WO2025008853A1

WO2025008853A1 - Method and system for routing traffic data

Info

Publication number: WO2025008853A1
Application number: PCT/IN2024/050722
Authority: WO
Inventors: Sandeep Bisht; Aayush Bhatnagar; Anurag Sinha; Ezaj Ansari; Ravindra Yadav; Prashant Pandey
Original assignee: Jio Platforms Ltd
Current assignee: Jio Platforms Ltd
Priority date: 2023-07-03
Filing date: 2024-06-11
Publication date: 2025-01-09
Anticipated expiration: 2026-01-03

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).

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 method for routing traffic data.

[00010] It is also an object of the present disclosure to provide a trained model for real-time traffic optimization in a cellular communication.

[00011] Yet another object of the present disclosure is to monitor traffic on one or more network nodes in real-time and to provide an increment in an effective distribution of the traffic.

SUMMARY OF THE DISCLOSURE

[00012] This section is provided to introduce certain implementations 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.

[00013] A first aspect of the present disclosure is related to a method for routing traffic data. The method encompasses receiving, by a receiving unit, a current load data of each of a plurality of network nodes. Further, the method encompasses analysing, by a processing unit, the current load data using a trained model. Furthermore, the method encompasses predicting, by the processing unit, 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. Furthermore, the method encompasses comparing, by the processing unit, the current load data with the corresponding load threshold value of each of the plurality of network nodes. Thereafter, the method encompasses identifying by an identification unit 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. Furthermore, the method encompasses alerting, by the processing unit, a Network Management System (NMS) associated with the one or more overload conditions at the one or more first network nodes. Lastly, the method encompasses routing, by the processing unit, the current load data of the one or more first network nodes to one or more second network nodes. [00014] As per another aspect of the present disclosure, each of the plurality of network nodes is a Service Communication Proxy (SCP) of a 5th Generation (5G) network.

[00015] As per another aspect of the present disclosure, the trained model is a machine learning

(ML) based model.

[00016] As per another aspect of the present disclosure, 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.

[00017] As per another aspect of the present disclosure, the routing, by the processing unit, 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.

[00018] As per another aspect of the present disclosure, the routing the current load data of the one or more first network nodes to the one or more second network nodes comprises the steps of: removing, by the processing unit, a set of data from the one or more 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, the set of data via a Network Repository Function (NRF), at the one or more second network nodes.

[00019] As per another aspect of the present disclosure, 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.

[00020] Another aspect of the present disclosure is related to a system for routing traffic data. The system comprises a receiving unit, an identification unit, and a processing unit connected to each other, wherein the receiving unit is configured to receive a current load data of each of a plurality of network nodes. Further, the processing unit is configured to analyse the current load data using a trained model. Furthermore, the processing unit is configured to 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. Furthermore, the processing unit is configured to compare the current load data with the corresponding load threshold value of each of the plurality of network nodes. Furthermore, the identification unit 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. Furthermore, the processing unit is configured to alert a Network Management System (NMS) associated with the one or more overload conditions at the one or more first network nodes. Furthermore, the processing unit is configured to route the current load data of the one or more first network nodes to one or more second network nodes.

[00021] Further, an aspect of the present disclosure relates to a non-transitory computer readable storage medium storing instructions for routing traffic data. The instructions include executable code which, when executed by one or more units of a system, causes: a receiving unit of the system to receive a current load data of each of a plurality of network nodes; the processing unit of the system to: 1) analyse the current load data using a trained model, 2) 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 3) compare the current load data with the corresponding load threshold value of each of the plurality of network nodes; the identification unit of the system 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 the processing unit of the system to: 1) alert a Network Management System (NMS) associated with the one or more overload conditions at the one or more first network nodes, and 2) route the current load data of the one or more first network nodes to one or more second network nodes.

BRIEF DESCRIPTION OF DRAWINGS

[00022] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary implementations 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, 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 implement such components.

[00023] FIG. 1 illustrates an exemplary block diagram representation of 5th generation core (5GC) network architecture [100],

[00024] FIG.2 illustrates an exemplary block diagram of a system [200] for routing traffic data, in accordance with exemplary implementations of the present disclosure.

[00025] FIG. 3 illustrates an exemplary network diagram [300] for implementation of a trained model for real-time traffic assessment and traffic optimization in the cellular communication, in accordance with exemplary implementations of the present disclosure.

