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EP-4740597-A1 - METHOD AND SYSTEM FOR ROUTING TRAFFIC DATA

EP4740597A1EP 4740597 A1EP4740597 A1EP 4740597A1EP-4740597-A1

Abstract

The present disclosure relates to method and system for routing traffic data. The disclosure encompasses: receiving a current load data of each of a plurality of network nodes; analysing the current load data using a trained model; predicting using the trained model, a load threshold value for each of the plurality of network nodes based on the analysis of a set of data associated with plurality of network nodes; comparing the current load data with the corresponding load threshold value; identifying one or more first network nodes with one or more overload conditions when the current load data of the one or more first network nodes exceed the corresponding load threshold value; alerting a Network Management System (NMS) associated with the overload condition(s) at the first network node(s); and routing the current load data of the first network node(s) to second network node(s).

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 [400] for routing traffic data, the method [400] comprising: receiving, by a receiving unit [202] , a current load data of each of a plurality of network nodes; analysing, by a processing unit [204], the current load data using a trained model; predicting, by the processing unit [204] using the trained model, a load threshold value for each of the plurality of network nodes based on the analysis of a set of data associated with the plurality of network nodes; comparing, by the processing unit [204], the current load data with the corresponding load threshold value of each of the plurality of network nodes; identifying, by an identification unit [206], one or more first network nodes with one or more overload conditions when the current load data of the one or more first network nodes exceed the corresponding load threshold value; alerting, by the processing unit [204], a Network Management System (NMS) associated with the one or more overload conditions at the one or more first network nodes; and routing, by the processing unit [204], the current load data of the one or more first network nodes to one or more second network nodes.
  2. 2. The method [400] 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 [400] as claimed in claim 1, wherein the trained model is a machine learning (ML) based model.
  4. 4. The method [400] as claimed in claim 1, wherein the set of data associated with the plurality of network nodes comprises at least one of: an information associated with an increase of traffic and a decrease of traffic at the plurality of network nodes, an information associated with a peak traffic data and a low traffic data at the plurality of network nodes in a past, a historical trend of traffic at the plurality of network nodes, and a reason and a cause of the increase of traffic and the decrease of traffic at the plurality of network nodes.
  5. 5. The method [400] as claimed in claim 1, wherein the routing, by the processing unit [204], the current load data of the one or more first network nodes to the one or more second network nodes is done through one of a manual consent and an automatic consent.
  6. 6. The method [400] as claimed in claim 1, wherein the routing the current load data of the one or more first network nodes to the one or more second network nodes, comprise the steps of: removing, by the processing unit [204], a set of data from the one or more first network nodes, the set of data comprises at least one of a Network Function (NF) type, a supported Public Land Mobile Network (PLMN), and a supported slice; and registering, by the processing unit [204], the set of data, via a Network Repository Function (NRF), at the one or more second network nodes.
  7. 7. The method [400] as claimed in claim 1, wherein the one or more second network nodes are one or more network nodes with an available bandwidth to handle the routed current load data of the one or more first network nodes.
  8. 8. A system [200] for routing traffic data, the system [200] comprising: a receiving unit [202], configured to receive a current load data of each of a plurality of network nodes; a processing unit [204] connected at least to the receiving unit [202], the processing unit [204] is configured to: analyse the current load data using a trained model, predict, using the trained model, a load threshold value for each of the plurality of network nodes based on the analysis of a set of data associated with the plurality of network nodes, and compare the current load data with the corresponding load threshold value of each of the plurality of network nodes; and an identification unit [206] connected at least to the processing unit [204], the identification unit [206] is configured to identify one or more first network nodes with one or more overload conditions when the current load data of the one or more first network nodes exceed the corresponding load threshold value, wherein processing unit [204] is further configured to: alert, a Network Management System (NMS) associated with the one or more overload conditions at the one or more first network nodes, and route, the current load data of the one or more first network nodes to one or more second network nodes.
  9. 9. The system [200] 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 [200] as claimed in claim 8, wherein the trained model is a machine learning (ML) based model.
  11. 11. The system [200] as claimed in claim 8, wherein the set of data associated with the plurality of network nodes comprises at least one of: an information associated with an increase of traffic and a decrease of traffic at the plurality of network nodes, an information associated with a peak traffic data and a low traffic data at the plurality of network nodes in a past, a historical trend of traffic at the plurality of network nodes, and a reason and a cause of the increase of traffic and the decrease of traffic at the plurality of network nodes.
  12. 12. The system [200] as claimed in claim 8, wherein the processing unit [204] is configured to route the current load data of the one or more first network nodes to the one or more second network nodes through one of a manual consent and an automatic consent.
  13. 13. The system [200] as claimed in claim 8, wherein to route the current load data from the one or more first network nodes to the one or more second network nodes, the processing unit [204] is configured to: remove, a set of data from the one or more first network nodes, the set of data comprises at least one of a Network Function (NF) type, a supported Public Land Mobile Network (PLMN), and a supported slice, and register, the set of data, via the NRF, at the one or more second network nodes.
  14. 14. The system [200] as claimed in claim 8, wherein the one or more second network nodes are one or more network nodes with an available bandwidth to handle the routed current load data of the one or more first network nodes.
  15. 15. A non-transitory computer readable storage medium storing instructions for routing traffic data, the instructions including executable code which, when executed by a one or more units of a system, causes: a receiving unit [202] to receive a current load data of each of a plurality of network nodes; a processing unit [204] to: analyse the current load data using a trained model, predict, using the trained model, a load threshold value for each of the plurality of network nodes based on the analysis of a set of data associated with the plurality of network nodes, and compare the current load data with the corresponding load threshold value of each of the plurality of network nodes; and an identification unit [206] to identify one or more first network nodes with one or more overload conditions when the current load data of the one or more first network nodes exceed the corresponding load threshold value; and further the processing unit [204] to: alert, a Network Management System (NMS) associated with the one or more overload conditions at the one or more first network nodes, and route, the current load data of the one or more first network nodes to one or more second network nodes.

