EP-4740407-A1 - METHOD AND SYSTEM FOR DYNAMICALLY DISTRIBUTING TRAFFIC TO A PLURALITY OF INSTANCES OF NETWORK FUNCTION

Abstract

The present disclosure relates to a method and system for dynamically distributing traffic to a plurality of instances of a Network Function (NF) The disclosure encompasses: determining, by a determination unit [302], a first capacity and a first load for the plurality of the NF instances using a trained model; fetching, by a fetching unit [304], a second capacity and a second load for the plurality of the NF instances in real time; comparing, by a comparator unit [306], the first capacity and the first load with the second capacity and the second load; determining, by the determination unit [302], a delta upon comparing the first capacity and the second capacity and, the first load and the second load, wherein the delta comprises computed relative weights; and updating, by an updating unit [308], the delta at the plurality of the NF instances.

Inventors

• BISHT, SANDEEP
• SINHA, ANURAG
• PANDEY, PRASHANT
• YADAV, RAVINDRA
• BHATNAGAR, AAYUSH
• BHATNAGAR, PRADEEP KUMAR
• JAIN, Abhiman
• Ansari, Ezaj
• SONKAR, Lakhichandra
• KUMAR, ANUJ

Assignees

• Jio Platforms Limited

Dates

Publication Date

20260513

Application Date

20240619

Claims (17)

1. 1. A method for dynamically distributing traffic to a plurality of instances of a Network Function (NF), said method comprising the steps of: determining, by a determination unit [302], a first capacity and a first load for the plurality of the NF instances using a trained model; fetching, by a fetching unit [304] a second capacity and a second load for the plurality of the NF instances in real time; comparing, by a comparator unit [306], the first capacity and the first load with the second capacity and the second load; determining, by the determination unit [302], a delta upon comparing the first capacity and the second capacity and, the first load and the second load, wherein the delta comprises computed relative weights; and updating, by an updating unit [308], the delta at the plurality of instances of the NF.
2. 2. The method as claimed in claim 1, wherein the first capacity and the first load comprise information determined based on a plurality of compute parameters.
3. 3. The method as claimed in claim 2, wherein the plurality of compute parameters is fetched from at least one of the plurality of NF instances.
4. 4. The method as claimed in claim 1, wherein the second capacity and the second load comprise information fetched from a repository.
5. 5. The method as claimed in claim 2, wherein the plurality of compute parameters comprises at least one of CPU usage, a memory usage, or network bandwidth.
6. 6. The method as claimed in claim 1, wherein the updating, by the updating unit [308], the delta at the plurality of the NF instances facilitates network traffic management and workload distribution.
7. 7. The method as claimed in claim 1, wherein the trained model is trained on historical data comprising past compute resource utilization trends, historical load distribution patterns, and prior network traffic behaviours of the plurality of instances of Network Function (NF).
8. 8. The method as claimed in claim 1, wherein the trained model processes the first capacity, the first load, the second capacity and the second load to identify patterns or trends in compute resource utilization.
9. 9. A system for dynamically distributing traffic to a plurality of instances of a Network Function (NF), said system comprising: a determination unit [302] configured to determine, a first capacity and a first load for the plurality of the NF instances using a trained model; a fetching unit [304] configured to fetch, a second capacity and a second load for the plurality of the NF instances in real time; a comparator unit [306] configured to compare, the first capacity and the first load with the second capacity and the second load; the determination unit [302] configured to determine, a delta upon comparing the first capacity and the second capacity and, the first load and the second load, wherein the delta comprises computed relative weights; and an updating unit [308] configured to update, the delta at the plurality of the NF instances.
10. 10. The system as claimed in claim 9, wherein the first capacity and the first load comprise information determined based on a plurality of compute parameters.
11. 11. The system as claimed in claim 10, wherein the plurality of compute parameters is fetched from at least one of the plurality of the NF instances.
12. 12. The system as claimed in claim 9, wherein the second capacity and the second load comprise information fetched from a repository.
13. 13. The system as claimed in claim 10, wherein the plurality of compute parameters comprises at least one of CPU usage, a memory usage, or network bandwidth.
14. 14. The system as claimed in claim 9, wherein the updating unit [308] updates the delta at the plurality of the NF instances to facilitate network traffic management and workload distribution.
15. 15. The system as claimed in claim 9, wherein the trained model is trained on historical data comprising past compute resource utilization trends, historical load distribution patterns, and prior network traffic behaviours of the plurality of instances of Network Function (NF).
16. 16. The system as claimed in claim 9, wherein the trained model processes the first capacity, the first load, the second capacity and the second load to identify patterns or trends in compute resource utilization.
17. 17. A non-transitory computer readable storage medium storing instructions for dynamically distributing traffic to a plurality of instances of a Network Function (NF), the instructions include executable code which, when executed by a one or more units of a system, causes: a determination unit [302] of the system to determine, a first capacity and a first load for the plurality of the NF instances using a trained model; a fetching unit [304] of the system to fetch, a second capacity and a second load for the plurality of the NF instances in real time; a comparator unit [306] of the system to compare, the first capacity and the first load with the second capacity and the second load; the determination unit [302] of the system to determine, a delta upon comparing the first capacity and the second capacity and, the first load and the second load, wherein the delta comprises computed relative weights; and an updating unit [308] of the system to update, the delta at the plurality of the NF instances.