[00026] FIG.4 illustrates an exemplary method flow diagram indicating a method [400] for routing traffic data, in accordance with exemplary implementations of the present disclosure.

[00027] FIG. 5 illustrates an exemplary system architecture diagram [500] for providing a trained model or SCP performance Artificial Intelligence (SCP-pAI) based model for traffic assessment and traffic optimization in a cellular communication, in accordance with exemplary implementations of the present disclosure.

[00028] FIG.6 illustrates an exemplary block diagram of a computing device upon which the features of the present disclosure may be implemented in accordance with exemplary implementation of the present disclosure.

[00029] The foregoing shall be more apparent from the following more detailed description of the disclosure.

DETAILED DESCRIPTION

[00030] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of implementations of the present disclosure. It will be apparent, however, that implementations 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 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 implementations 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.

[00031] The ensuing description provides exemplary implementations only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary implementations will provide those skilled in the art with an enabling description for implementing an exemplary implementation. It 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.

[00032] It should be noted that the terms "mobile device", "user equipment", "user device", “communication device”, “device” and similar terms are used interchangeably for the purpose of describing the disclosure. These terms are not intended to limit the scope of the disclosure or imply any specific functionality or limitations on the described implementations. The use of these terms is solely for convenience and clarity of description. The disclosure is not limited to any particular type of device or equipment, and it should be understood that other equivalent terms or variations thereof may be used interchangeably without departing from the scope of the disclosure as defined herein.

[00033] Specific details are given in the following description to provide a thorough understanding of the implementations. However, it will be understood by one of ordinary skill in the art that the implementations 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 implementations in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the implementations.

[00034] Also, it is noted that individual implementations may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, 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.

[00035] 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 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 “comprising” as an open transition word — without precluding any additional or other elements.

[00036] As used herein, an “electronic device”, or “portable electronic device”, or “user device” or “communication device” or “user equipment” or “device” refers to any 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 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, Wireless Fidelity (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, 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.

[00037] Further, the user device may also comprise a “processor” or “processing unit” 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 digital signal processor (DSP) core, a controller, a microcontroller, Application Specific Integrated Circuits, 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.

[00038] All modules, units, components used herein, unless explicitly excluded herein, may be software modules or hardware processors, the processors being a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASIC), Field Programmable Gate Array circuits (FPGA), any other type of integrated circuits, etc.

As used herein the receiving unit include at least one receiver configured for receiving data, signals, information, or a combination thereof from units/components within the system and/or connected with the system.

[00039] Further, in accordance with the present disclosure, it is to be acknowledged that the functionality described for the various the components/units can be implemented interchangeably. While specific embodiments may disclose a particular functionality of these units for clarity, it is recognized that various configurations and combinations thereof are within the scope of the disclosure. The functionality of specific units as disclosed in the disclosure should not be construed as limiting the scope of the present disclosure. Consequently, alternative arrangements and substitutions of units, provided they achieve the intended functionality described herein, are considered to be encompassed within the scope of the present disclosure.

[00040] 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 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.

[00041] Radio Access Technology (RAT) refers to the technology used by mobile devices/ 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 and receiving data. Examples of RATs include GSM (Global System for Mobile Communications), CDMA (Code Division Multiple Access), UMTS (Universal Mobile Telecommunications System), UTE (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 devices often support multiple RATs, allowing them to connect to different types of networks and provide optimal performance based on the available network resources.

[00042] As used herein, a Service Communication Proxy (SCP) is a decentralized solution and composed of control plane and data plane. This solution is deployed along side of 5G Network Functions (NF) for providing routing control, resiliency, and observability to the core network. 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 Application Programming Interface (API), load balancing, monitoring, overload control and the like.

[00043] In wireless communication systems there are conditions when a particular instance of SCP is overloaded while other proxies are well underloaded. In such cases, due to overloaded SCP instance, Key Performance Indicators (KPI) may degrade impacting user experience. To avoid such cases, the present disclosure provides automatic or with manual consent traffic optimization to offload the overloaded proxy.

