Method and system for scaling up network nodes

Abstract

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

Description

METHOD AND SYSTEM FOR SCALING UP NETWORK NODESFIELD OF THE DISCLOSURE

[0001] The present disclosure relates generally to the field of wireless communication systems. More particularly, the present disclosure relates to methods and systems for scaling up network nodes to handle overload conditions.BACKGROUND

[0002] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

[0003] Wireless communication technology has rapidly evolved over the past few decades, with each generation bringing significant improvements and advancements. The first generation of wireless communication technology was based on analog technology and offered only voice services. However, with the advent of the second-generation (2G) technology, digital communication and data services became possible, and text messaging was introduced. 3G technology marked the introduction of high-speed internet access, mobile video calling, and location-based services. The fourth-generation (4G) technology revolutionized wireless communication with faster data speeds, better network coverage, and improved security. Currently, the fifth-generation (5G) technology is being deployed, promising even faster data speeds, low latency, and the ability to connect multiple devices simultaneously. With each generation, wireless communication technology has become more advanced, sophisticated, and capable of delivering more services to its users.

[0004] In the prior art, managing network scalability in 5G networks, particularly for Service Communication Proxies (SCPs), presents several challenges. As the number of 5G subscribers increases or the service patterns of existing subscribers change, SCP proxies may begin to experience higher traffic loads. Initially, traffic distribution optimizations are performed to ensure that the load is evenly distributed across SCP proxies. However, there may come a point where all proxies are operating at maximum capacity, leading to potential service degradation and impacting user experience. A significant problem in the existing techniques is the lack of predictivemechanisms to anticipate and manage these overload conditions. Traditional methods rely on reactive approaches, where scaling decisions are made only after the network is already experiencing congestion. This can lead to delays in scaling up the network infrastructure, resulting in reduced service quality and potential downtime. Furthermore, the prior art lacks an intelligent system that can provide recommendations for scaling out SCP proxies, taking into account factors such as the optimal site for deployment and the types of Network Functions (NFs) that should be supported by the new proxies. The absence of a proactive and intelligent scaling approach limits the efficiency and effectiveness of network management in 5G systems.

[0005] Thus, in order to improve radio access network capacity and performance, there exists an imperative need in the art to provide methods and systems for scaling up network nodes that efficiently manage the overload conditions at the network.OBJECTS OF THE PRESENT DISCLOSURE

[0006] Some of the objects of the present disclosure, which at least one implementation disclosed herein satisfies are listed herein below.

[0007] It is an object of the present disclosure to provide a system and method for scaling up network nodes.

[0008] It is another object of the present disclosure to provide a system and method for scaling up network nodes that proactively manages network load by predicting future overload conditions using historical data trends.

[0009] It is another object of the present disclosure to provide a system and method for scaling up network nodes that utilize Artificial Intelligence (Al) and Machine Learning (ML) to notify network administrators of the need to scale out before reaching critical load levels, ensuring uninterrupted service quality.

[0010] It is another object of the present disclosure to provide a system and method for scaling up network nodes that offer consent-based scale-out decisions, allowing for more controlled and deliberate expansion of network resources.

[0011] It is another object of the present disclosure to provide a system and method for scaling up network nodes that generate specific site and Network Function (NF) type recommendations for the new scale-out SCP Proxies, optimizing resource distribution and efficiency.

[0012] It is another object of the present disclosure to provide a system and method for scaling up network nodes that enable a seamless and dynamic adaptation of the network infrastructure in response to the evolving demands of 5G service patterns and subscriber behaviours.

[0013] It is another object of the present disclosure to provide a system and method for scaling up network nodes that minimize the latency between detecting potential overload conditions and initiating scale-out actions, thereby reducing the likelihood of service degradation or downtime.

[0014] It is another object of the present disclosure to provide a system and method for scaling up network nodes that incorporate a user-friendly notification system, ensuring that critical information regarding load thresholds and scale-out recommendations is communicated efficiently to the Network Management System (NMS).

[0015] It is yet another object of the present disclosure to provide a system and method for scaling up network nodes that systematically store and utilize load data, enabling a more intelligent and data-driven approach to network management and scaling decisions.SUMMARY

[0016] 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.

