF) cluster. Further, the executable code executed by one or more units of a system causes a computation unit to generate locally a set of KPIs comprising one or more KPIs, based on the set of performance counters. Furthermore, the executable code executed by one or more units of a system causes a storage unit to store the one or more KPIs at a cluster level and a node level.
DESCRIPTION OF THE DRAWINGS
[0019] 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 different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present 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 drawings includes disclosure of electrical components or circuitry commonly used to implement such components.
[0020] FIG.1 illustrates an exemplary block diagram representation of 5th
generation core (5GC) network architecture.

[0021] 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.
[0022] FIG. 3 illustrates an exemplary block diagram of a system for locally
generating and storing key performance indicators (KPIs), in accordance with exemplary implementations of the present disclosure.
[0023] FIG. 4 illustrates a method flow diagram for locally generating and
storing key performance indicators (KPIs) in accordance with exemplary implementations of the present disclosure.
[0024] The foregoing shall be more apparent from the following more detailed
description of the disclosure.
DETAILED DESCRIPTION
[0025] In the following description, for the purposes of explanation, various
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 address any of the problems discussed above or might address only some of the problems discussed above.
[0026] The ensuing description provides exemplary embodiments only, and is
not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. 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.
[0027] Specific details are given in the following description to provide a
5 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 embodiments in unnecessary detail.
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[0028] 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 a sequential process, many of the operations may be performed in
15 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.
[0029] The word “exemplary” and/or “demonstrative” is used herein to mean
20 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
designs, nor is it meant to preclude equivalent exemplary structures and techniques
25 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
any additional or other elements.
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[0030] As used herein, a “processing unit” or “processor” or “operating
processor” or “computation unit” 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
5 processor, a digital signal processor, a plurality 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,
10 and/or any other functionality that enables the working of the system according to
the present disclosure. More specifically, the processor or processing unit is a hardware processor.
[0031] As used herein, “a user equipment”, “a user device”, “a smart-user-
15 device”, “a smart-device”, “an electronic device”, “a mobile device”, “a handheld
device”, “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
20 phone, laptop, a general-purpose computer, desktop, personal digital assistant,
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 unit(s) which are required to implement the features of the present disclosure. 25
[0032] 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
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that may be required by one or more units of the system to perform their respective functions.
[0033] All modules, units, components used herein, unless explicitly excluded
5 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
microprocessors in association with a DSP core, a controller, a microcontroller,
Application Specific Integrated Circuits (ASIC), Field Programmable Gate Array
10 circuits (FPGA), any other type of integrated circuits, etc.
[0034] As used herein the transceiver unit include at least one receiver and at
least one transmitter configured respectively for receiving and transmitting data,
signals, information or a combination thereof between units/components within the
15 system and/or connected with the system.
[0035] As discussed in the background section, the current known solutions have
several shortcomings. The present disclosure aims to overcome the above-mentioned and other existing problems in this field of technology by providing
20 method and system of locally generating and storing key performance indicators
(KPIs). The present disclosure generates KPIs on cluster level as well as container/node level which helps to pinpoint exact issue for KPI degradation in case of error scenario due to a faulty container/node. Problem in NF is traceable at node/container while node is serving live traffic. In case one of node is
25 malfunctioning, the KPI from that node gets hampered and it may be quickly
concluded. The present disclosure generates a set of KPI locally based on counters received from all processes of cluster and make sure it’s available at NF so that it is available wherever required as per use case of peer NFs, without extra message flow to retrieve these KPI from external node i.e., NMS node.