[00044] Mainly, the present disclosure aims to overcome the problems as mentioned in the background section and other existing problems in this field of technology by providing a trained model, wherein the trained model may be an SCP performance Artificial Intelligence (SCP-pAI) based model, for traffic assessment and traffic optimization in a cellular communication to monitor traffic on a particular proxy in real-time and to provide an increment in an effective distribution of the traffic. The model may be implemented in a module which may process the performance statistics fetched from all SCP Proxies and decide performance degradation event. The module may further generate an alternate routing path for the data traffic in case required. The trained model is trained using machine learning or artificial intelligence (Al) by utilizing Transaction per Second (TPS) data and/or traffic data of the one or more network nodes (e.g., one or more proxies of the SCP). The trained model or the SCP-pAI based model monitors the TPS data and/or traffic data to monitor the traffic on the one or more network nodes and compare the existing traffic on a respective network node with the threshold value of handling the TPS and/or traffic. If the TPS data and/or traffic data exceeds the threshold value, the trained model identifies the one or more network nodes that are available to handle the overloaded TPS and/or traffic and generates an alert about threshold breach to divide the traffic from an overloaded node to the one or more network nodes capable of handling the traffic. The decision to shift the traffic from one proxy to the one or more network nodes can be taken manually by designated person or automatically by the trained model.

[00045] In an implementation, the threshold value of the one or more network nodes is a dynamic value which can be altered manually or automatically by the service provider.

[00046] Hereinafter, exemplary implementations of the present disclosure will be described with reference to the accompanying drawings.

[00047] FIG. 1 illustrates an exemplary block diagram representation of 5th generation core (5GC) network architecture, in accordance with exemplary implementation 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 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], aNetwork Exposure Function (NEF) [118], aNetwork 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.

[00048] The User Equipment (UE) [102] interfaces with the network via the Radio Access Network (RAN) [104]; the Access and Mobility Management Function (AMF) [106] manages connectivity and mobility, while the Session Management Function (SMF) [108] administers session control; the service communication proxy (SCP) [110] routes and manages communication between network services, enhancing efficiency and security, and the Authentication Server Function (AUSF) [112] handles user authentication; the Non-Standalone Access Architecture Function (NSSAAF) [114] for integrating the 5G core network with existing 4G LTE networks i.e., to enable Non-Standalone (NSA) 5G deployments, the Network Slice Selection Function (NSSF) [116], Network Exposure Function (NEF) [118], and Network Repository Function (NRF) [120] enable network customization, secure interfacing with external applications, and maintain network function registries respectively; the Policy Control Function (PCF) [122] develops operational policies, and the Unified Data Management (UDM) [124] manages subscriber data; the Application Function (AF) [126] enables application interaction, the User Plane Function (UPF) [128] processes and forwards user data, and the Data Network (DN) [130] connects to external internet resources; collectively, these components are designed to enhance mobile broadband, ensure low-latency communication, and support massive machine-type communication, solidifying the 5GC as the infrastructure for next-generation mobile networks.

[00049] 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 network). It consists of radio base stations and the radio access technologies that enable wireless communication.

[00050] 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.

[00051] 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.

[00052] Service Communication Proxy (SCP) [110] is a network function in the 5G core network that facilitates communication between other network functions by providing a secure and efficient messaging service. It acts as a mediator for service-based interfaces.

[00053] 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. [00054] 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.

[00055] 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.

[00056] Network Exposure Function (NEF) [118] is a network function that exposes capabilities and services of the 5G network to external applications, enabling integration with third-party services and applications.

[00057] 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.

[00058] Policy Control Function (PCF) [122] is a network function responsible for policy control decisions, such as QoS, charging, and access control, based on subscriber information and network policies.

[00059] Unified Data Management (UDM) [124] is a network function that centralizes the management of subscriber data, including authentication, authorization, and subscription information.

[00060] Application Function (AF) [126] is a network function that represents external applications interfacing with the 5G core network to access network capabilities and services.

[00061] User Plane Function (UPF) [128] is a network function responsible for handling user data traffic, including packet routing, forwarding, and QoS enforcement.

[00062] Data Network (DN) [130] refers to a network that provides data services to user equipment (UE) in a telecommunications system. The data services may include but are not limited to Internet services, private data network related services.

[00063] Referring to FIG. 2, an exemplary block diagram of a system [200] for routing traffic data is shown, in accordance with exemplary implementations of the present disclosure. The system [200] comprises a receiving unit [202], a processing unit [204], and an identification unit [206] connected to each other. 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 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. Further, in an implementation, the system [200] may be present in a server device configured at a network end to implement the features of the present disclosure. The system [200] may be a part of the server device / or may be independent of but in communication with the server device.