[0017] An aspect of the present disclosure provides a method for scaling up network nodes. The method includes receiving, by a receiving unit, a current load data associated with each of a plurality of network nodes. The method further includes predicting, by a processing unit using a trained model, a load threshold value for each of the plurality of network nodes. The method further includes comparing, by a comparing unit, the current load data with the corresponding load threshold value of each of the plurality of network nodes to forecast overload conditions at the plurality of network nodes. Thereafter, the method includes alerting, by an alerting unit, a Network Management System (NMS) to scale up the network nodes in an event the current load data breach the corresponding load threshold value of the plurality of network nodes.

[0018] In an aspect, each of the plurality of network nodes is a Service Communication Proxy (SCP) of a 5th Generation (5G) network.

[0019] In an aspect, the trained model is trained based on of a historical set of data associated with the plurality of network nodes, the historical set of data comprises past traffic load patterns,traffic distribution trends, peak traffic times, and historical overload events at the plurality of network nodes.

[0020] In an aspect, the trained model is an artificial intelligence (Al) based model.

[0021] In an aspect, the current load data associated with the plurality of network nodes comprises information about increase and decrease of traffic at the plurality of network nodes, information about peak traffic data and low traffic data at the plurality of network nodes in past, historical trend of traffic at the plurality of network nodes, reason and causes of increase and decrease of traffic at the plurality of network nodes.

[0022] In an aspect, the method comprises notifying, by the processing unit, network node scale-up data to the NMS, wherein the network node scale-up data comprises site details, network function (NF) type details, number of required network nodes.

[0023] In an aspect, the scale-up corresponds to addition of at least one SCP node in the 5G network.

[0024] Another aspect of the present disclosure provides a system for scaling up network nodes. The system includes a receiving unit configured to receive a current load data associated with each of a plurality of network nodes. The system further includes a processing unit configured to predict, via a trained model, a load threshold value for each of the plurality of network nodes. The system further includes a comparing unit configured to compare the current load data with the corresponding load threshold value of each of the plurality of network nodes to forecast overload conditions at the plurality of network nodes. Further, the system includes an alerting unit configured to alert a Network Management System (NMS) to scale up the network nodes in an event the current load data breaches the corresponding load threshold value of the plurality of network nodes.

[0025] Yet another aspect of the present disclosure provides a non -transitory computer- readable storage medium storing instruction for scaling up network nodes, the storage medium comprising executable code which, when executed by one or more units of a system, causes: a receiving unit to receive a current load data associated with each of a plurality of network nodes; a processing unit to predict, via a trained model, a load threshold value for each of the plurality of network nodes; a comparing unit to compare the current load data with the corresponding load threshold value of each of the plurality of network nodes to forecast overload conditions at the plurality of network nodes; and an alerting unit to alert a Network Management System (NMS) to scale up the network nodes in an event the current load data breaches the corresponding load threshold value of the plurality of network nodes.BRIEF DESCRIPTION OF DRAWINGS

[0026] 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.

[0027] FIG. 1A illustrates an exemplary block diagram representation of 5th generation core (5GC) network architecture.

[0028] FIG. IB illustrates an exemplary block diagram of a system with functional units and modules, in accordance with exemplary implementations of the present disclosure.

[0029] FIG. 2 illustrates an exemplary block diagram of an architecture for implementation of a system for scaling up network nodes in a wireless communication network, in accordance with exemplary implementations of the present disclosure.

[0030] FIG. 3 illustrates an exemplary method flow diagram indicating the process scaling up network nodes, in accordance with exemplary implementations of the present disclosure.

[0031] FIG. 4 illustrates an exemplary block diagram of a computing device upon which an embodiment of the present disclosure may be implemented.

[0032] The foregoing shall be more apparent from the following more detailed description of the disclosure.DETAILED DESCRIPTION

[0033] 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 problemsdiscussed 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.

[0034] 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.

[0035] 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 invention. These terms are not intended to limit the scope of the invention 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 invention 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 invention as defined herein.

[0036] 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.

[0037] 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 figures.

[0038] 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 hereinis 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.

[0039] 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, 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.

[0040] 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 (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.

[0041] 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), thirdgeneration (3G), fourth generation (4G), and now fifth generation (5G), and more such generations are expected to continue in the forthcoming time.