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[0036] FIG. 1 illustrates an exemplary block diagram representation of 5th
generation core (5GC) network architecture, in accordance with exemplary
implementation of the present disclosure. As shown in figure 1, the 5GC network
architecture [100] includes a user equipment (UE) [102], a radio access network
5 (RAN) [104], an access and mobility management function (AMF) [106], a Session
Management Function (SMF) [108], a Service Communication Proxy (SCP) [110], an Authentication Server Function (AUSF) [112], a Network Slice Specific Authentication and Authorization Function (NSSAAF) [114], a Network Slice Selection Function (NSSF) [116], a Network Exposure Function (NEF) [118], a
10 Network Repository Function (NRF) [120], a Policy Control Function (PCF) [122],
a Unified Data Management (UDM) [124], an application function (AF) [126], a User Plane Function (UPF) [128], a data network (DN) [130], wherein 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.
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[0037] Radio Access Network (RAN) [104] is the part of a mobile
telecommunications system that connects user equipment (UE) [102] to the core network (CN) and provides access to different types of networks (e.g., 5G network). It consists of radio base stations and the radio access technologies that enable
20 wireless communication.
[0038] Access and Mobility Management Function (AMF) [106] is a 5G core
network function responsible for managing access and mobility aspects, such as UE
registration, connection, and reachability. It also handles mobility management
25 procedures like handovers and paging.
[0039] Session Management Function (SMF) [108] is a 5G core network
function responsible for managing session-related aspects, such as establishing,
modifying, and releasing sessions. It coordinates with the User Plane Function
30 (UPF) for data forwarding and handles IP address allocation and QoS enforcement.
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[0040] Service Communication Proxy (SCP) [110] is a network function in the
5G core network that facilitates communication between other network functions by providing a secure and efficient messaging service. It acts as a mediator for service-based interfaces. 5
[0041] Authentication Server Function (AUSF) [112] is a network function in
the 5G core responsible for authenticating UEs during registration and providing security services. It generates and verifies authentication vectors and tokens.
10 [0042] Network Slice Specific Authentication and Authorization Function
(NSSAAF) [114] is a network function that provides authentication and authorization services specific to network slices. It ensures that UEs can access only the slices for which they are authorized.
15 [0043] Network Slice Selection Function (NSSF) [116] is a network function
responsible for selecting the appropriate network slice for a UE based on factors such as subscription, requested services, and network policies.
[0044] Network Exposure Function (NEF) [118] is a network function that
20 exposes capabilities and services of the 5G network to external applications,
enabling integration with third-party services and applications.
[0045] Network Repository Function (NRF) [120] is a network function that acts
as a central repository for information about available network functions and
25 services. It facilitates the discovery and dynamic registration of network functions.
[0046] Policy Control Function (PCF) [122] is a network function responsible
for policy control decisions, such as QoS, charging, and access control, based on subscriber information and network policies. 30
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[0047] Unified Data Management (UDM) [124] is a network function that
centralizes the management of subscriber data, including authentication, authorization, and subscription information.
5 [0048] Application Function (AF) [126] is a network function that represents
external applications interfacing with the 5G core network to access network capabilities and services.
[0049] User Plane Function (UPF) [128] is a network function responsible for
10 handling user data traffic, including packet routing, forwarding, and QoS
enforcement.
[0050] Data Network (DN) [130] refers to a network that provides data services
to user equipment (UE) in a telecommunications system. The data services may
15 include but are not limited to Internet services, private data network related services.