[00064] The system [200] is configured for routing traffic data, with the help of the interconnection between the components/units of the system [200] .

[00065] As used herein, the term routing traffic data is the process of directing the traffic. The system [200] may use traffic management tools such as firewalls, balancers, and content delivery networks to optimize performance, improve availability, and ensure security, at the network end.

[00066] Particularly, for routing traffic data, initially the receiving unit [202] of the system [200] is configured to receive a current load data of each of a plurality of network nodes. The current load data may refer to as the data related to current traffic at a particular network node or the plurality of network nodes. The plurality of network nodes may refer to as the connection point among network devices such as routers, printers, or switches that can receive and send data from one endpoint to the other. The present disclosure further discloses that each of the plurality of network node may be a Service Communication Proxy (SCP) of a 5th generation network. The SCP is a HTTP/2 based network function enabling dynamic scaling and management of communication and services in the 5G network. The 5G network refers to the 5G telecommunications network.

[00067] Further, the processing unit [204] is configured to analyse the current load data using a trained model. The present disclosure further discloses that the trained model may be a machine learning (ML) based model that has been trained on a large dataset and can be fine-tuned for a specific task.

[00068] Furthermore, the processing unit [204] is configured to 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. The load threshold value may refer to a specific value limit or threshold for the current load data at a particular network node which indicates the limit of traffic to be handled by the network node before the network node is overloaded and may lead to failure of the network node. The present disclosure further discloses that the set of data 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. The increase of traffic and the decrease of traffic at the plurality of network nodes may refer to the situation where the current load data at each of the plurality of network has been increased and decreased respectively. The present disclosure further discloses that the set of data associated with the plurality of network nodes may be an information associated with a peak traffic data and a low traffic data at the plurality of network nodes in a past. The peak traffic data refers to the highest level of the current load data at the particular network node at a prior time. The low traffic data refers to the lowest level of the current load data at the particular network node at a prior time. The past refers to a time prior to routing traffic data. The present disclosure further discloses that the set of data associated with the plurality of network nodes may be a historical trend of traffic at the plurality of network nodes. The historical trend of traffic may refer to the levels of current load data at some particular network node at various periods of time in the past. The present disclosure further discloses that the set of data associated with the plurality of network nodes may be a reason and a cause of the increase of traffic and the decrease of traffic at the plurality of network nodes. These set of data are analysed by the trained model and thereby leads to prediction of the load threshold value for each of the plurality of network nodes.

[00069] Furthermore, the processing unit [204] is configured to compare the current load data with the corresponding load threshold value of each of the plurality of network nodes.

[00070] Furthermore, 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. The one or more first network nodes are the network nodes from the plurality of network nodes which are undergoing with the one or more overload conditions. The overload condition refers to the condition where at the particular network node, the current load data is greater than or equal to the load threshold value, which may lead the network node towards failure. [00071] Furthermore, the processing unit [204] is configured to alert a network management system (NMS) associated with the one or more overload conditions at the one or more first network nodes. The NMS is a server that runs a network management application. Network elements communicate with the NMS to relay management and control information. The NMS also enables network data analysis and reporting.

[00072] Furthermore, the processing unit [204] is configured to route the current load data of the one or more first network nodes to one or more second network nodes. The one or more second network nodes are the network nodes from the plurality of network nodes, which does not have overload condition, and where the current load data is lesser than the load threshold value. The present disclosure further discloses that the one or more second network nodes are the network nodes with available bandwidth to handle routed traffic (i.e., the routed current load data) of the one or more first network nodes. The available bandwidth refers to the amount of current load data which is available with the plurality of network nodes before that particular network nodes become overloaded.

[00073] The present disclosure further discloses that the routing of the current load data is done by the processing unit [204] through one of a manual consent and an automatic consent. The consent may refer to the consent taken by the system [200] from the appropriate Network Functions to route the current load data from the one or more first network nodes to the one or more second network nodes. Manual consent is when the system asks for a consent manually for routing the traffic. Automatic consent is when the consent to route traffic/ current load data from the one or more first network nodes to the one or more second network nodes is provided by the system [200] automatically.

