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 PROVIDING SOFTWARE UPGRADE RECOMMENDATIONS”
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.

5 METHOD AND SYSTEM FOR PROVIDING SOFTWARE UPGRADE
RECOMMENDATIONS
TECHNICAL FIELD
The present disclosure relates generally to the field of wireless communication
10 systems. More particularly, the present disclosure relates to methods and systems
for providing software upgrade recommendations.
BACKGROUND
The following description of related art is intended to provide background
15 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 be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.
20
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
25 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
30 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.
2

5
Existing solutions for network software upgrades often rely on manual processes and lack real-time predictive capabilities. These solutions typically involve scheduled upgrades without considering the dynamic nature of network traffic and performance indicators. As a result, there is a higher risk of degraded network
10 performance or failure post-upgrade, necessitating a rollback to the previous
version. This reactive approach can lead to disruptions in service and a negative impact on user experience. Furthermore, existing solutions do not employ advanced analytics or artificial intelligence to predict the outcomes of an upgrade. Consequently, network administrators must rely on historical data and intuition to
15 decide when and how to implement upgrades. This lack of predictive insight can
result in sub-optimal upgrade timings, leading to unnecessary downtime or prolonged exposure to security vulnerabilities. Additionally, traditional upgrade methods do not provide a mechanism for gradually directing traffic to upgraded instances. Instead, traffic is often switched in bulk, which can overwhelm the new
20 software and lead to immediate performance issues. This all-or-nothing approach
lacks the flexibility to test the upgrade under real-world conditions and adjust based on performance metrics.
Thus, there exists an imperative need in the art to provide system and method for
25 providing software upgrade recommendation and overcome the limitations of the
existing technologies, which the present disclosure aims to address.
OBJECTS OF THE PRESENT DISCLOSURE
Some of the objects of the present disclosure, which at least one embodiment
30 disclosed herein satisfies are listed herein below.
It is an object of the present disclosure to provide system and method for providing software upgrade recommendations.
3

5 It is another object of the present disclosure to provide a system and method for
providing software upgrade recommendations that reduce the chances of failure post-upgrade by gradually increasing the load on the upgraded instance.
It is another object of the present disclosure to provide a system and method for
10 providing software upgrade recommendations that automatically and quickly
decide on further rollout of the upgraded release in the network based on real-time performance data.
It is another object of the present disclosure to provide a system and method for
15 providing software upgrade recommendations that employ artificial intelligence to
predict thresholds for various key performance indicators (KPIs), allowing for proactive decision-making during the upgrade process.
It is another object of the present disclosure to provide a system and method for
20 providing software upgrade recommendations that enable the monitoring and
comparison of current KPIs with predicted thresholds, ensuring that upgrades are only fully implemented if they meet predefined performance criteria.
It is another object of the present disclosure to provide a system and method for
25 providing software upgrade recommendations that can automatically fetch upgrade
releases from a Network Management System (NMS) Platform, streamlining the upgrade process.
It is yet another object of the present disclosure to provide a system and method for
30 providing software upgrade recommendations that minimize the impact on user
experience by ensuring that network performance is maintained or enhanced post-upgrade.
SUMMARY
4

5 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.
10 An aspect of the present disclosure may relate to a method for providing software
upgrade recommendations. The method includes identifying, by an identification unit at a Service Communication Proxy (SCP) controller, an upgrade release of one or more instances of one or more network functions (NFs). The method further includes monitoring, by a monitoring unit at the SCP controller, one or more Key
15 Performance Indicators (KPIs) of the upgraded one or more instances of the one or
more NFs. The method further includes predicting, by a predicting unit using an intelligent module, a set of thresholds corresponding to the one of more KPIs of the upgraded one or more instances of the one or more NFs. The method further includes comparing, by a comparator at the SCP controller, the monitored one or
20 more KPIs with the corresponding predicted set of thresholds. The method further
includes generating, by a generation unit at the SCP controller, at least a recommendation for performing at least one of removing and implementing the upgrade release based at least on the comparison.
25 In an aspect, the method comprises directing, by a processing unit at the SCP
controller, at least a fraction of network traffic to the upgraded one or more instances of the one or more NFs.
In an aspect, the method comprises directing, by the processing at the SCP
30 controller, at least other fraction of the network traffic to other one or more
instances of other one or more NFs.
5

5 In an aspect, the method comprises monitoring, by the monitoring unit at the SCP
controller, the one or more KPIs of the upgraded one or more instances of the one or more NFs periodically after a predefined time period.
In an aspect, the method comprises determining, by a determination unit at the SCP
10 controller, whether the one of more KPIs of the upgraded one or more instances of
the one or more NFs breaches the corresponding predicted set of thresholds.
In an aspect, upon determining that the one or more KPIs of the upgraded one or
more instances of the one or more NFs breaches or fails to breach the corresponding
15 predicted set of thresholds, generating, by the generation unit, at least the
recommendation for removing the upgrade for the upgraded one or more instances of the one or more NFs.
In an aspect, upon determining that the one or more KPIs of the upgraded one or
20 more instances of the one or more NFs fails to breach the corresponding predicted
set of thresholds, generating, by the generation unit, at least the recommendation for implementing the upgrade for the upgraded one or more instances of the one or more NFs corresponding to gradual increase in the network traffic for the upgraded one or more instances of the one or more NFs. 25
In an aspect, the one or more KPIs comprises at least one of error code percentage KPI, traffic load information KPI, request timeout KPI, and request failure KPI.
In an aspect, the intelligent module comprises a trained model, the trained model is
30 trained based on historical data, wherein the historical data comprises parameters
comprising at least one of request timeout, response time, and combination thereof.
In an aspect, the upgrade release is automatically fetched from a Network Management System (NMS) Platform.
6