[0051] The 5GC network architecture also comprises a plurality of interfaces for
connecting the network functions with a network entity for performing the network functions. The NSSF [116] is connected with the network entity via the interface
20 denoted as (Nnssf) interface in the figure. The NEF [118] is connected with the
network entity via the interface denoted as (Nnef) interface in the figure. The NRF [120] is connected with the network entity via the interface denoted as (Nnrf) interface in the figure. The PCF [122] is connected with the network entity via the interface denoted as (Npcf) interface in the figure. The UDM [124] is connected with
25 the network entity via the interface denoted as (Nudm) interface in the figure. The
AF [126] is connected with the network entity via the interface denoted as (Naf) interface in the figure. The NSSAAF [114] is connected with the network entity via the interface denoted as (Nnssaaf) interface in the figure. The AUSF [112] is connected with the network entity via the interface denoted as (Nausf) interface in
30 the figure. The AMF [106] is connected with the network entity via the interface
denoted as (Namf) interface in the figure. The SMF [108] is connected with the
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network entity via the interface denoted as (Nsmf) interface in the figure. The SMF
[108] is connected with the UPF [128] via the interface denoted as (N4) interface
in the figure. The UPF [128] is connected with the RAN [104] via the interface
denoted as (N3) interface in the figure. The UPF [128] is connected with the DN
5 [130] via the interface denoted as (N6) interface in the figure. The RAN [104] is
connected with the AMF [106] via the interface denoted as (N2). The AMF [106]
is connected with the RAN [104] via the interface denoted as (N1). The UPF [128]
is connected with other UPF [128] via the interface denoted as (N9). The interfaces
such as Nnssf, Nnef, Nnrf, Npcf, Nudm, Naf, Nnssaaf, Nausf, Namf, Nsmf, N9, N6, N4, N3, N2,
10 and N1 can be referred to as a communication channel between one or more
functions or modules for enabling exchange of data or information between such functions or modules, and network entities.
[0052] FIG. 2 illustrates an exemplary block diagram of a computing device
15 [200] (herein, also referred to as a computer system [200]) upon which one or more
features of the present disclosure may be implemented in accordance with an
exemplary implementation of the present disclosure. In an implementation, the
computing device [200] may also implement a method for locally generating and
storing key performance indicators (KPIs), utilising a system, or one or more sub-
20 systems, provided in the network. In another implementation, the computing device
[200] itself implements the method for locally generating and storing KPIs, using
one or more units configured within the computing device [200], wherein said one
or more units are capable of implementing the features as disclosed in the present
disclosure.
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[0053] The computing device [200] may include a bus [202] or other
communication mechanism(s) for communicating information, and a hardware
processor [204] coupled with bus [202] for processing said information. The
hardware processor [204] may be, for example, a general-purpose microprocessor.
30 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
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bus [202], for storing information and instructions to be executed by the processor
[204]. The 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 a non-transitory storage
5 media accessible to the processor [204], render the computing device [200] into a
special purpose device that is customized to perform operations according to the 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].
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[0054] A storage device [210], such as a magnetic disk, optical disk, or solid-
state 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 display [212], such as a cathode ray tube (CRT), Liquid crystal Display (LCD),
15 Light Emitting Diode (LED) display, Organic LED (OLED) display, etc., for
displaying information to a user of the computing device [200]. An input device [214], including alphanumeric and other keys, touch screen input means, etc. may be coupled to the bus [202] for communicating information and command selections to the processor [204]. Another type of user input device may be a cursor
20 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]. The cursor controller [216] typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the cursor controller [216] to specify
25 positions in a plane.
[0055] The computing device [200] may implement the techniques described
herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware
and/or program logic which, in combination with the computing device [200],
30 causes or programs the computing device [200] to be a special-purpose device.
According to one implementation, the techniques herein are performed by the
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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]. The
one or more instructions may be read into the main memory [206] from another
storage medium, such as the storage device [210]. Execution of the one or more
5 sequences of the one or more 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, software instructions.
10 [0056] The computing device [200] also may include a communication interface
[218] coupled to the bus [202]. The communication interface [218] provides two-way data communication coupling to a network link [220] that is connected to a local network [222]. For example, the communication interface [218] may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or
15 a modem to provide a data communication connection to a corresponding type of
telecommunication line. In 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 implementation, the communication interface [218] sends and receives
20 electrical, electromagnetic or optical signals that carry digital data streams
representing different types of information.
[0057] The computing device [200] can send and receive data, including
program code, messages, etc. through the network(s), the network link [220] and
25 the communication interface [218]. In an 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, and/or stored in the storage device [210], or other non-volatile storage for later execution.