[00074] Also, according to the present disclosure, for routing of the current load data, the processing unit [204] is first configured to remove a set of data from the one or more first network nodes, wherein 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. The processing unit [204] is further configured to register the set of data, via a Network Repository Function (NRF), at the one or more second network nodes. As used herein a Network Function is a logical node within a network infrastructure that has well-defined external interfaces and well-defined functional behaviour. Also, the PLMN stands for Public Land Mobile Network and is a mobile operator's cellular network in a specific country. Each PLMN has a unique PLMN code that combines an MCC (Mobile Country Code) and the operators' MNC (Mobile Network Code). The slice is equipmentvendor agnostic and can span across a radio network from vendor one, to the core from vendor two and so on. Operators may define the specific characteristics of a slice including speed, latency, reliability, and security. The NRF refers to as a centralized repository for all the 5G network functions (NFs) in the operator’s network.

[00075] Referring to FIG. 3, that depicts an exemplary network diagram [300] for implementation of a trained model for real-time traffic assessment and traffic optimization in the cellular communication is shown, in accordance with exemplary implementations of the present disclosure. The network as depicted in the network diagram [300] comprises one or more network nodes [304], a server [306], a client [302] such as including but not limited to Access and Mobility Management Function (AMF). The one or more network nodes [304] include at least a SCP [304], and at least one SCP Proxy (e.g., one or more proxies [308, 310, 312]). Pertinently, it may be understood by a person skilled in the art that even though only 3 proxies are shown in Fig. 3, this disclosure encompasses implementation of any number of proxies, that is, one or more proxies (not limiting to three) may be implemented for practicing the features of the present disclosure. In order to reroute the traffic data and alert a Network Management System (NMS) about the threshold breach, the client [302] is configured to receive data from one or more sources. The one or more sources may include client-based network functions, local servers, cloud-based servers and the like. In an implementation, traffic-based data, signal data, user data may be received from the Network Functions at the client level. In an implementation, the data associated with pattern of traffic, historical data, occurrence of events that impact the traffic at the network and the like may be received from the local servers and the cloud-based servers.

[00076] The trained model implemented in the network [300] is configured for providing traffic assessment and traffic optimization in a cellular communication to monitor traffic on a particular proxy in real-time and to increment an effective distribution of the traffic, with the help of the interconnection between the components/units of the system [200] .

[00077] In order to provide the trained model for traffic assessment and traffic optimization in a cellular communication, the client [302] is configured to communicate with the SCP [304] to establish a connection for a user.

[00078] The SCP [304] includes the one or more proxies [308, 310, 312] that handles routing of the traffic and identifies anomalies in the network. The SCP [304] monitors the real-time TPS data to identify traffic load at the one or more proxies [308, 310, 312] . The system [200] using the trained model assesses the traffic load at pre-defined regular intervals to ensure that the overloaded traffic is distributed to the one or more proxies [308, 310, 312] at regular intervals. The pre-defined regular intervals may be defined by certain operator policies. If there is an increase of the TPS value of the one or more proxies [308, 310, 312] beyond the threshold value, it leads to communication and data usage issues to users due to an overload of traffic. In such cases, the traffic is required to be divided and routed to one or more proxies that are available to handle the divided traffic without causing communication and data usage issues to users. Upon identifying the real-time availability of the one or more proxies that are capable of handling the overloaded traffic, the SCP [304] with the help of the system [200] generates an alert of the same.

[00079] When the alert about the threshold breach is generated, the decision to distribute the traffic from the overloaded one or more proxies [308, 310, 312] to other proxy(s) can be taken manually by service provider or automatically by the trained model, using the system [200] .

[00080] In an implementation the trained model based on the TPS pattern and traffic pattern, triggers via the system [200], the one or more proxies [308, 310, 312] to update their registration data that requires inputs like supported (NF) types, supported Public Land Mobile Network (PLMN), supported Slice, and/or the like.

[00081] In an implementation the one or more proxies [308, 310, 312] upon on receiving the trigger from the trained model re-registers the updated registration data to a Controller. The controller on receiving updated registration data from the one or more proxies [308, 310, 312], sends updated Registration to Network Repository Function (NRF) and broadcasts the updated Registration data to all the one or more proxies. After this, the NRF can then notify subscriber NFs of the updates.

[00082] The server [306] is configured to perform functions of a cellular communication in a conventional manner.

[00083] Referring to Fig. 4, an exemplary method flow diagram [400] indicating the routing traffic data is shown, in accordance with exemplary implementations of the present disclosure. In an implementation the method [400] is performed by the system [200], As shown in Figure 2, the method [400] starts at step [402], As used herein, the term routing traffic data is the process of directing the traffic. The system [200] may use traffic management tools such as firewalls, balancers, and content delivery networks to optimize performance, improve availability, and ensure security, at the network end.