Description

METHOD AND SYSTEM FOR ROUTING TRAFFIC DATA 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 routing traffic data for traffic assessment and traffic optimization in a cellular communication. 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. 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] A 5G cellular communication system involves traffic load at Service Communication Proxies (SCPs) and in case of occurrence of traffic load, communication and data usage issues occur to the users. This overload of traffic is needed to be assessed and distributed by the SCPs to its one or more proxies. This overload is conventionally monitored by monitoring data Traffic Load or Transaction per Second (TPS) data at the one or more proxies of the SCPs. Conventionally, a TPS data report is generated which is transmitted to authorized personnels. Based on the TPS data report, the traffic is distributed to the one or more proxies to allow a smooth functioning of the cellular connection system. [0005] Therefore, the existing solutions for real-time traffic assessment and traffic optimization in a cellular communication have several shortcomings such as manual monitoring of the TPS pattern, manual identification of the overload of the traffic, and/or manual identification of the one or more proxies that are time-consuming and error prone processes. Further, in the existing solutions, a report of the TPS pattern for the one or more proxies is received after a substantial period of time, which leads to communication and data usage issues to users during said period. [0006] Further, over the period of time various solutions have been developed to improve the performance of communication devices and for traffic optimization in a cellular communication. However, there are certain challenges with existing solutions. Every network node such as a proxy of an SCP has a pre-defined threshold value of handling TPS to which the proxy can handle traffic without causing any communication and data usage issues to users. An increase of the TPS value of the proxy beyond the threshold value leads to communication and data usage issues to users due to an overload of traffic. In such cases, it is required to handle the traffic without causing communication and data usage issues to the users. The existing solutions involve monitoring the TPS pattern manually and dividing the traffic to the one or more proxies manually by identifying the overload of traffic and identifying manually the one or more proxies available to handle such overload of traffic. The manual monitoring of the TPS pattern, manual identification of the overload of the traffic, and manual identification of the one or more proxies is a time-consuming and error prone process. Further, a report of the TPS pattern for a particular proxy is received after a substantial period of time, which leads to communication and data usage issues, Key Performance Indicator (KPI) degradation to the users during said period. [0007] Thus, there exists an imperative need in the art to overcome the limitations of the existing solutions and to provide a method and system for routing traffic data in an effective and efficient manner. OBJECTS OF THE DISCLOSURE [0008] Some of the objects of the present disclosure, which at least one implementation disclosed herein satisfies are listed herein below. [0009] It is an object of the present disclosure to provide a system and a meth