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[0058] Referring to FIG. 3, an exemplary block diagram of a system [300] for
locally generating and storing key performance indicators (KPIs), is shown, in
accordance with the exemplary implementations of the present disclosure. The
system [300] comprises at least one transceiver unit [302], at least one computation
5 unit [304], at least one storage unit [306], and at least one assessing unit [308]. 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 figures all units
shown within the system [300] should also be assumed to be connected to each
other. Also, in Figure 3 only a few units are shown, however, the system [300] may
10 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 reside in a server or a network entity.
15 [0059] The system [300] is configured for locally generating and storing key
performance indicators KPIs, with the help of the interconnection between the components/units of the system [300].
[0060] Further, in accordance with the present disclosure, it is to be
20 acknowledged that the functionality described for the various components/units can
be implemented interchangeably. While specific embodiments may disclose a
particular functionality of these units for clarity, it is recognized that various
configurations and combinations thereof are within the scope of the disclosure. The
functionality of specific units as disclosed in the disclosure should not be construed
25 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 of the present disclosure.
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[0061] The system [300] comprises a Key Performance Indicator (KPI)
manager. In an implementation, the KPI manager is tasked for management of KPIs.
5 [0062] Further, the KPI manager comprises the transceiver unit [302] configured
to receive a set of performance counters related to one or more processes at one or more network nodes in a network function (NF) cluster.
[0063] Herein, the NF cluster is referred to a collection of the one or more
10 network nodes which may include, but not limited to, a network repository function
(NRF), policy control function (PCF), unified data management (UDM), session
management function (SMF), access and mobility management function (AMF),
etc. Further, the set of performance counters mentioned herein are referred to as one
or more indicators that may reflect the operational performance of the one or more
15 nodes such as: For example, in case the network node is the SMF, the set of
performance counter associated with the SMF may include a number of requests
received for establishing protocol data unit (PDU) session, a frequency at which the
PDU sessions are released by the SMF, an amount of data being transferred to the
active PDU sessions, and similar known to a person skilled in the art. Further, in an
20 implementation, the KPI manager may generate and store the set of performance
counters for the one or more nodes in the NF cluster, that are received at the
transceiver unit.
[0064] Further, the KPI manager comprises the computation unit [304]. The
25 computation unit [304] is configured to generate locally a set of KPIs comprising
one or more KPIs, based on the set of performance counters. The computation unit
[304] herein is connected at least to the transceiver unit [302]. The computation unit
[304] may further process the set of performance counters that are received at the
transceiver unit [302], in order to locally generate the set of KPIs from the set of
30 performance counters. The set of KPIs comprising the one or more KPIs may
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further reflect an operation status and overall performance of the one or more network nodes.
[0065] In an example embodiment, based on the set of performance counters
5 associated with the SMF (as mentioned earlier), the computation unit may further
generate the one or more KPIs for the SMF which may include, for example, a percentage of successful PDU session setups out of the total session setup requests, which may indicate the dependability on the SMF in establishing the PDU sessions.
10 [0066] In another example, the generated one or more KPIs may include an
average data transfer per second in the active PDU sessions, which may further reflect an overall data throughput of the SMF.
[0067] Further, the local generation of the set of KPIs mentioned herein may
15 refer that the KPI manager via the computation unit [304] may directly derive the
set of KPIs from the set of performance counters, without requiring any external systems, such as a north bound monitoring system (NMS). The NMS may refer to a system that is connected to one or more KPI managers for storing the performance of the NF cluster based on the one or more KPIs, which may be utilized for future
20 trends and analysis. Also, the local generation of KPIs refers to generating KPIs by
the KPI managers at nodes or the NFs by directly receiving the performance counters from the NFs, and storing the KPIs with storage units at the nodes implementing the NFs as well as at the NF cluster (which is a central unit and may be connected to each NF in the NF cluster). In an exemplary implementation,
25 separate storage units may be present at the individual NFs and the NF clusters.