[00084] Next at step [404], the method [400] as disclosed by the present disclosure comprises receiving, by the receiving unit [202], the current load data of each of a plurality of network nodes. The current load data may refer to as the data related to current traffic at a particular network node or the plurality of network nodes. The plurality of network nodes may refer to as the connection point among network devices such as routers, printers, or switches that can receive and send data from one endpoint to the other. The present disclosure further discloses that each of the plurality of network node may be a Service Communication Proxy (SCP) of a 5th generation network. The SCP is a HTTP/2 based network function enabling dynamic scaling and management of communication and services in the 5G network. The 5G network refers to the 5G telecommunications network.

[00085] Next at step [406], the method [400] as disclosed by the present disclosure comprises analysing, by the processing unit [204], the current load data using a trained model. The present disclosure further discloses that the trained model may be a machine learning (ML) based model that has been trained on a large dataset and can be fine-tuned for a specific task.

[00086] Next at step [408], the method [400] as disclosed by the present disclosure comprises predicting, by the processing unit [204] using the trained model, the 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. The load threshold value may refer to a specific value limit or threshold for the current load data at a particular network node which indicates the limit of traffic to be handled by the network node before the network node is overloaded and may lead to failure of the network node. The present disclosure further discloses that 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. The increase of traffic and the decrease of traffic at the plurality of network nodes may refer to the situation where the current load data at each of the plurality of network has been increased and decreased respectively. The present disclosure further discloses that the set of data associated with the plurality of network nodes may be an information associated with a peak traffic data and a low traffic data at the plurality of network nodes in a past. The peak traffic data refers to the highest level of the current load data at the particular network node at a prior time. The low traffic data refers to the lowest level of the current load data at the particular network node at a prior time. The past refers to a time prior to routing traffic data. The present disclosure further discloses that the set of data associated with the plurality of network nodes may be a historical trend of traffic at the plurality of network nodes. The historical trend of traffic may refer to the levels of current load data at some particular network node at various periods of time in the past. The present disclosure further discloses that the set of data associated with the plurality of network nodes may be a reason and a cause of the increase of traffic and the decrease of traffic at the plurality of network nodes. These set of data are analysed by the trained model and thereby leads to prediction of the load threshold value for each of the plurality of network nodes.

[00087] Then at step [410], the method [400] as disclosed by the present disclosure comprises comparing, by the processing unit [204], the current load data with the corresponding load threshold value of each of the plurality of network nodes.

[00088] Then at step [412], the method [400] as disclosed by the present disclosure comprises identifying, by the 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. The one or more first network nodes are the network nodes from the plurality of network nodes which are undergoing with the one or more overload conditions. The overload condition refers to the condition where at the particular network node, the current load data is greater than or equal to the load threshold value, which may lead the network node towards failure.

[00089] Then at step [414], the method [400] as disclosed by the present disclosure comprises alerting, by the processing unit [204], the NMS associated with the one or more overload conditions at the one or more first network nodes. The NMS is a server that runs a network management application. Network elements communicate with the NMS to relay management and control information. The NMS also enables network data analysis and reporting.

[00090] Lastly at step [416], the method [400] as disclosed by the present disclosure comprises 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. The one or more second network nodes are the network nodes from the plurality of network nodes, which does not have overload condition, and where the current load data is lesser than the load threshold value. The present disclosure further discloses that the one or more second network nodes are the network nodes with available bandwidth to handle routed traffic (i.e., the routed current load data) of the one or more first network nodes. The available bandwidth refers to the amount of current load data which is available with the plurality of network nodes before that particular network nodes become overloaded.

[00091] The present disclosure further discloses that the routing of the current load data is done by the processing unit [204] through one of a manual consent and an automatic consent. The consent may refer to the consent taken by the system [200] from the appropriate Network Functions to route the current load data from the one or more first network nodes to the one or more second network nodes. Manual consent is when the system asks for a consent manually for routing the traffic. Automatic consent is when the consent to route traffic/ current load data from the one or more first network nodes to the one or more second network nodes is provided by the system [200] automatically.