5
Another aspect of the present disclosure relates to a system for providing software upgrade recommendations. The system includes an identification unit at a Service Communication Proxy (SCP) controller configured to identify an upgrade release of one or more instances of one or more network functions (NFs). The system
10 further includes a monitoring at the SCP controller configured to monitor one or
more Key Performance Indicators (KPIs) of the upgraded one or more instances of the one or more NFs. The system further includes a predicting unit configured to predict, using an intelligent module, a set of thresholds corresponding to the one of more KPIs of the upgraded one or more instances of the one or more NFs. The
15 system further includes a comparator at the SCP controller configured to compare
the monitored one or more KPIs with the corresponding predicted set of thresholds. Further, the system includes a generation unit at the SCP controller configured to generate at least a recommendation for performing at least one of removing and implementing the upgrade release based at least on the comparison.
20
Yet another aspect of the present disclosure may relate to a non-transitory computer-readable storage medium storing instruction for providing software upgrade recommendations, the storage medium comprising executable code which, when executed by one or more units of a system, causes: an identification unit to
25 identify an upgrade release of one or more instances of one or more network
functions (NFs); a monitoring unit to monitor one or more Key Performance Indicators (KPIs) of the upgraded one or more instances of the one or more NFs; a predicting unit to predict, using an intelligent module, a set of thresholds corresponding to the one of more KPIs of the upgraded one or more instances of
30 the one or more NFs; a comparator to compare the monitored one or more KPIs
with the corresponding predicted set of thresholds; and a generation unit to generate at least a recommendation for performing at least one of removing and implementing the upgrade release based at least on the comparison.
7

5 BRIEF DESCRIPTION OF DRAWINGS
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,
10 emphasis instead being placed upon clearly illustrating the principles of the present
disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components, electronic components or circuitry commonly used to
15 implement such components.
FIG. 1 illustrates an exemplary block diagram representation of 5th generation core (5GC) network architecture.
20 FIG. 2 illustrates an exemplary block diagram of a system for providing software
upgrade recommendation, in accordance with the exemplary implementations of the present disclosure.
FIG. 3 illustrates an exemplary block diagram of an architecture for implementation
25 of a system for providing software upgrade recommendation, in accordance with
exemplary implementations of the present disclosure.
FIG. 4 illustrates an exemplary method flow diagram indicating the process for
providing software upgrade recommendation, in accordance with exemplary
30 embodiments of the present disclosure.
FIG. 5 illustrates an exemplary block diagram of a computing device upon which an embodiment of the present disclosure may be implemented.
8

5 The foregoing shall be more apparent from the following more detailed description
of the disclosure.
DETAILED DESCRIPTION
In the following description, for the purposes of explanation, various specific details
10 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 can each be used independently of one another or with any combination of other features. An individual feature may not address any of the
15 problems discussed above or might address only some of the problems discussed
above. Some of the problems discussed above might not be fully addressed by any of the features described herein. Example embodiments of the present disclosure are described below, as illustrated in various drawings in which like reference numerals refer to the same parts throughout the different drawings.
20
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
25 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.
It should be noted that the terms "mobile device", "user equipment", "user device",
30 “communication device”, “device” and similar terms are used interchangeably for
the purpose of describing the invention. These terms are not intended to limit the scope of the invention or imply any specific functionality or limitations on the described embodiments. The use of these terms is solely for convenience and clarity of description. The invention is not limited to any particular type of device or
9

5 equipment, and it should be understood that other equivalent terms or variations
thereof may be used interchangeably without departing from the scope of the invention as defined herein.
Specific details are given in the following description to provide a thorough
10 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, networks, processes, and other
components may be shown as components in block diagram form in order not to
obscure the embodiments in unnecessary detail. In other instances, well-known
15 circuits, processes, algorithms, structures, and techniques may be shown without
unnecessary detail in order to avoid obscuring the embodiments.
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,
20 or a block diagram. Although a flowchart may describe the operations as a
sequential process, many of the operations can 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.
25
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
30 construed as preferred or advantageous over other aspects or 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
10

5 “comprising” as an open transition word—without precluding any additional or
other elements.
As used herein, an “electronic device”, or “portable electronic device”, or “user device” or “communication device” or “user equipment” or “device” refers to any
10 electrical, electronic, electromechanical and computing device. The user device is
capable of receiving and/or transmitting one or parameters, performing function/s, communicating with other user devices and transmitting data to the other user devices. The user equipment may have a processor, a display, a memory, a battery and an input-means such as a hard keypad and/or a soft keypad. The user
15 equipment may be capable of operating on any radio access technology including
but not limited to IP-enabled communication, Zig Bee, Bluetooth, Bluetooth Low Energy, Near Field Communication, Z-Wave, Wi-Fi, Wi-Fi direct, etc. For instance, the user equipment may include, but not limited to, a mobile phone, smartphone, virtual reality (VR) devices, augmented reality (AR) devices, laptop,
20 a general-purpose computer, desktop, personal digital assistant, tablet computer,
mainframe computer, or any other device as may be obvious to a person skilled in the art for implementation of the features of the present disclosure.
Further, the user device may also comprise a “processor” or “processing unit”
25 includes processing unit, wherein processor refers to any logic circuitry for
processing instructions. The processor may be a general-purpose processor, a
special purpose processor, a conventional processor, a digital signal processor, a
plurality of microprocessors, one or more microprocessors in association with a
DSP core, a controller, a microcontroller, Application Specific Integrated Circuits,
30 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 the system according to the present disclosure. More specifically, the processor is a hardware processor.
11

5 As portable electronic devices and wireless technologies continue to improve and
grow in popularity, the advancing wireless technologies for data transfer are also
expected to evolve and replace the older generations of technologies. In the field of
wireless data communications, the dynamic advancement of various generations of
cellular technology are also seen. The development, in this respect, has been
10 incremental in the order of second generation (2G), third generation (3G), fourth
generation (4G), and now fifth generation (5G), and more such generations are expected to continue in the forthcoming time.
Radio Access Technology (RAT) refers to the technology used by mobile devices/
15 user equipment (UE) to connect to a cellular network. It refers to the specific
protocol and standards that govern the way devices communicate with base stations,
which are responsible for providing the wireless connection. Further, each RAT has
its own set of protocols and standards for communication, which define the
frequency bands, modulation techniques, and other parameters used for transmitting
20 and receiving data. Examples of RATs include GSM (Global System for Mobile
Communications), CDMA (Code Division Multiple Access), UMTS (Universal
Mobile Telecommunications System), LTE (Long-Term Evolution), and 5G. The
choice of RAT depends on a variety of factors, including the network infrastructure,
the available spectrum, and the mobile device's/device's capabilities. Mobile
25 devices often support multiple RATs, allowing them to connect to different types
of networks and provide optimal performance based on the available network
resources.
As used herein, a Service Communication Proxy (SCP) in the 5G system
30 architecture is designed to offer a variety of functions related to network
communication. These functions include indirect communication, delegated discovery, and message routing and forwarding to destination network functions or other SCPs. Additionally, SCP supports communication security aspects like authorization, load balancing, monitoring, and overload control. SCPs can also
12