This makes the KPIs available locally for monitoring or assessing the health of the NFs or the NF clusters and also for exactly pin-pointing the cause of any issue that may arise with an NF. However, the KPIs that are generated by the KPI manager may be further sent to the NMS for further use, as may be desired.
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[0068] In addition, the local generation of the set of KPIs may further reduces
the time required to process and access a performance data of the one or more nodes.
Further, the extra message flow caused between the NF and NMS for sending the
set of performance counters and generating KPIs will be reduced, which may
5 further improve the efficiency of KPI manager in real time. Further, the local
generation of the set of KPIs may also assist in faster detection and resolution of performance issues that might be present in the one or more nodes.
[0069] As noted above, the KPI manager comprises the storage unit [306]
10 configured to store the one or more KPIs at a cluster level and a node level. The
storage unit [306] herein is connected at least to the computation unit [304].
[0070] In one implementation, the storage unit [306] may store the set of KPIs
at the node level. Herein the node level, may refer that the storage unit [306] stores
15 the set of KPIs for each node present within the NF cluster separately. For example,
by storing the set of KPIs for a node (suppose SMF) may further assist in tracking and monitoring the performance of the SMF node independently.
[0071] In another implementation, the storage unit [306] may store the set of
20 KPIs at the cluster level. Herein, the cluster level, may refer that the storage unit
[306] may further store a collective set of KPIs of the one or more nodes, in order
to allow the KPI manager to analyse the overall performance of the NF cluster. For
example, if the one or more nodes in the NF cluster are NRF, PCF, UDM, SMF,
and AMF, and then the one or more nodes may perform one or more functions in
25 collaboration with each other such as PDU session management, authentication,
policy control, and user equipment mobility. Further, the storing of the set of KPIs at the cluster level may further reflect an overall performance of the NF cluster.
[0072] Further, the KPI manager comprises the assessing unit [308] configured
30 to assess a KPI degradation issue in the event the at least one of the one or more
network nodes in the NF cluster are malfunctioning. The assessing unit [308] herein
20

is connected at least to the storage unit [306]. The assessing unit [308] may analyse
set of KPIs for each node from the one or more nodes, in order to identify any signs
of degradation in set of KPIs within a specific node. The degradation within the
specific node may refer to a low performance (such as malfunction) of said node
5 based on the one or more set of KPIs. The assessing unit [308] may identify such
malfunctions based on a comparison of the real-time performance of a specific node
with a pre-defined threshold of said specific node. In case, the real-time
performance of said specific node may negatively divert from the pre-defined
threshold, the assessing unit [308] may address the malfunctioning of said specific
10 node to the KPI manager.
[0073] It is to be noted that the malfunction within the node may arise due to one
or more reasons, such as network congestions at the node, configuration issues
during an update or maintenance of the node, any external interference, such as
15 electromagnetic interference, and other reasons known to a person skilled in the art.
[0074] The one or more KPIs are made available to a target network node, in an
event the at least one of the one or more network nodes in the NF cluster are malfunctioning. Here, the target node may use the one or more KPIs to assess the
20 root cause of the issue that the malfunctioning node may be facing. This assessment
may be further used to correct the issue. Also, the target node may be a node that performs this assessment, and/or may take responsibility of the malfunctioning node, and/or may designate one or more operations of the malfunctioning node to other nodes in order to avoid any major impact in the one or more services offered
25 by the one or more nodes.
[0075] In addition, the transceiver unit [302] is further configured to transmit the
one or more set of KPIs to the north bound monitoring system (NMS). Post
receiving, the one or more set of KPIs, the NMS may further store the one or more
30 set of KPIs for future analysis.
21

[0076] In an exemplary scenario, suppose a NF cluster XYZ may include a set
of nodes such as the NRF, PCF, UDM, SMF, and AMF for performing one or more
functions such as PDU session management, authentication, policy control, and
user equipment mobility. Further, based on the set of performance counters,
5 received at the transceiver unit [302], and the computation unit [304] locally
generates the set of KPIs which are then stored at node level and cluster level as mentioned earlier.