[0042] 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), LTE (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.

[0043] As used herein, Service Communication Proxy 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 to 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.

[0044] As discussed in the background section, the current known solutions for managing network scalability in 5G networks, particularly for Service Communication Proxies (SCPs), presents several challenges. As the number of 5G subscribers increases or the service patterns of existing subscribers change, SCP proxies may begin to experience higher traffic loads. Initially, traffic distribution optimizations are performed to ensure that the load is evenly distributed across SCP proxies. However, there may come a point where all proxies are operating at maximum capacity, leading to potential service degradation and impacting user experience. A significant problem in the prior art is the lack of predictive mechanisms to anticipate and manage these overload conditions. Traditional methods rely on reactive approaches, where scaling decisions are made only after the network is already experiencing congestion. This can lead to delays in scaling up the network infrastructure, resulting in reduced service quality and potential downtime. Furthermore, the prior art lacks an intelligent system that can provide recommendations for scaling out SCP proxies, taking into account factors such as the optimal site for deployment and the typesof Network Functions (NFs) that should be supported by the new proxies. This absence of a proactive and intelligent scaling approach limits the efficiency and effectiveness of network management in 5G systems.

[0045] The present disclosure aims to overcome the above-mentioned and other existing problems in this field of technology by introducing a method that enhances the scalability management of 5G network nodes, particularly Service Communication Proxies (SCPs). The method significantly improves upon the prior art by integrating a predictive mechanism that uses artificial intelligence to anticipate and address overload conditions before they lead to network congestion and service degradation. In the disclosed method, a receiving unit collects current load data from a plurality of network nodes, which include SCPs for determining real-time network usage and potential stress points. A processing unit, using a trained model, predicts a load threshold value for each network node. The predictive capability of the trained model is a substantial improvement over prior systems as it is based on a historical set of data comprising past traffic load patterns, traffic distribution trends, peak traffic times, and historical overload events. This means the system can recognize potential overload conditions much earlier. When the comparing unit assesses the current load data against the load threshold values and identifies a potential overload situation, it does not simply wait for the congestion to occur. Instead, an alerting unit proactively informs the Network Management System (NMS) of the need to scale up the network nodes. This early warning system enables the NMS to implement scale-up measures in a timely fashion, thereby avoiding the reactive delays seen in prior art systems.

[0046] It would be appreciated by the person skilled in the art that the present disclosure provides a solution that transforms the reactive, often delayed response to network overload into a proactive, intelligent, and strategic process. This approach not only improves the user experience by maintaining service quality but also enhances the operational efficiency of network management in 5G systems.

[0047] In an example, there are 5 virtual proxy or machines available at the network. The present disclosure keeps track of the parameters received at the network to check the possibility of the bottleneck conditions at the network. For instance, in one case, these 5 virtual machines may at the verge of getting overload in next one month. Thus, the present disclosure may alert the Network Management System (NMS) team to scale up the proxy at the network such as increasing the number of proxies at the SCP. This may prevent the bad effects or bad impact that may occur after existing proxy achieve the condition of bottleneck.

[0048] In another example, the message received at the plurality of network nodes are analysed by the system using the ML based model to check if a certain threshold value is reachedor crossed at the network node. In case the certain threshold value is not crossed, the scaling up of the network nodes is not required. However, if the certain threshold value is crossed, then an alert may be sent to the NMS team to plan scaling up the network nodes, proxy to efficiently handle the bottleneck conditions at the network. In an example, the threshold may be defined as 85% of the actual capacity of the virtual machines.

[0049] In an example, the threshold value is pre-defined by a network operator.

[0050] In another example, the pre-defined threshold can be modified by the network operator.

[0051] Hereinafter, exemplary implementations of the present disclosure will be described with reference to the accompanying drawings.