5 interact with UDR, to resolve the UDM Group ID/UDR Group ID/AUSF Group
ID/PCF Group ID/CHF Group ID/HSS Group ID based on UE identity, e.g. SUPI or IMPI/IMPU. SCPs can be deployed at PLMN level, shared-slice level and slice-specific level. It is left to operator deployment to ensure that SCPs can communicate with relevant NRFs. In order to enable SCPs to route messages through several
10 SCPs, an SCP may register its profile in the NRF. Alternatively, local configuration
may be used.. In addition, the SCP is configured for message forwarding and routing to destination NF/NF service, message forwarding and routing to a next hop SCP, communication security (e.g., authorization of the NF Service Consumer to access the NF Service Producer API), load balancing, monitoring, overload control
15 and the like.
As discussed in the background section, Existing solutions for network software upgrades often rely on manual processes and lack real-time predictive capabilities. These solutions typically involve scheduled upgrades without considering the
20 dynamic nature of network traffic and performance indicators. As a result, there is
a higher risk of degraded network performance or failure post-upgrade, necessitating a rollback to the previous version. This reactive approach can lead to disruptions in service and a negative impact on user experience. Furthermore, existing solutions do not employ advanced analytics or artificial intelligence to
25 predict the outcomes of an upgrade. Consequently, network administrators must
rely on historical data and intuition to decide when and how to implement upgrades. This lack of predictive insight can result in suboptimal upgrade timings, leading to unnecessary downtime or prolonged exposure to security vulnerabilities. Additionally, traditional upgrade methods do not provide a mechanism for
30 gradually directing traffic to upgraded instances. Instead, traffic is often switched
in bulk, which can overwhelm the new software and lead to immediate performance issues. This all-or-nothing approach lacks the flexibility to test the upgrade under real-world conditions and adjust based on performance metrics.
13

5 The present disclosure aims to overcome the above-mentioned and other existing
problems in this field of technology by providing an efficient and effective system
and method for software upgrade recommendations based on real-time traffic
analysis and predictive analytics. Unlike the traditional methods that rely on manual
processes and lack predictive capabilities, the disclosed system employs artificial
10 intelligence to predict key performance indicators (KPIs) thresholds, enabling
proactive decision-making during the upgrade process. The present disclosure
introduces a methodology where only a fraction of network traffic is directed to the
upgraded instance initially, reducing the risk of immediate performance
degradation. This gradual approach allows for real-time monitoring of the upgraded
15 instance's performance against predicted KPI thresholds. If the performance is
within acceptable limits, the system gradually increases the traffic load to the
upgraded instance, ensuring a smooth transition without overwhelming the system.
Furthermore, the present disclosure utilizes an intelligent module based on
historical data to predict the KPI thresholds, providing a more accurate and data-
20 driven approach to upgrade decision-making. This predictive capability enables
network administrators to implement upgrades at optimal times, minimizing the risk
of downtime and enhancing overall network performance.
By automatically fetching upgrade releases from a Network Management System
25 (NMS) Platform, the system streamlines the upgrade process, reducing the potential
for human error and ensuring that upgrades are implemented efficiently. The
system's ability to monitor and compare real-time KPIs with predicted thresholds
ensures that upgrades are only fully implemented if they meet predefined
performance criteria, thus reducing the likelihood of needing to roll back an upgrade
30 due to unexpected performance issues.
It would be appreciated by the person skilled in the art the, the present disclosure provides a system and method for software upgrade recommendations that address the limitations of existing solutions by incorporating artificial intelligence,
14

5 predictive analytics, and a gradual traffic management approach, resulting in more
reliable and efficient network upgrades.
Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the accompanying drawings.
10
FIG. 1 illustrates an exemplary block diagram representation of 5th generation core (5GC) network architecture, in accordance with exemplary embodiment of the present disclosure. As shown in FIG. 1, the 5GC network architecture [100] includes a user equipment (UE) [102], a radio access network (RAN) [104], an
15 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
20 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.
25
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, LTE). It consists of radio base stations and the radio access technologies that enable wireless communication.
30
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 procedures like handovers and paging.
15

5
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 (UPF) for data forwarding and handles IP address allocation and QoS enforcement.
10
Service Communication Proxy (SCP) [110] is a network function in the 5G core that facilitates communication between other network functions by providing a secure and efficient messaging service. It acts as a mediator for service-based interfaces.
15
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.
20 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.
25 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.
Network Exposure Function (NEF) [118] is a network function that exposes
30 capabilities and services of the 5G network to external applications, enabling
integration with third-party services and applications.
16

5 Network Repository Function (NRF) [120] is a network function that acts as a
central repository for information about available network functions and services. It facilitates the discovery and dynamic registration of network functions.
Policy Control Function (PCF) [122] is a network function responsible for policy
10 control decisions, such as QoS, charging, and access control, based on subscriber
information and network policies.
Unified Data Management (UDM) [124] is a network function that centralizes the
management of subscriber data, including authentication, authorization, and
15 subscription information.
Application Function (AF) [126] is a network function that represents external applications interfacing with the 5G core network to access network capabilities and services. 20
User Plane Function (UPF) [128] is a network function responsible for handling user data traffic, including packet routing, forwarding, and QoS enforcement.
Data Network (DN) [130] represents external networks or services that users
25 connect to through the mobile network, such as the internet or enterprise networks.
Referring to FIG. 2, an exemplary block diagram of a system [200] for providing
software upgrade recommendations is shown, in accordance with the exemplary
implementations of the present disclosure. The system [200] comprises an SCP
30 controller [200A]. The SCP controller [200A] comprises an identification unit
[202], a monitoring unit [204], a predicting unit [206], a comparator [208], a generation unit [210], a processing unit [212] and a determination unit [214]. Also, all of the components/ units of the system [200] are assumed to be connected to each other unless otherwise indicated below. Also, in FIG. 2 only a few units are
17