Description

METHOD AND SYSTEM FOR DYNAMICALLY DISTRIBUTING TRAFFIC TO A PLURALITY OF INSTANCES OF NETWORK FUNCTION FIELD OF THE INVENTION [0001] The present disclosure relates generally to the field of wireless communication systems. In particular, the present disclosure relates to load balancing and capacity management of network functions. More particularly, the present disclosure relates to a method and system for dynamically distributing traffic to a plurality of instances of a network function (NF). 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. The third- generation (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] For existing solutions, one significant problem with the prior art is that some network functions (NFs) do not support capacity and current load reporting. This limitation makes it difficult to monitor, manage, and balance the load effectively, which can lead to network congestion and possible service degradation. Another problem with existing systems is that compute utilization at the same load can vary significantly between different NF instances. This inconsistency can lead to inefficiencies in resource allocation, as well as potential network function overloads and service disruptions. Existing systems often lack real-time monitoring of NF compute statistics. This lack of timely information makes it difficult to avoid potential failures due to high resource utilization, which can degrade network key performance indicators (KPIs). Further, the prior art relies on the NF's ability to support capacity and current load in registration or heartbeat requests. If an NF does not have this functionality, the system's ability to manage resources effectively is severely compromised. [0005] These issues contribute to inefficient use of network resources, potential service disruptions, and overall instability in the network's performance. [0006] Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks. [0007] Thus, there exists an imperative need in the art to provide a method and system for dynamically distributing traffic to a plurality of instances of a network function (NF). The proposed invention seeks to address these shortcomings by providing a more dynamic, efficient, and robust solution for managing network function resources. OBJECTS OF THE DISCLOSURE [0008] Some of the objects of the present disclosure, which at least one embodiment disclosed herein satisfies are listed herein below. [0009] It is an object of the present disclosure to provide a method and system for dynamically distributing traffic to a plurality of instances of a network function (NF). [0010] It is another object of the present disclosure to provide a method and system for distributing traffic to a plurality of instances of a network function (NF) that ensures efficient allocation and utilization of network function (NF) resources. By fetching the allocated compute resources and the current utilization of these resources from registered NF instances, the invention seeks to optimize the distribution of network traffic across multiple NF instances. [0011] It is yet another object of the present disclosure to provide a method and system for distributing traffic to a plurality of instances of a network function (NF) that enables real-time monitoring of NF compute statistics. This helps in proactive avoidance of potential failures due to high resource utilization, thus enhancing network performance and stability. [0012] It is yet another object of the present disclosure to provide a method and system for dist