[0077] Further, in an event of a malfunction in one of the network nodes (e.g.,
10 AMF), the assessing unit [308] detects performance degradation. For instance, the
AMF may experience a mobility management failure, leading to increased
handover failures for connected User Equipment (UE). Since the AMF node is
experiencing the issue, the KPI manager may fetch the KPIs from the AMF node
level or the cluster level, avoiding any extra message flow with the NMS, which
15 was being performed in the existing and known systems.
[0078] Referring to FIG. 4, an exemplary method flow diagram [400] for locally
generating and storing key performance indicators (KPIs), in accordance with
exemplary implementations of the present disclosure is shown. In an
20 implementation the method [400] is performed by the system [300]. Further, in an
implementation, the system [300] may be present in a server device to implement the features of the present disclosure.
[0079] Also, as shown in Figure 4, the method [400] initially starts at step [402].
25
[0080] At step [404], the method [400] comprises steps of receiving, by the
transceiver unit [302] at the KPI manager, the set of performance counters related to one or more processes at one or more network nodes in the NF cluster. The method [400] further explains that the KPI manager is tasked for management of
30 KPIs. Further, the NF cluster is referred to a collection of the one or more network
nodes which may include, but not limited to, a network repository function (NRF),
22

policy control function (PCF), unified data management (UDM), session
management function (SMF), access and mobility management function (AMF),
etc. Further, the set of performance counters mentioned herein are referred to as one
or more indicators that may reflect the operational performance of the one or more
5 nodes such as: For example, in case the network node is the SMF, the set of
performance counter associated with the SMF may include a number of requests
received for establishing protocol data unit (PDU) session, a frequency at which the
PDU sessions are released by the SMF, an amount of data being transferred to the
active PDU sessions, and similar known to a person skilled in the art. Further, in an
10 implementation, the KPI manager may retrieve and stores the set of performance
counters for the one or more nodes in the NF cluster, that are received at the transceiver unit.
[0081] At step [406], the method [400] comprises steps of generating locally, by
15 the computation unit [304] at the KPI manager, the set of KPIs comprising one or
more KPIs, based on the set of performance counters. The method [400] further
explains that the computation unit [304] may further process the set of performance
counters that are received at the transceiver unit [302], in order to locally generate
the set of KPIs from the set of performance counters. The set of KPIs comprising
20 the one or more KPIs may further reflect an operation status and overall
performance of the one or more network nodes.
[0082] In an example embodiment, based on the set of performance counters
associated with the SMF (as mentioned earlier), the computation unit may further
25 generate the one or more KPIs for the SMF which may include, for example, a
percentage of successful PDU session setups out of the total session setup requests, which may indicate the dependability on the SMF in establishing the PDU sessions.
[0083] In another example, the generated one or more KPIs may include an
30 average data transfer per second in the active PDU sessions, which may further
reflect an overall data throughput of the SMF.
23

[0084] Further, the local generation of the set of KPIs mentioned herein may
refer that the KPI manager via the computation unit [304] may directly derive the
set of KPIs from the set of performance counters, without requiring any external
5 systems, such as a north bound monitoring system (NMS). The NMS may refer to
a system that is connected to one or more KPI managers for storing the performance of the NF cluster based on the one or more KPIs, which may be utilized for future trends and analysis. Also, the local generation of KPIs refers to generating KPIs by the KPI managers at nodes or the NFs by directly receiving the performance
10 counters from the NFs, and storing the KPIs with storage units at the nodes
implementing the NFs as well as at the NF cluster (which is a central unit and may be connected to each NF in the NF cluster). In an exemplary implementation, separate storage units may be present at the individual NFs and the NF clusters. This makes the KPIs available locally for monitoring or assessing the health of the
15 NFs or the NF clusters and also for exactly pin-pointing the cause of any issue that
may arise with an NF. However, the KPIs that are generated by the KPI manager may be further sent to the NMS for further use, as may be desired.