[0052] FIG. 1A illustrates an exemplary block diagram representation of 5th generation core (5GC) network architecture. As shown in FIG. 1A, the 5GC network architecture

[0101] includes a user equipment (UE) [101a], a radio access network (RAN) [101b], a 5G Core Network and a Data Network [10 Ip], The 5G Core Network includes an access and mobility management function (AMF) [101c], a Session Management Function (SMF) [lOld], a Service Communication Proxy (SCP) [lOle], an Authentication Server Function (AUSF) [lOlf], a Network Slice Specific Authentication and Authorization Function (NSSAAF) [101g], a Network Slice Selection Function (NSSF) [lOlh], a Network Exposure Function (NEF) [lOli], a Network Repository Function (NRF) [lOlj], a Policy Control Function (PCF) [101k], a Unified Data Management (UDM)

[1011] , an application function (AF) [101m], a User Plane Function (UPF) [101n],

[0053] The User Equipment (UE) [101a] interfaces with the network via the Radio Access Network (RAN) [101b]. The RAN [101b] in the 5G architecture is also called as New Radio or NG-RAN, and these terms may be interchangeably used herein. Radio Access Network (RAN) [101b] is the part of a mobile telecommunications system that connects user equipment (UE) [101a] to the core network (CN) and provides access to different types of networks (e.g., 5G, LTE). It consists of radio base stations and the radio access technologies that enable wireless communication.

[0054] The Access and Mobility Management Function (AMF) [101c] manages connectivity and mobility. When a UE [101a] is active, i.e. it is interacting with the 5G network, e.g., by using data / call functionalities, the AMF [101c] knows and maintains the location of the UE [101a] within the network. The AMF [101c] is configured to maintain the tracking area or registration area of the UE [101a], in case the UE is inactive. The AMF [101c] is configured to communicatewith other network functions / elements such as the Session Management Function (SMF) [ 10 Id], etc. to ensure that the UE [101a] is allowed and is able to avail the services by the network.

[0055] Particularly, the Access and Mobility Management Function (AMF) [101c] is a 5G core network function responsible for managing access and mobility aspects, such as UE registration, connection, and reachability etc. It also handles mobility management procedures like handovers and paging.

[0056] The Session Management Function (SMF) [lOld] 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.

[0057] The Service Communication Proxy (SCP) [lOle] is a network function in the 5G core that facilitates communication between other network functions by providing a secure and efficient messaging service. It acts as a mediator for service-based interfaces.

[0058] The Authentication Server Function (AUSF) [10 If] 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.

[0059] The Network Slice Specific Authentication and Authorization Function (NSSAAF) [101g] 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.

[0060] The Network Slice Selection Function (NSSF) [lOlh] 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.

[0061] The Network Exposure Function (NEF) [lOli] is a network function that exposes capabilities and services of the 5G network to external applications, enabling integration with third-party services and applications.

[0062] The Network Repository Function (NRF) [10 Ij] 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.

[0063] The Policy Control Function (PCF) [101k] is a network function responsible for policy control decisions, such as QoS, charging, and access control, based on subscriber information and network policies.

[0064] The Unified Data Management (UDM)

[1011] is a network function that centralizes the management of subscriber data, including authentication, authorization, and subscription information.

[0065] The Application Function (AF) [101m] is a network function that represents external applications interfacing with the 5G core network to access network capabilities and services.

[0066] The User Plane Function (UPF) [10 In] is a network function responsible for handling user data traffic, including packet routing, forwarding, and QoS enforcement.

[0067] The Data Network (DN) [10 Ip] represents external networks or services that users connect to through the mobile network, such as the internet or enterprise networks.

[0068] Referring to FIG. IB, an exemplary block diagram of a system

[0100] for scaling up network nodes is shown, in accordance with the exemplary implementations of the present invention. The system

[0100] comprises a receiving unit

[0102] , a comparing unit

[0104] , an alerting unit

[0106] and a processing unit

[0108] , Also, all of the components / units of the system

[0100] are assumed to be connected to each other unless otherwise indicated below. Also, in FIG. 1 only a few units are shown, however, the system

[0100] may comprise multiple such units or the system

[0100] may comprise any such numbers of said units, as required to implement the features of the present disclosure. Further, in an implementation, the system

[0100] may be present at a network level to implement the features of the present invention. In an implementation, the system

[0100] may reside in a server, a network entity or a SCP controller

[0204] .