5 shown, however, the system [200] may comprise multiple such units or the system
[200] may comprise any such numbers of said units, as required to implement the features of the present disclosure. In an implementation, the system [200] may reside in a server, a network entity, an SCP controller [200A], [302] or an intelligent module [304] as disclosed in FIG. 3.
10
The system [200] comprises the identification unit [202] configured to identify an upgrade release of one or more instances of one or more network functions (NFs). The identification unit [202] is responsible for recognizing the specific version or release of the software that is intended for the upgrade. The upgrade release may
15 include new features, bug fixes, or enhancements aimed at improving the
performance or security of the network functions. The upgrade release may be automatically fetched from a Network Management System (NMS) Platform. This feature streamlines the upgrade process by eliminating the need for manual intervention to obtain the latest software release. The NMS serves as a centralized
20 repository for software releases, and the system [200] is configured to automatically
retrieve the relevant upgrade package for the network functions being upgraded. The automation ensures that the system always has access to the most current and compatible software versions, reducing the likelihood of errors and compatibility issues during the upgrade process. The identification unit [202] is responsible for
25 receiving a network behaviour data including but not limited to traffic data, fault
data, statistics data, performance data and Key Performance Indicators (KPIs) from the SCP [100].
The system [200] comprises the monitoring unit [204] communicatively coupled to
30 the identification unit [202]. The monitoring unit [204] is configured to monitor one
or more Key Performance Indicators (KPIs) of the upgraded one or more instances of the one or more NFs. The one or more KPIs monitored by the monitoring unit [204] can include various metrics related to network performance, such as error code percentage, traffic load information, request timeout, and request failure rates.
18

5 By monitoring the one or more KPIs, the monitoring unit [204] provides real-time
data on how the upgraded instances are performing in the network environment.
The monitoring facilitates in assessing the impact of the upgrade on the network
and determining whether the upgrade has led to any degradation in performance.
The continuous monitoring of the one or more KPIs by the monitoring unit [204]
10 enables the system to make informed decisions about whether to proceed with the
full rollout of the upgrade, scale back the traffic directed to the upgraded instances, or even roll back the upgrade if significant issues are detected.
The system [200] comprises the predicting unit [206] communicatively coupled to
15 the monitoring unit [204]. The predicting unit [206] is configured to predict, using
an intelligent module, a set of thresholds corresponding to one or more Key Performance Indicators (KPIs) of the upgraded one or more instances of the one or more network functions (NFs). The intelligent module may include a trained model. The trained model utilizes historical data to predict the set of thresholds
20 corresponding to one or more KPIs, which are then used to determine acceptable
performance levels for the upgraded one or more instances of the one or more NFs. The predicting unit [206] facilitates in providing a foresight into the expected performance of the upgraded one or more instances of the one or more NFs, enabling proactive decision-making regarding the upgrade process.
25
The trained model may be trained using historical data that includes parameters such as request timeout, response time, or a combination of both. The trained model may be Machine Learning as a Service (MLaaS). The historical data is utilized to train the model to predict thresholds of the one or more KPIs. By analysing past
30 performance metrics, the trained model can identify patterns and trends that help in
setting appropriate thresholds for evaluating the performance of upgraded network functions. This data-driven approach enhances the accuracy of the predictions, enabling more informed decisions during the software upgrade process.
19

5 The system [200] comprises the comparator [208] communicatively coupled to the
monitoring unit [204] and the predicting unit [206]. The comparator [208] is configured to compare the monitored one or more KPIs with the corresponding predicted set of thresholds. The comparison facilitates in assessing whether the performance of the upgraded network functions is within acceptable limits. If the
10 monitored one or more KPIs deviate significantly from the predicted corresponding
thresholds, it may indicate potential issues with the upgrade. The comparator [208] facilitates in determining whether to continue with the upgrade rollout, increase the network traffic directed to the upgraded one or more instances of the one or more NFs, or roll back the upgrade to prevent further degradation of network
15 performance.
The system [200] comprises the generation unit [210] communicatively coupled to the monitoring unit [204], the predicting unit [206], and the comparator [208]. The generation unit [210] is configured to generate at least a recommendation for
20 performing at least one of removing and implementing the upgrade release based
on the comparison conducted by the comparator [208]. If the comparator [208] determines that the monitored one or more Key Performance Indicators (KPIs) of the upgraded one or more instances of the one or more NFs breaches the corresponding predicted set of thresholds, the generation unit [210] is configured to
25 generate a recommendation for removing the upgrade for the upgraded instances.
Conversely, if the monitored one or more KPIs fails to breach the set of predicted thresholds, the generation unit [210] is configured to generate a recommendation for implementing the upgrade for the upgraded one or more instances of the one or more NFs, which may include a gradual increase in network traffic directed to these
30 instances. This process ensures that the decision to proceed with or roll back the
upgrade is data-driven, minimizing the risk of degraded network performance and enhancing the overall efficiency of the upgrade process.
20

5 The system [200] comprises the processing unit [212] communicatively coupled to
the generation unit [210]. The processing unit [212] is configured to direct at least
a fraction of network traffic to the upgraded one or more instances of the one or
more network functions (NFs). The selective directing of traffic facilitates in
gradually testing the performance of the upgraded one or more instances under real-
10 world conditions without fully committing all traffic to the upgraded one or more
instances. Additionally, the processing unit [212] is further configured to direct
another fraction of the network traffic to other instances of the network functions
that have not been upgraded. This balanced approach ensures that the network can
continue to operate smoothly while the upgraded instances are being evaluated,
15 minimizing the risk of service disruptions or performance degradation.
For example, a telecommunications company is upgrading the software of its network routers (the network functions). The processing unit directs 30% of the network traffic to the routers with the upgraded software, while the remaining 70%
20 of the traffic continues to flow through the routers with the older software version.
This allows the company to monitor the performance of the upgraded routers under a controlled load, ensuring that they can handle the traffic without any issues before fully transitioning all traffic to the upgraded routers. If the upgraded routers perform well, the processing unit can gradually increase the percentage of traffic directed to
25 them until they handle all the network traffic. If issues arise, the processing unit can
redirect traffic back to the older routers while the problems are resolved.
The system [200] comprises the determination unit [214] communicatively coupled
to the monitoring unit [204]. The monitoring unit [204] is further configured to
30 monitor the one or more Key Performance Indicators (KPIs) of the upgraded one
or more instances of the one or more network functions (NFs) periodically after a predefined time period. Thereafter, the determination unit [214] is configured to determine whether the one or more KPIs of the upgraded one or more instances of the one or more NFs breaches the corresponding predicted set of thresholds. The
21