[0085] In addition, the local generation of the set of KPIs may further reduces
20 the time required to process and access a performance data of the one or more nodes.
Further, the extra message flow caused between the NF and NMS for sending the
set of performance counters and generating KPIs will be reduced, which may
further improve the efficiency of KPI manager in real time. Further, the local
generation of the set of KPIs may also assist in faster detection and resolution of
25 performance issues that might be present in the one or more nodes.
[0086] At step [408], the method [400] comprises steps of storing, by the storage
unit [306] at the KPI manager, the one or more KPIs at the cluster level and the
node level. The method [400] further explains that the storage unit [306] is
30 connected at least to the computation unit [304].
24

[0087] In one implementation, the storage unit [306] may store the set of KPIs
at the node level. Herein the node level, may refer that the storage unit [306] stores
the set of KPIs for each node present within the NF cluster separately. For example,
by storing the set of KPIs for a node (suppose SMF) may further assist in tracking
5 and monitoring the performance of the SMF node independently.
[0088] In another implementation, the storage unit [306] may store the set of
KPIs at the cluster level. Herein, the cluster level, may refer that the storage unit [306] may further store a collective set of KPIs of the one or more nodes, in order
10 to allow the KPI manager to analyse the overall performance of the NF cluster. For
example, if the one or more nodes in the NF cluster are NRF, PCF, UDM, SMF, and AMF, and then the one or more nodes may perform one or more functions in collaboration with each other such as PDU session management, authentication, policy control, and user equipment mobility. Further, the storing of the set of KPIs
15 at the cluster level may further reflect an overall performance of the NF cluster.
[0089] The method [400] further comprise assessing, by the assessing unit [308],
the KPI degradation issue in the event the at least one of the one or more network nodes in the NF cluster are malfunctioning. The method [400] further explains that
20 the one or more KPIs are made available to the target network node, in the event
the at least one of the one or more network nodes in the NF cluster are malfunctioning. The assessing unit [308] may analyse set of KPIs for each node from the one or more nodes, in order to identify any signs of degradation in set of KPIs within a specific node. The degradation within the specific node may refer to
25 a low performance (such as malfunction) of said node based on the one or more set
of KPIs. The assessing unit [308] may identify such malfunctions based on a comparison of the real-time performance of a specific node with a pre-defined threshold of said specific node. In case, the real-time performance of said specific node may negatively divert from the pre-defined threshold, the assessing unit [308]
30 may address the malfunctioning of said specific node to the KPI manager.
25

[0090] It is to be noted that the malfunction within the node may arise due to one
or more reasons, such as network congestions at the node, configuration issues during an update or maintenance of the node, any external interference, such as electromagnetic interference, and other reasons known to a person skilled in the art. 5
[0091] The one or more KPIs are made available to a target network node, in an
event the at least one of the one or more network nodes in the NF cluster are malfunctioning. Here, the target node may use the one or more KPIs to assess the root cause of the issue that the malfunctioning node may be facing. This assessment
10 may be further used to correct the issue. Also, the target node may be a node that
performs this assessment, and/or may take responsibility of the malfunctioning node, and/or may designate one or more operations of the malfunctioning node to other nodes in order to avoid any major impact in the one or more services offered by the one or more nodes.
15
[0092] The method [400] further comprise transmitting, by the transceiver unit
[302] at the KPI manager, the one or more KPIs to the north bound monitoring system (NMS). The method [400] further explains that post receiving, the one or more set of KPIs, the NMS may further store the one or more set of KPIs for future
20 analysis.
[0093] In an exemplary scenario, suppose a NF cluster XYZ may include a set
of nodes such as the NRF, PCF, UDM, SMF, and AMF for performing one or more
functions such as PDU session management, authentication, policy control, and
25 user equipment mobility. Further, based on the set of performance counters,
received at the transceiver unit [302], and the computation unit [304] locally generates the set of KPIs which are then stored at node level and cluster level as mentioned earlier.