[0069] The system

[0100] is configured for scaling up network nodes handling of overload conditions, with the help of the interconnection between the components / units of the system

[0100] ,

[0070] In order to monitor the overload conditions and to alert the Network Management System, the receiving unit

[0102] of the system

[0100] is configured to receive a current load data of each of a plurality of network nodes. Further, the each of the plurality of network nodes is a Service Communication Proxy (SCP) of a 5th Generation (5G) network. The current load data may include but not limited to information associated with existing users and new users, real-time traffic, Transaction Per Second (TPS), key performance indicators, Metric, metadata associated with signals, the traffic volume, types of services being accessed, and the number of active users at any given time on each of the plurality of network nodes.

[0071] Further, system

[0100] comprises the processing unit

[0108] communicatively coupled to the receiving unit

[0102] , The processing unit

[0108] is configured to predict, using a 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. In an implementation of the presentdisclosure, the trained model is a machine learning (ML) based model. The Machine Learning based techniques are used for prediction of overload conditions at the network level based on the possible trends associated with increase in number of users accessing the network. The threshold value may be defined based on the analysis of the data using the machine learning model (such as the trained model). In an example, threshold value may be defined as 90% of the actual capacity of the proxy level. In another example, threshold level may vary based on the analysis of the key performance indicators associated with received messages, historical data and the like. Thus, the system

[0100] automatically predicts the overload conditions whenever the system

[0100] suspect of crossing the threshold level. For instance, the machine trained model may automatically predict the time left or possible time left in which the overload conditions may occur based on the current trend and pattern. In an example, the trained model may determine based on the analysis of the data that the overload conditions may be achieved in next 15 day; thus, the NMS may take step to efficiently control such conditions prior to the occurrence of such conditions.

[0072] In an implementation of the present disclosure, the set of data associated with the plurality of network nodes comprises information about the increase and decrease of traffic at the plurality of network nodes, information about peak traffic data and low traffic data at the plurality of network nodes in past, historical trend of traffic at the plurality of network nodes, reason and causes of increase and decrease of traffic at the plurality of network nodes. The trained model

[0206] may be trained based on a historical set of data associated with the plurality of network nodes. This historical set of data comprises past traffic load patterns, traffic distribution trends, peak traffic times, and historical overload events at the plurality of network nodes. By analysing the historical data, the trained model

[0206] can accurately predict the load threshold value for each network node.

[0073] Further, the system

[0100] comprises comparing unit

[0104] communicatively coupled to the receiving unit

[0102] and the processing unit

[0108] , The comparing unit

[0104] is configured to compare the current load data with the corresponding load threshold value of each of the plurality of network nodes to forecast overload conditions on the plurality of network nodes. In an example, by performing comparison, the comparing unit

[0104] is able to predict when a node is approaching a state where demand may exceed capacity i.e., an overload condition. It would be appreciated by the person skilled in the art that the foresight allows initiation of preventive measures to handle the increased load efficiently. Such as scale up of network nodes.

[0074] Further, the system comprises alerting unit

[0106] communicatively coupled to the comparing unit

[0104] , The alerting unit

[0106] is configured to alert a Network Management System(NMS) to scale up the network nodes in an event when the current load data exceeds the corresponding load threshold value of the plurality of network nodes.

[0075] In an implementation of the present disclosure, the processing unit

[0108] is configured to notify the network node scale-up data to the NMS, wherein the network node scale-up data comprises node deployment site details, network function (NF) type details, and number of required network nodes.

[0076] In an implementation of the present disclosure, the NMS may take the required steps to efficiently handle the overload condition at the network. In an example, the NMS may plan to scale up the proxy or virtual machines when the plurality of proxy has overall crossed the defined threshold level in terms of their capacity. The alerting to the NMS may be done via a social based platform, via email, via Short Message Service (SMS), and the like.

[0077] Referring to FIG. 2, an exemplary block diagram of an architecture for implementation of a system for scaling up network nodes to handle overload in a wireless communication network is shown, in accordance with exemplary implementations of the present disclosure. The system architecture

[0200] comprises one or more SCP proxies (such as SCP proxy 1 [202A] and / or SCP proxy2 [202B]), SCP controller

[0204] , trained model

[0206] , one or more network function (NF) consumers (such as NF consumerl [208A] and / or NF consumed [208B]), one or more NFs (such as NF-A [210A], NF-B [210B], NF-C [210C], NF-D [210D]), and notification targets

[0212] , Also, all of the components / units of the system architecture

[0200] 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 architecture

[0200] may comprise multiple such units or the system architecture

[0200] may comprise any such numbers of said units, as required to implement the features of the present disclosure.