5 determination facilitates in assessing the success of the software upgrade and
deciding on the next steps. If the KPIs breaches the thresholds, indicating a potential degradation in performance, the determination unit [214] signals the generation unit [210] to generate a recommendation for rolling back the upgrade. Conversely, if the KPIs fails to breach the thresholds, indicating that the upgrade is performing
10 satisfactorily, the generation unit [210] is prompted to generate a recommendation
for fully implementing the upgrade and possibly increasing the network traffic directed to the upgraded instances. This process ensures that the upgrade decisions are based on real-time performance data, minimizing risks and optimizing network operations.
15
Referring to FIG. 3, an exemplary block diagram of a system architecture [300] for providing software upgrade recommendations is shown, in accordance with the exemplary implementations of the present disclosure. The system architecture [300] comprises a Service Communication Proxy (SCP) controller [302], an intelligent
20 module [304], a notification interface [306], the AMF [106], the SMF [108], the
SCP [110] and the PCF [122]. Also, all of the components/ units of the system architecture [300] are assumed to be connected to each other unless otherwise indicated below. Also, in FIG. 3 only a few units are shown, however, the system architecture [300] may comprise multiple such units or the system architecture
25 [300] may comprise any such numbers of said units, as required to implement the
features of the present disclosure.
In an embodiment, the intelligent module [304] is a distinct entity and may operate
independently. In other embodiments, the intelligent module [304] may be
30 integrated within the SCP controller [302], indicating a more centralized approach.
In an illustration of the present disclosure, one or more instances of the SCP [110] receive data from network functions including the Access and Mobility Management Function (AMF) [106], Session Management Function (SMF) [108],
22

5 and Policy Control Function (PCF) [122]. The SCP [110] is responsible for
managing the communication between these functions and the rest of the system. An administrator may utilize an Application Programming Interface (API) to convey relevant details of network functions, which may be upgraded to send it to the SCP [110]. Further, the SCP [110] may automatically fetch the details of a
10 network function, which may be upgraded form the NMS Platform, where the
details are already present in the NMS Platform. The SCP controller [302] identifies an upgrade release of one or more instances of one or more network functions (NFs). The SCP controller [302] receives a network behaviour data including but not limited to traffic data, fault data, statistics data, performance data and Key
15 Performance Indicators (KPIs) from the SCP [100]. The SCP controller [302] may
use the trained model, within the intelligent module [304], that is trained using historical network performance data to predict the performance thresholds for various Key Performance Indicators (KPIs) after a network function has been upgraded. The trained model may be Machine Learning as a Service (MLaaS). The
20 SCP controller [302] would collect current KPI data from the SCP [110] after the
upgrade. The SCP controller [302] would compare the real-time KPIs against the predicted thresholds. Based on the comparison, the notification interface [306] is used to alert administrators about the upgrade's performance, whether it's operating within the acceptable thresholds or if there's a deviation requiring attention.
25
If the current KPIs breaches the predicted thresholds, indicating potential performance issues, the system may recommend a rollback of the software upgrade. Conversely, if the KPIs fails to breach the thresholds, indicating a successful upgrade, the system may lead the SCP controller [302] to determine the routing
30 information of the upgraded network and recommend to the SCP [110] to gradually
shift the traffic data to the upgraded network functions. The SCP [110] further shifts the data traffic between the network functions. This phased approach is managed by the processing unit which directs a specified portion of the network traffic to the upgraded instances to closely monitor performance before a full-scale rollout.
23

5
In another illustration of the present disclosure, one or more instances of the SCP [110] receive data from network functions including the Access and Mobility Management Function (AMF) [106], Session Management Function (SMF) [108], and Policy Control Function (PCF) [122]. The intelligent module [304] is
10 responsible for managing the communication between these functions and the rest
of the system. An administrator may utilize an Application Programming Interface (API) to convey relevant details of network function which may be upgraded to send it to the SCP [110]. Further, the SCP [110] may automatically fetch the details of a network function which may be upgraded form the NMS Platform, where the
15 details are already present in the NMS Platform. The intelligent module [304]
identifies an upgrade release of one or more instances of one or more network functions (NFs). The intelligent module [304] receives network behaviour data including but not limited to traffic data, fault data, statistics data, performance data and Key Performance Indicators (KPIs) from the SCP [100]. The intelligent module
20 [304] includes a trained model that is trained using historical network performance
data. The trained model predicts the performance thresholds for various Key Performance Indicators (KPIs) after a network function has been upgraded. The trained model may be Machine Learning as a Service (MLaaS). The intelligent module [304] would collect current KPI data from the SCP [110] after the upgrade.
25 The intelligent module [304] would compare the real-time KPIs against the
predicted thresholds. Based on the comparison, the notification interface [306] is used to alert administrators about the upgrade's performance, whether it's operating within the acceptable thresholds or if there's a deviation requiring attention.
30 If the current KPIs breaches the predicted thresholds, indicating potential
performance issues, the system may recommend a rollback of the software upgrade. Conversely, if the KPIs fails to breach the thresholds, indicating a successful upgrade, the system may lead the intelligent module [304] to determine the routing information of the upgraded network and recommend to the SCP [110] to gradually
24

5 shift the data traffic to the upgraded network functions. The SCP [110] further shifts
the data traffic between the network functions. This phased approach is managed by the processing unit which directs a specified portion of the network traffic to the upgraded instances to closely monitor performance before a full-scale rollout.
10
In addition, the system [300] can fetch upgrade releases automatically from a Network Management System (NMS) Platform, which allows for the integration of updates directly into the network functions AMF [106], SMF [108], SCP [110], and PCF [122] as needed, further automating the upgrade process and ensuring that the
15 latest software is always in use.
Referring to FIG. 4, an exemplary method flow diagram [400], for providing
software upgrade recommendations, in accordance with exemplary embodiments
of the present disclosure is shown. In an implementation, the method [400] is
20 performed by the system [200], the system architecture [300], or the SCP controller
[302]. As shown in FIG. 4, the method [400] starts at step [402].
At step [404], the method [400] as disclosed by the present disclosure comprises identifying, by an identification unit [202] at the SCP controller [302], an upgrade
25 release of one or more instances of one or more network functions (NFs). The
upgrade release may include new features, bug fixes, or enhancements aimed at improving the performance or security of the network functions. The upgrade release may be automatically fetched from a Network Management System (NMS) Platform. This feature streamlines the upgrade process by eliminating the need for
30 manual intervention to obtain the latest software release. The NMS Platform serves
as a centralized repository for software releases, and the system [200] is configured to automatically retrieve the relevant upgrade package for the network functions being upgraded. The automation ensures that the system always has access to the
25