30 [0094] Further, in an event, of a malfunction in one of the network nodes (e.g.,
AMF), the assessing unit [308] detects performance degradation. For instance, the
26

AMF may experience a mobility management failure, leading to increased handover failures for connected User Equipment (UE). Since the AMF node is experiencing the issue, the KPI manager may fetch the KPIs from the AMF node level or the cluster level, avoiding any extra message flow with the NMS, which was being performed in the existing and known systems.
[0095] The method [400] herein terminates at step [410].
[0096] The present disclosure further discloses a non-transitory computer
readable storage medium storing instructions for locally generating and storing KPIs, the instructions include executable code which, when executed by one or more units of a system, causes: a transceiver unit [302] to receive a set of performance counters related to one or more processes at one or more network nodes in a network function (NF) cluster. Further, the executable code executed by one or more units of a system causes a computation unit [304] to generate locally a set of KPIs comprising one or more KPIs, based on the set of performance counters. Furthermore, the executable code executed by one or more units of a system causes a storage unit [306] to store the one or more KPIs at a cluster level and a node level.
[0097] As is evident from the above, the present disclosure provides a
technically advanced solution for locally generating and storing KPIs. The present solution encompasses many advantages such as to pinpoint exact issue in an event the KPI is down with the precise problem while also locating the exact NF node, while serving live traffic. The present disclosure further improves the operation efficiency of the NF nodes as the local calculation of set of KPIs for each NF node allows in effectively acting on any real-time performance issues faced by the NF nodes.
[0098] While considerable emphasis has been placed herein on the disclosed
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 implemented is illustrative and non-limiting.

We Claim:
1. A method [400] for locally generating and storing key performance indicators
(KPIs), the method [400] comprising:
- receiving, by a transceiver unit [302] at a Key Performance Indicator (KPI) manager, a set of performance counters related to one or more processes at one or more network nodes in a network function (NF) cluster;
- generating locally, by a computation unit [304] at the KPI manager, a set of KPIs comprising one or more KPIs, based on the set of performance counters; and
- storing, by a storage unit [306] at the KPI manager, the one or more KPIs at a cluster level and a node level.
2. The method [400] as claimed in claim 1, the method [400] further comprising:
- transmitting, by the transceiver unit [302] at the KPI manager, the one or
more KPIs to a north bound monitoring system (NMS).
3. The method [400] as claimed in claim 1, wherein the one or more KPIs are made available to a target network node, in an event the at least one of the one or more network nodes in the NF cluster are malfunctioning.
4. The method [400] as claimed in claim 3, the method [400] further comprising:
- assessing, by an assessing unit [308], a KPI degradation issue in the event
the at least one of the one or more network nodes in the NF cluster are
malfunctioning.
5. A system [300] for locally generating and storing key performance indicators
(KPIs), the system [300] comprising a Key Performance Indicator (KPI)
manager, the KPI manager further comprising:

- a transceiver unit [302] configured to receive a set of performance counters related to one or more processes at one or more network nodes in a network function (NF) cluster;
- a computation unit [304] configure to generate locally a set of KPIs comprising one or more KPIs, based on the set of performance counters; and
- a storage unit [306] configured to store the one or more KPIs at a cluster level and a node level.

6. The system [300] as claimed in claim 5, wherein the transceiver unit [302] is configured to transmit the one or more KPIs to a north bound monitoring system (NMS).
7. The system [300] as claimed in claim 5, wherein the one or more KPIs are made available to a target network node, in an event the at least one of the one or more network nodes in the NF cluster are malfunctioning.
8. The system [300] as claimed in claim 7, the system [300] further comprising:
an assessing unit [308] configured to assess a KPI degradation issue in the event
the at least one of the one or more network nodes in the NF cluster are
malfunctioning.