[0078] In operation, the SCP Proxy (such as SCP proxy 1 [202A] and / or SCP proxy2 [202B]) may first determine the current load at a regular interval. Following the determination of the current load, the SCP controller

[0204] may receive the determined current load. The current load data may correspond to network load data of the one or more network functions (such as NF-A [210A], NF-B [210B], NF-C [210C], NF-D [210D]). Examples, of the one or more network functions (such as NF-A [210A], NF-B [210B], NF-C [210C], NF-D [210D]) may include, but not limited only to various network functions of the5Gcore network. Examples of NFs include, but not limited only to PCF, charging function (CHF), AMF, SMF, UDM, NSSF. Thereafter, the trained model

[0206] may retrieve the current load data from the at least one SCP controller

[0204] at intervals that may be set based on at least of the network operator's policy or other networkrequirements such as traffic conditions, historical data patterns, expected service demand, or planned network maintenance activities.

[0079] Once the trained model

[0206] acquires the current load data, it may forecast a load threshold value for potential overload situations. The forecast may be performed by analysing the current load data against historical traffic patterns and trends to establish a threshold that indicates the maximum load that the SCP Proxy (such as SCP proxy 1 [202A] and / or SCP proxy2 [202B]) can manage before it is deemed at risk of overload. The trained model

[0206] then communicates the predictive threshold data to the SCP controller

[0204] . The SCP controller

[0204] may then compare the current load with the corresponding predicted threshold.

[0080] In response to the detected breach, the SCP controller

[0204] may generate an alert that may be transmitted to Network Management System (NMS). Subsequently, the SCP controller

[0204] may generate a recommendation for a scale-out action. The recommendation may include detailed specifications, including for example, the site location and the types of network functions (NF) that will be supported by the additional SCP Proxy nodes required to manage the excess load. The generated recommendation and the alert are transmitted to the Network Management System (NMS) through the notification targets

[0212] , For example, the recommendation includes the specific site where the new SCP Proxy should be placed and specifies that it should support NF-C [210C] type functions, which are currently under heavy demand. The NMS receives this recommendation through the notification targets

[0212] , thereby allowing taking swift action to scale out and balance the network load effectively. Notification target refers to an entity or a group of entities (such as a dedicated person or a group of persons) that facilitates in taking the scale out decision. The entity or group of entities would be communicated, via email and SMS etc., of the breach of the current load data. Further, scale out may be shown as alarm on NMS and it would be visible to the entity or the group of entities.

[0081] It would be appreciated by the person skilled in the art that the system architecture

[0200] ensures that as the network faces varying loads, proactive measures are taken to scale the network resources accordingly to maintain an optimal user experience by preventing network overload and managing the distribution of traffic across the network infrastructure.

[0082] Referring to FIG. 3, an exemplary method flow diagram

[0300] for scaling up network nodes is shown, in accordance with exemplary implementations of the present invention. In an implementation the method

[0300] is performed by the system

[0100] , the system architecture

[0200] or the SCP controller

[0204] , As shown in FIG. 3, the method

[0300] starts at step

[0302] ,

[0083] At step

[0304] , the method

[0300] as disclosed by the present disclosure comprises receiving, by a receiving unit

[0102] , a current load data associated with each of a plurality of network nodes. The current load data may include but not limited to information associated with existing users and new users, real-time traffic, Transaction Per Second (TPS), key performance indicators, Metric, metadata associated with signals, the traffic volume, types of services being accessed, and the number of active users at any given time on each of the plurality of network nodes.

[0084] Next, at step

[0306] , the method

[0300] as disclosed by the present disclosure comprises predicting, by a processing unit

[0108] using a trained model

[0206] , a load threshold value for each of the plurality of network nodes. The trained model

[0206] may include but not limited to artificial intelligence and machine learning techniques. The trained model

[0206] may be trained based on a historical set of data associated with the plurality of network nodes. The historical set of data comprises past traffic load patterns, traffic distribution trends, peak traffic times, and historical overload events at the plurality of network nodes. By analyzing the historical data, the trained model

[0206] can accurately predict the load threshold value for each network node. The predicted load threshold value may represent the point at which the network node is expected to become overloaded.

