EP-4740394-A1 - METHOD AND SYSTEM FOR PROVIDING SOFTWARE UPGRADE RECOMMENDATIONS

Abstract

The present disclosure relates to a method and a system for providing software upgrade recommendations The disclosure encompasses: identifying, by an identification unit [202], an upgrade release of one or more instances of one or more network functions (NFs); monitoring, by a monitoring unit [204], 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] 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; comparing, by a comparator [208], the monitored one or more KPIs with the corresponding predicted set of thresholds; and generating, by a generation unit [210], at least a recommendation for performing at least one of removing and implementing the upgrade release based at least on the comparison.

Inventors

• BISHT, SANDEEP
• BHATNAGAR, AAYUSH
• SINHA, ANURAG
• Ansari, Ezaj
• YADAV, RAVINDRA
• PANDEY, PRASHANT

Assignees

• Jio Platforms Limited

Dates

Publication Date

20260513

Application Date

20240611

Claims (20)

1. 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 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 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 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. 2. The method as claimed in claim 1, wherein the method comprises directing, 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. 3. The method as claimed in claim 2, wherein the method comprises directing, 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.
4. 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.
5. 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.
6. 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 for the upgraded one or more instances of the one or more NFs.
7. 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 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.
8. 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.
9. 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. 11. A system for providing software upgrade recommendations, the system 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 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; 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.
12. 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.
13. 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. 14. 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. 15. The system as claimed in claim 14, wherein the system comprises: 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.
16. 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 instances of the one or more NFs.
17. 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 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.
18. 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. 19. The system as claimed in claim 11, 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.
20. 20. The system as claimed in claim 11, wherein the upgrade release is automatically fetched from a Network Management System (NMS) Platform.

Description

METHOD AND SYSTEM FOR PROVIDING SOFTWARE UPGRADE RECOMMENDATIONS TECHNICAL FIELD The present disclosure relates generally to the field of wireless communication 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 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. 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. Existing solutions for network software upgrades often rely on manual processes and lack realtime 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 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 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 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 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 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. 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 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 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 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 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 s