5 most current and compatible software versions, reducing the likelihood of errors
and compatibility issues during the upgrade process.
Next, at step [406], the method [400] as disclosed by the present disclosure comprises monitoring, by a monitoring unit [204] at the SCP controller [302], one
10 or more Key Performance Indicators (KPIs) of the upgraded one or more instances
of the one or more NFs. The one or more KPIs monitored by the monitoring unit [204] can include various metrics related to network performance, such as error code percentage, traffic load information, request timeout, and request failure rates. By monitoring the one or more KPIs, the monitoring unit [204] provides real-time
15 data on how the upgraded instances are performing in the network environment.
The monitoring facilitates in assessing the impact of the upgrade on the network and determining whether the upgrade has led to any degradation in performance. The continuous monitoring of the one or more KPIs by the monitoring unit [204] enables the system to make informed decisions about whether to proceed with the
20 full rollout of the upgrade, scale back the traffic directed to the upgraded instances,
or even roll back the upgrade if significant issues are detected.
Next, the method at step [408] comprises predicting, by a predicting unit [206] using an intelligent module [304], a set of thresholds corresponding to the one of
25 more KPIs of the upgraded one or more instances of the one or more NFs. The
intelligent module [304] includes a trained model utilizes historical data to predict the set of thresholds corresponding to one or more KPIs, which are then used to determine acceptable performance levels for the upgraded one or more instances of the one or more NFs. The predicting unit [206] facilitates in providing a foresight
30 into the expected performance of the upgraded one or more instances of the one or
more NFs, enabling proactive decision-making regarding the upgrade process. The trained model may be trained using historical data that includes parameters such as request timeout, response time, or a combination of both. The trained model may be Machine Learning as a Service (MLaaS). The historical data is utilized to train
26

5 the model to predict thresholds of the one or more KPIs. By analysing past
performance metrics, the intelligent module [304] can identify patterns and trends
that help in setting appropriate thresholds for evaluating the performance of
upgraded network functions. This data-driven approach enhances the accuracy of
the predictions, enabling more informed decisions during the software upgrade
10 process.
Next at step [410] the method comprises comparing, by a comparator [208] at the SCP controller [302], the monitored one or more KPIs with the corresponding predicted set of thresholds. The comparison facilitates in assessing whether the
15 performance of the upgraded network functions is within acceptable limits. If the
monitored one or more KPIs deviate significantly from the predicted corresponding thresholds, it may indicate potential issues with the upgrade. The comparator [208] facilitates in determining whether to continue with the upgrade rollout, increase the network traffic directed to the upgraded one or more instances of the one or more
20 NFs, or roll back the upgrade to prevent further degradation of network
performance.
Next, at step [412] of the method comprises generating, by a generation unit [210] at the SCP controller [302], at least a recommendation for performing at least one
25 of removing and implementing the upgrade release based at least on the
comparison. If the comparator [208] determines that the monitored one or more Key Performance Indicators (KPIs) of the upgraded one or more instances of the one or more NFs breaches the corresponding predicted set of thresholds, the generation unit [210] is configured to generate a recommendation for removing the upgrade
30 for the upgraded instances. Conversely, if the monitored one or more KPIs fails to
breach the set of predicted thresholds, the generation unit [210] is configured to generate a recommendation for implementing the upgrade for the upgraded one or more instances of the one or more NFs, which may include a gradual increase in network traffic directed to these instances. This process ensures that the decision to
27

5 proceed with or roll back the upgrade is data-driven, minimizing the risk of
degraded network performance and enhancing the overall efficiency of the upgrade process.
In an implementation of the present disclosure, the KPI is in error percent, and the
predicted set of thresholds is found to be 40%. The monitored error percent KPI
10 stands at 45%, the monitored KPI breaches the corresponding predicted set of
thresholds. Therefore, the generation unit [210] generates a recommendation to remove the upgrade.
In another implementation of the present disclosure, the KPI is in success percent,
and the predicted threshold is 55%. The monitored success percent is found to be
15 70 %. In this case, the monitored KPI fails to breach the set of predicted thresholds
and hence, the generation unit [210] generates a recommendation to implement the upgrade and gradually increase the network traffic.
20 The method [400] further includes directing, by the processing unit [212] at the SCP
controller [302] communicatively coupled to the generation unit [210], at least a fraction of network traffic to the upgraded one or more instances of the one or more network functions (NFs). The processing unit [212] is configured to selectively direct traffic to facilitate in gradually testing the performance of the upgraded one
25 or more instances under real-world conditions without fully committing all traffic
to the upgraded one or more instances. Additionally, the processing unit [212] is further configured to direct another fraction of the network traffic to other instances of the network functions that have not been upgraded. This balanced approach ensures that the network can continue to operate smoothly while the upgraded
30 instances are being evaluated, minimizing the risk of service disruptions or
performance degradation.
28

5 The method [400] comprises monitoring, by the monitoring unit [204] at the SCP
controller [302], the one or more Key Performance Indicators (KPIs) of the upgraded one or more instances of the one or more network functions (NFs) periodically after a predefined time period. Thereafter, the method [400] further comprises determining, by the determination unit [214] at the SCP controller [302]
10 whether the one or more KPIs of the upgraded one or more instances of the one or
more NFs breaches the corresponding predicted set of thresholds. The determination facilitates in assessing the success of the software upgrade and deciding on the next steps. If the KPIs breaches the thresholds, indicating a potential degradation in performance, the determination unit [214] signals the generation unit
15 [210] to generate a recommendation for rolling back the upgrade. Conversely, if the
KPIs fails to breach the thresholds, indicating that the upgrade is performing satisfactorily, the generation unit [210] is prompted to generate a recommendation for fully implementing the upgrade and possibly increasing the network traffic directed to the upgraded instances. This process ensures that the upgrade decisions
20 are based on real-time performance data, minimizing risks and optimizing network
operations.
Thereafter, the method terminates at step [414].
25 Therefore, the present method encompasses using an AI model that plays a crucial
role in roll out of new NF releases. During the upgrade procedures the AI model monitors the KPIs of the new release software. If it passes the criteria check, the old release is removed, and the system is upgraded to the new release. If the new release software fails, then it is removed and users such as developers are alerted
30 using the configured notification event.
It is pertinent to note that the present disclosure is not limited and restricted to the abovementioned operations/features and for various different use cases the solution