[00092] Also, according to the present disclosure, for routing of the current load data, the processing unit [204] firstly removes a set of data from the one or more first network nodes, wherein 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. The processing unit [204] then registers the set of data, via a Network Repository Function (NRF), at the one or more second network nodes. As used herein the Network Function is a logical node within a network infrastructure that has well-defined external interfaces and well-defined functional behaviour. Also, the PLMN stands for Public Land Mobile Network and is a mobile operator's cellular network in a specific country. Each PLMN has a unique PLMN code that combines an MCC (Mobile Country Code) and the operators' MNC (Mobile Network Code). The slice is equipment-vendor agnostic and can span across a radio network from vendor one, to the core from vendor two and so on. Operators may define the specific characteristics of a slice including speed, latency, reliability, and security. The NRF refers to as a centralized repository for all the 5G network functions (NFs) in the operator’s network.

[00093] Thereafter, the method [400] terminates at step [418],

[00094] Referring to Fig. 5, an exemplary system architecture diagram [500] for providing a trained model or SCP performance Artificial Intelligence (SCP-pAI) [502] based model for routing traffic data, traffic assessment and traffic optimization in a cellular communication, in accordance with exemplary implementations of the present disclosure is shown. The model may be implemented in a module which may process the performance statistics fetched from all SCP Proxies and decide performance degradation event. The module may further generate an alternate routing path for the data traffic in case required.

[00095] Mainly, a current load data of one or more network nodes (SCP-Proxy-I [506], SCP- Proxy-II [508], and SCP -Proxy-Ill [510]) is received by the receiving unit [202] and analysed by the processing unit [204] . The load threshold value for each of the SCP-Proxy-I [506], SCP-Proxy- II [508], and SCP -Proxy-Ill [510] is determined based on the analysis of a set of data associated with the plurality of network nodes. The processing unit [204] compares the current load data on each of the SCP-Proxy-I [506], SCP-Proxy-II [508], and SCP-Proxy-III [510] with the corresponding load threshold value of each of the SCP-Proxy-I [506], SCP-Proxy-II [508], and SCP-Proxy-III [510], Basis this comparison, overload conditions on one or more of the SCP- Proxy-I [506], SCP-Proxy-II [508], and SCP-Proxy-III [510] are identified. Basis the identified overload conditions, the NMS is informed about such overload conditions. Thereafter, the traffic load is routed from the overloaded SCP Proxy to one of the SCP-Proxy-I [506], SCP-Proxy-II [508], and SCP-Proxy-III [510] that is available to handle the load. The same is notified to the associated Network Functions (NFs).

[00096] For example, the SCP-pAI [502] communicates with the SCP - Proxy Egress [504], The SCP- Proxy egress [504] is connected with and communicates with each of the SCP-Proxy-I [506], SCP-Proxy-II [508], and SCP-Proxy-III [510], The SCP-Proxy-I [506] is initially connected with at least one of an NF A [512], and an NF B [514], The SCP-Proxy-II [508] is initially connected with at least of an NF C [516] , an NF X [518] , and an NF Y [520] . The SCP-Proxy-III [510] is initially connected with at least one of an NF XA [522], an NF XB [524], and an NF XC [526], The initial path for reaching a particular NF, say NF A [512] is through SCP-Proxy-I [506], if due to some reason, say increase in traffic at NF B [514], the SCP-Proxy-I tends to become overloaded, then in that scenario, the SCP-pAI [502] selects another Proxy such as SCP-Proxy-III [510] which has comparatively less traffic, as an alternate path based on ML process. Then the SCP-Proxy Egress [504] selects the SCP-Proxy-III [510] and then connects the NF-A [512] to the SCP-Proxy-III [510],

[00097] Fig. 6 illustrates an exemplary block diagram of a computing device [600] (or referred herein as computer system [600]) upon which the features of the present disclosure may be implemented in accordance with exemplary implementation of the present disclosure. In an implementation, the computing device [600] may also implement the method [400] for routing traffic data by utilising the system [200] . In another implementation, the computing device [600] itself implements the method [400] for routing traffic data using one or more units configured within the computing device [600], wherein said one or more units are capable of implementing the features as disclosed in the present disclosure.

