EP-4740391-A1 - METHOD AND SYSTEM FOR SCALING UP NETWORK NODES

Abstract

The present disclosure relates to a method and a system for scaling up network nodes. The disclosure encompasses: receiving, by a receiving unit [102], a current load data associated with each of a plurality of network nodes; predicting, by a processing unit [108] via a trained model [206], a load threshold value for each of the plurality of network nodes; comparing, by a comparing unit [104], the current load data with the corresponding load threshold value of each of the plurality of network nodes to forecast overload conditions at the plurality of network nodes; and alerting, by an alerting unit [106], a Network Management System (NMS) to scale up the network nodes in an event the current load data breaches the corresponding load threshold value of the plurality of network nodes.

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 (15)

1. 1. A method for scaling up network nodes, the method comprising: receiving, by a receiving unit [102], a current load data associated with each of a plurality of network nodes; predicting, by a processing unit [108] using a trained model [206], a load threshold value for each of the plurality of network nodes; comparing, by a comparing unit [104], the current load data with the corresponding load threshold value of each of the plurality of network nodes to forecast overload conditions at the plurality of network nodes; and alerting, by an alerting unit [106], a Network Management System (NMS) to scale up the network nodes in an event the current load data breaches the corresponding load threshold value of the plurality of network nodes.
2. 2. The method as claimed in claim 1, wherein each of the plurality of network nodes is a Service Communication Proxy (SCP) of a 5 th Generation (5G) network.
3. 3. The method as claimed in claim 1, wherein the trained model [206] is trained based on of a historical set of data associated with the plurality of network nodes, the historical set of data comprises past traffic load patterns, traffic distribution trends, peak traffic times, and historical overload events at the plurality of network nodes.
4. 4. The method as claimed in claim 1, wherein the trained model [206] is an artificial intelligence (Al) based model.
5. 5. The method as claimed in claim 1, wherein the current load data associated with the plurality of network nodes comprises information indicative of increase and decrease of traffic at the plurality of network nodes, information indicative of peak traffic data and low traffic data at the plurality of network nodes in past, historical trend of traffic at the plurality of network nodes, reason and causes of increase and decrease of traffic at the plurality of network nodes.
6. 6. The method as claimed in claim 1, further comprises notifying, by the processing unit [108], network node scale-up data to the NMS, wherein the network node scale-up data comprises site details, network function (NF) type details, number of required network nodes.
7. 7. The method as claimed in claim 6, wherein the scale-up corresponds to addition of at least one SCP node in the 5G network.
8. 8. A system for scaling up network nodes, said system comprising: a receiving unit [102] configured to receive a current load data associated with each of a plurality of network nodes; a processing unit [108] configured to predict, viaatrained model [206], a load threshold value for each of the plurality of network nodes; a comparing unit [104] configured to compare the current load data with the corresponding load threshold value of each of the plurality of network nodes to forecast overload conditions at the plurality of network nodes; and an alerting unit [106] configured to alert a Network Management System (NMS) to scale up the network nodes in an event the current load data breaches the corresponding load threshold value of the plurality of network nodes.
9. 9. The system as claimed in claim 8, wherein each of the plurality of network nodes is a Service Communication Proxy (SCP) of a 5 th Generation (5G) network.
10. 10. The system as claimed in claim 8, wherein the trained model [206] is trained based on of a historical set of data associated with the plurality of network nodes, the historical set of data comprises past traffic load patterns, traffic distribution trends, peak traffic times, and historical overload events at the plurality of network nodes.
11. 11. The system as claimed in claim 8, wherein the trained model [206] is an artificial intelligence (Al) based model.
12. 12. The system as claimed in claim 8, wherein the current load data associated with the plurality of network nodes comprises information indicative of increase and decrease of traffic at the plurality of network nodes, information indicative of peak traffic data and low traffic data at the plurality of network nodes in past, historical trend of traffic at the plurality of network nodes, reason and causes of increase and decrease of traffic at the plurality of network nodes.
13. 13. The system as claimed in claim 8, wherein the processing unit [108] is configured to notify network node scale-up data to the NMS, wherein the network node scale-up data comprises site details, network function (NF) type details, number of required network nodes.
14. 14. The system as claimed in claim 13, wherein the network node scale-up data corresponds to addition of at least one SCP node in the 5G network.
15. 15. A non-transitory computer-readable storage medium storing instruction for scaling up network nodes, the storage medium comprising executable code which, when executed by one or more units of a system, causes: a receiving unit [102] to receive a current load data associated with each of a plurality of network nodes; a processing unit [108] to predict, via a trained model [206], a load threshold value for each of the plurality of network nodes; a comparing unit [104] to compare the current load data with the corresponding load threshold value of each of the plurality of network nodes to forecast overload conditions at the plurality of network nodes; and an alerting unit [106] to alert a Network Management System (NMS) to scale up the network nodes in an event the current load data breaches the corresponding load threshold value of the plurality of network nodes.

Description

METHOD AND SYSTEM FOR SCALING UP NETWORK NODES FIELD OF THE DISCLOSURE [0001] The present disclosure relates generally to the field of wireless communication systems. More particularly, the present disclosure relates to methods and systems for scaling up network nodes to handle overload conditions. BACKGROUND [0002] 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. [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] In the prior art, managing network scalability in 5G networks, particularly for Service Communication Proxies (SCPs), presents several challenges. As the number of 5G subscribers increases or the service patterns of existing subscribers change, SCP proxies may begin to experience higher traffic loads. Initially, traffic distribution optimizations are performed to ensure that the load is evenly distributed across SCP proxies. However, there may come a point where all proxies are operating at maximum capacity, leading to potential service degradation and impacting user experience. A significant problem in the existing techniques is the lack of predictive mechanisms to anticipate and manage these overload conditions. Traditional methods rely on reactive approaches, where scaling decisions are made only after the network is already experiencing congestion. This can lead to delays in scaling up the network infrastructure, resulting in reduced service quality and potential downtime. Furthermore, the prior art lacks an intelligent system that can provide recommendations for scaling out SCP proxies, taking into account factors such as the optimal site for deployment and the types of Network Functions (NFs) that should be supported by the new proxies. The absence of a proactive and intelligent scaling approach limits the efficiency and effectiveness of network management in 5G systems. [0005] Thus, in order to improve radio access network capacity and performance, there exists an imperative need in the art to provide methods and systems for scaling up network nodes that efficiently manage the overload conditions at the network. OBJECTS OF THE PRESENT DISCLOSURE [0006] Some of the objects of the present disclosure, which at least one implementation disclosed herein satisfies are listed herein below. [0007] It is an object of the present disclosure to provide a system and method for scaling up network nodes. [0008] It is another object of the present disclosure to provide a system and method for scaling up network nodes that proactively manages network load by predicting future overload conditions using historical data trends. [0009] It is another object of the present disclosure to provide a system and method for scaling up network nodes that utilize Artificial Intelligence (Al) and Machine Learning (ML) to notify network administrators of the need to scale out before reaching critical load levels, ensuring uninterrupted service quality. [0010] It is another object of the present disclosure to provide a system and method for scaling up network nodes that offer consent-based scale-out decisions, allowing for more controlled and deliberate expansion of network resources. [0011] It is another object of the present disclosure to provide a system and method for scaling up network nodes that generate specific site and Network Function (NF) type recommendations for the new scale-out SCP Proxies, optimizing resource distribution and efficiency. [0012] It is another object of the present disclosure to provide a system and method for scaling up network nodes that enable a seamless and dynamic adaptation of the network infrastructure in response to the evolving de