5 may be implemented in light of the present disclosure in a manner as obvious to a
person skilled in the art for execution of the present solution.
In an example, a mobile network provider that wants to roll out a new software update to its cell towers (network functions) to improve signal strength and data
10 speed. The system's identification unit automatically selects the correct software
version from the Network Management System (NMS) Platform. As the new software is deployed on a subset of towers, the monitoring unit tracks performance indicators like call drop rate and data throughput. Using past performance data, the predicting unit sets thresholds for acceptable post-upgrade performance. The
15 comparator then assesses the current performance against these benchmarks. If the
performance doesn't meet expectations, indicating potential issues with the update, the generation unit suggests halting the rollout and reverting to the old software. If the upgrade shows positive results, with KPIs meeting or exceeding the predicted thresholds, the mobile network provider is advised to incrementally redirect more
20 traffic to the updated towers, carefully managed by the processing unit. This phased
approach ensures stability and quality of service for users while the network is upgraded.
FIG. 5 illustrates an exemplary block diagram of a computer system [500] upon
25 which an embodiment of the present disclosure may be implemented. In an
implementation, the computing device implements the method for providing
software upgrade recommendations using the system [200]. In another
implementation, the computer system [500] itself implements the method for
providing software upgrade recommendations by using one or more units
30 configured within the computing device, wherein said one or more units are capable
of implementing the features as disclosed in the present disclosure.
The computer system [500] may include a bus [502] or other communication mechanism for communicating information, and a processor [504] coupled with bus

5 [502] for processing information. The processor [504] may be, for example, a
general-purpose microprocessor. The computer system [500] may also include a main memory [506], such as a random-access memory (RAM), or other dynamic storage device, coupled to the bus [502] for storing information and instructions to be executed by the processor [504]. The main memory [506] also may be used for
10 storing temporary variables or other intermediate information during execution of
the instructions to be executed by the processor [504]. Such instructions, when stored in non-transitory storage media accessible to the processor [504], render the computer system [500] into a special-purpose machine that is customized to perform the operations specified in the instructions. The computer system [500]
15 further includes a read only memory (ROM) [508] or other static storage device
coupled to the bus [502] for storing static information and instructions for the processor [504].
A storage device [510], such as a magnetic disk, optical disk, or solid-state drive is
20 provided and coupled to the bus [502] for storing information and instructions. The
computer system [500] may be coupled via the bus [502] to a display [512], such
as a cathode ray tube (CRT), for displaying information to a computer user. An
input device [514], including alphanumeric and other keys, may be coupled to the
bus [502] for communicating information and command selections to the processor
25 [504]. Another type of user input device may be a cursor control [516], such as a
mouse, a trackball, or cursor direction keys, for communicating direction
information and command selections to the processor [504], and for controlling
cursor movement on the display [512]. This input device typically has two degrees
of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allow
30 the device to specify positions in a plane.
The computer system [500] may implement the techniques described herein using customized hard-wired logic, one or more Application-Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs), firmware and/or program

5 logic which in combination with the computer system [500] causes or programs the
computer system [500] to be a special-purpose machine. According to one embodiment, the techniques herein are performed by the computer system [500] in response to the processor [504] executing one or more sequences of one or more instructions contained in the main memory [506]. Such instructions may be read
10 into the main memory [506] from another storage medium, such as the storage
device [510]. Execution of the sequences of instructions contained in the main memory [506] causes the processor [504] to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.
15
The computer system [500] also may include a communication interface [518] coupled to the bus [502]. The communication interface [518] provides a two-way data communication coupling to a network link [520] that is connected to a local network [522]. For example, the communication interface [518] may be an
20 integrated services digital network (ISDN) card, cable modem, satellite modem, or
a modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface [518] 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
25 implementation, the communication interface [518] sends and receives electrical,
electromagnetic, or optical signals that carry digital data streams representing various types of information.
The computer system [500] can send messages and receive data, including program
30 code, through the network(s), the network link [520] and the communication
interface 518. In the Internet example, a server [530] might transmit a requested code for an application program through the Internet [528], the Internet Service Provider (ISP) [526], the local network [522] and the communication interface [518]. The received code may be executed by the processor [504] as it is received,

5 and/or stored in the storage device [510], or other non-volatile storage for later
execution.
The present disclosure provides a non-transitory computer-readable storage medium storing instruction for providing software upgrade recommendations, the
10 storage medium comprising executable code which, when executed by one or more
units of a system, causes: an identification unit [202] to identify an upgrade release of one or more instances of one or more network functions (NFs); a monitoring unit [204] to monitor one or more Key Performance Indicators (KPIs) of the upgraded one or more instances of the one or more NFs; a predicting unit [206] to predict,
15 using an intelligent module [304], a set of thresholds corresponding to the one of
more KPIs of the upgraded one or more instances of the one or more NFs; a comparator [208] to compare the monitored one or more KPIs with the corresponding predicted set of thresholds; and a generation unit [210] to generate at least a recommendation for performing at least one of removing and
20 implementing the upgrade release based at least on the comparison.
The present disclosure provides an efficient and effective system and method for software upgrade recommendations based on real-time traffic analysis and predictive analytics. Unlike the traditional methods that rely on manual processes
25 and lack predictive capabilities, the disclosed system employs artificial intelligence
to predict key performance indicators (KPIs) thresholds, enabling proactive decision-making during the upgrade process. The present disclosure introduces a methodology where only a fraction of network traffic is directed to the upgraded instance initially, reducing the risk of immediate performance degradation. This
30 gradual approach allows for real-time monitoring of the upgraded instance's
performance against predicted KPI thresholds. If the performance is within acceptable limits, the system gradually increases the traffic load to the upgraded instance, ensuring a smooth transition without overwhelming the system. Furthermore, the present disclosure utilizes an intelligent module based on

5 historical data to predict the KPI thresholds, providing a more accurate and data-
driven approach to upgrade decision-making. This predictive capability enables network administrators to implement upgrades at optimal times, minimizing the risk of downtime and enhancing overall network performance. By automatically fetching upgrade releases from a Network Management System Platform(NMS
10 Platform), the system streamlines the upgrade process, reducing the potential for
human error and ensuring that upgrades are implemented efficiently. The system's ability to monitor and compare real-time KPIs with predicted thresholds ensures that upgrades are only fully implemented if they meet predefined performance criteria, thus reducing the likelihood of needing to roll back an upgrade due to
15 unexpected performance issues.
While considerable emphasis has been placed herein on the disclosed embodiments,
it will be appreciated that many embodiments can be made and that many changes
can be made to the embodiments without departing from the principles of the
20 present disclosure. These and other changes in the embodiments 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.