[00098] The computing device [600] may include a bus [602] or other communication mechanism for communicating information, and a hardware processor [604] coupled with bus [602] for processing information. The hardware processor [604] may be, for example, a general purpose microprocessor. The computer system [600] may also include amain memory [606], such as a random access memory (RAM), or other dynamic storage device, coupled to the bus [602] for storing information and instructions to be executed by the processor [604] . The main memory [606] also may be used for storing temporary variables or other intermediate information during execution of the instructions to be executed by the processor [604] . Such instructions, when stored in non-transitory storage media accessible to the processor [604], render the computer system [600] into a special-purpose machine that is customized to perform the operations specified in the instructions. The computer system [600] further includes a read only memory (ROM) [608] or other static storage device coupled to the bus [602] for storing static information and instructions for the processor [604] .

[00099] A storage device [610], such as a magnetic disk, optical disk, or solid-state drive is provided and coupled to the bus [602] for storing information and instructions. The computer system [600] may be coupled via the bus [602] to a display [612], such as a cathode ray tube (CRT), Liquid crystal Display (LCD), Light Emitting Diode (LED) display, Organic LED (OLED) display, etc. for displaying information to a computer user. An input device [614], including alphanumeric and other keys, touch screen input means, etc. may be coupled to the bus [602] for communicating information and command selections to the processor [604] . Another type of user input device may be a cursor controller [616], such as a mouse, a trackball, or cursor direction keys, for communicating direction information and command selections to the processor [604], and for controlling cursor movement on the display [612], 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 the device to specify positions in a plane.

[000100] The computer system [600] may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computer system [600] causes or programs the computer system [600] to be a special -purpose machine. According to one implementation, the techniques herein are performed by the computer system [600] in response to the processor [604] executing one or more sequences of one or more instructions contained in the main memory [606] . Such instructions may be read into the main memory [606] from another storage medium, such as the storage device [610], Execution of the sequences of instructions contained in the main memory [606] causes the processor [604] to perform the process steps described herein. In alternative implementations of the present disclosure, hard-wired circuitry may be used in place of or in combination with software instructions.

[000101] The computer system [600] also may include a communication interface [618] coupled to the bus [602] . The communication interface [618] provides a two-way data communication coupling to a network link [620] that is connected to a local network [622], For example, the communication interface [618] may be an 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 [618] 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 implementation, the communication interface [618] sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

[000102] The computer system [600] can send messages and receive data, including program code, through the network(s), the network link [620] and the communication interface [618], In the Internet example, a server [630] might transmit a requested code for an application program through the Internet [628], the ISP [626], the local network [622], the host [624] and the communication interface [618], The received code may be executed by the processor [604] as it is received, and/or stored in the storage device [610], or other non-volatile storage for later execution.

[000103] Further, an aspect of the present disclosure relates to a non-transitory computer readable storage medium storing instructions for routing traffic data. The instructions include executable code which, when executed by one or more units of a system [200], causes: a receiving unit [202] of the system [200] to receive a current load data of each of a plurality of network nodes; the processing unit [204] of the system [200] to analyse the current load data using a trained model; the processing unit [204] of the system [200] to 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; the processing unit [204] of the system [200] to compare the current load data with the corresponding load threshold value of each of the plurality of network nodes; an identification unit [104] of the system [200] 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; the processing unit [204] of the system [200] to alert an NMS associated with the one or more overload conditions at the one or more first network nodes; and the processing unit[204] of the system [200] to route the current load data of the one or more first network nodes to one or more second network nodes.

[000104] As is evident from the above, the present disclosure provides a technically advanced solution by providing the trained model for traffic assessment and traffic optimization in a cellular communication to monitor traffic on a particular proxy in real-time and to increment an effective distribution of the traffic. Various advantages of the present disclosure involve real-time monitoring of the traffic load on the one or more network nodes; real-time distribution of the traffic load based on identified traffic load on the one or more network nodes; optimum usage of the one or more network nodes; enhanced user communication and data usage experience and prevention of KPI degradation; decreased operational costs due to a decreased overall network failure due to traffic overload; scale-out prevention in case of any sudden traffic surge; and prediction of future overload conditions beforehand based on historical trends which may also help in taking optimum routing steps either automatically or through manual consent.

[000105] While considerable emphasis has been placed herein on the disclosed implementations, it will be appreciated that many implementations can be made and that many changes can be made to the implementations without departing from the principles of the present disclosure. These and other changes in the implementations 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.

Claims

We Claim:

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. 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. The method [400] as claimed in claim 1, wherein the trained model is a machine learning (ML) based model.

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. 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. 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. 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. 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. 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. The system [200] as claimed in claim 8, wherein the trained model is a machine learning (ML) based model.

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. 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. 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. 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. 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.