5 We Claim:
1. A method for providing software upgrade recommendations, the method
comprising:
identifying, by an identification unit [202] at a Service Communication
Proxy (SCP) controller [302], an upgrade release of one or more instances
10 of one or more network functions (NFs);
monitoring, by a monitoring unit [204] at the SCP controller [302], one or more Key Performance Indicators (KPIs) of the upgraded one or more instances of the one or more NFs;
predicting, by a predicting unit [206] at the SCP controller [302] using
15 an intelligent module [304], a set of thresholds corresponding to the one of
more KPIs of the upgraded one or more instances of the one or more NFs;
comparing, by a comparator [208] at the SCP controller [302], the
monitored one or more KPIs with the corresponding predicted set of
thresholds; and
20 generating, by a generation unit [210] at the SCP controller [302], at
least a recommendation for performing at least one of removing and implementing the upgrade release based at least on the comparison.
2. The method as claimed in claim 1, wherein the method comprises directing,
25 by a processing unit [212] at the SCP controller [302], at least a fraction of
network traffic to the upgraded one or more instances of the one or more NFs.
3. The method as claimed in claim 2, wherein the method comprises directing,
30 by the processing unit [212] at the SCP controller [302], at least other
fraction of the network traffic to other one or more instances of other one or more NFs.

5 4. The method as claimed in claim 2, wherein the method comprises
monitoring, by the monitoring unit [204] at the SCP controller [302], the one or more KPIs of the upgraded one or more instances of the one or more NFs periodically after a predefined time period.
10 5. The method as claimed in claim 4, wherein the method comprises:
determining, by a determination unit [214] at the SCP controller [302], whether the one of more KPIs of the upgraded one or more instances of the one or more NFs breaches or fails to breach the corresponding predicted set of thresholds.
15
6. The method as claimed in claim 5, wherein upon determining that the one
or more KPIs of the upgraded one or more instances of the one or more NFs
breaches the corresponding predicted set of thresholds, generating, by the
generation unit [210], at least the recommendation for removing the upgrade
20 for the upgraded one or more instances of the one or more NFs.
7. The method as claimed in claim 5, wherein upon determining that the one
or more KPIs of the upgraded one or more instances of the one or more NFs
fails to breach the corresponding predicted set of thresholds, generating, by
25 the generation unit [210] at the SCP controller [302], at least the
recommendation for implementing the upgrade for the upgraded one or more instances of the one or more NFs corresponding to gradual increase in the network traffic for the upgraded one or more instances of the one or more NFs.
30
8. The method as claimed in claim 1, wherein the one or more KPIs comprises
at least one of error code percentage KPI, traffic load information KPI,
request timeout KPI, and request failure KPI.

5 9. The method as claimed in claim 1, wherein the intelligent module [304]
comprises a trained model, the trained model is trained based on historical data, wherein the historical data comprises parameters comprising at least one of request timeout, response time, and combination thereof.
10 10. The method as claimed in claim 1, wherein the upgrade release is
automatically fetched from a Network Management System (NMS) Platform.
11. A system for providing software upgrade recommendations, the system
15 comprising:
a Service Communication Proxy (SCP) controller [302] comprising:
an identification unit [202] configured to identify an upgrade release of one or more instances of one or more network functions (NFs);
a monitoring unit [204] configured to monitor one or more Key
20 Performance Indicators (KPIs) of the upgraded one or more instances of the
one or more NFs;
a predicting unit [206] configured to predict, using an intelligent
module [304], a set of thresholds corresponding to the one of more KPIs of
the upgraded one or more instances of the one or more NFs;
25 a comparator [208] configured to compare the monitored one or more
KPIs with the corresponding predicted set of thresholds; and
a generation unit [210] configured to generate at least a recommendation for performing at least one of removing and implementing the upgrade release based at least on the comparison. 30
12. The system as claimed in claim 11, wherein the system comprises a
processing unit [212] at the SCP controller [302]configured to direct at least
a fraction of network traffic to the upgraded one or more instances of the
one or more NFs.
35
37

5 13. The system as claimed in claim 12, wherein the processing unit [212] at the
SCP controller [302] is further configured to direct at least other fraction of the network traffic to other one or more instances of other one or more NFs.
14. 10
The system as claimed in claim 12, wherein the monitoring unit [204] at the SCP controller [302] is further configured to monitor the one or more KPIs of the upgraded one or more instances of the one or more NFs periodically after a predefined time period.
15. The system as claimed in claim 14, wherein the system comprises:
15 a determination unit [214] at the SCP controller [302] configured to
determine whether the one of more KPIs of the upgraded one or more instances of the one or more NFs breaches the corresponding predicted set of thresholds.
20 16. The system as claimed in claim 15, wherein upon determining that the one
or more KPIs of the upgraded one or more instances of the one or more NFs breaches the corresponding predicted set of thresholds, the generation unit [210] at the SCP controller [302] is further configured to generate at least the recommendation for removing the upgrade for the upgraded one or more
25 instances of the one or more NFs.
17. The system as claimed in claim 15, wherein upon determining that the one
or more KPIs of the upgraded one or more instances of the one or more NFs
fails to breach the corresponding predicted set of thresholds, the generation
30 unit [210] is configured to generate at least the recommendation for
implementing the upgrade for the upgraded one or more instances of the one or more NFs corresponding to gradual increase in the network traffic for the upgraded one or more instances of the one or more NFs.

5 18. The system as claimed in claim 11, wherein the one or more KPIs comprises
at least one of error code percentage KPI, traffic load information KPI, request timeout KPI, and request failure KPI.
19. The system as claimed in claim 11, wherein the intelligent module [304]
10 comprises a trained model, the trained model is trained based on historical
data, wherein the historical data comprises parameters comprising at least one of request timeout, response time, and combination thereof.
20. The system as claimed in claim 11, wherein the upgrade release is
15 automatically fetched from a Network Management System (NMS)
Platform.
Dated this 4th day of July 2023
(GARIMA SAHNEY)
20 IN/PA-1826
AGENT FOR THE APPLICANT(S) OF SAIKRISHNA & ASSOCIATES