[0085] Next, at step

[0308] , the method

[0300] as disclosed by the present disclosure comprises comparing, by a comparing unit

[0104] , the current load data with the corresponding load threshold value of each of the plurality of network nodes to forecast overload conditions at the plurality of network nodes. By continuously comparing the current load data with the corresponding predicted threshold, the comparing unit

[0104] can identify instances where the network node is approaching or exceeding their load capacity limits. This early detection of overload conditions allows for timely intervention and scaling actions to be taken, thereby ensuring the smooth functioning of the network and maintaining an optimal user experience. In an example, SCP Proxy 1 [202A] predicted load threshold of 1000 transactions per second (TPS). SCP controller

[0204] receives the current load data, which indicates that SCP Proxyl [202A] is currently handling 950 TPS. As the SCP controller

[0204] continues to monitor the load, it observes an increase to 1020 TPS, surpassing the threshold. This comparison, performed by the comparing unit

[0104] , identifies an overload condition on SCP Proxyl [202A], The SCP controller

[0204] can then alert the Network Management System (NMS) to take appropriate scaling actions, such as adding additional SCP proxies or redistributing traffic, to prevent potential service degradation and maintain a seamless user experience.

[0086] Next, at step

[0310] , the method

[0300] as disclosed by the present disclosure comprises alerting, by an alerting unit

[0106] , a Network Management System (NMS) to scale up the network nodes in an event the current load data breaches the corresponding load threshold value of the plurality of network nodes. For example, if the current load data on SCP Proxy 1 [202A] exceeds its load threshold value, the alerting unit

[0106] sends an alert to the Network Management System (NMS). The NMS then initiates actions to add additional SCP proxies or enhance the capacity of existing proxies to manage the increased traffic, ensuring uninterrupted service for users.

[0087] Thereafter, the method terminates at step

[0312] .

[0088] In an exemplary implementation of the present disclosure, the each of the plurality of network nodes is the SCP of the 5th Generation (5G) network.

[0089] In an exemplary implementation of the present disclosure, the trained model is the ML based model.

[0090] In an exemplary implementation of the present disclosure, the set of data associated with the plurality of network nodes comprises information about increase and decrease of traffic at the plurality of network nodes, information about peak traffic data and low traffic data at the plurality of network nodes in past, historical trend of traffic at the plurality of network nodes, reason and causes of increase and decrease of traffic at the plurality of network nodes.

[0091] In an exemplary implementation of the present disclosure, the method

[0300] further comprises notifying, by the processing unit

[0108] , the network node scale-up data to the NMS, wherein the network node scale-up data comprises site details, network function (NF) type details, number of required network nodes.

[0092] In an exemplary implementation of the present disclosure, the scale-up of the network nodes corresponds to the addition of at least one SCP node in the 5G network.

[0093] In an example, the Network Management System may include entities associated with the management of the network issues such as network managing users, managing teams, network handing teams, platforms and the like.

[0094] In an exemplary implementation of the present disclosure, the alerting may include sending a notification on the device of the network management team. The network management team after receiving the notification may plan to scale up the virtual machines or nodes or proxy in order to meet the future demands to access the network.

[0095] As is evident from the above, the present disclosure provides a technically advanced solution for handling the overload conditions and accordingly notify or alert the NMS. Thus, thepresent disclosure overall efficiently monitors the user experience, reliably and seamlessly mange the handling of overload conditions, meet the public demands, and the like. Further, the present disclosure using Al at SCP level predicts future overload condition beforehand based on historical trends and thus provide scale-out notification and consent-based scale-out to meet the requirement of the new users. Also, the present disclosure provides site & supporting NF recommendations for scaling-out SCP Proxy.

[0096] FIG. 4 illustrates an exemplary block diagram of a computing system

[0400] upon which an embodiment of the present disclosure may be implemented. In an implementation, the computing device implements the method for scaling up network nodes using the system

[0100] , In another implementation, the computing device itself implements the method for scaling up network nodes in 5G core (5GC) network by using one or more units configured within the computing device, wherein said one or more units are capable of implementing the features as disclosed in the present disclosure.

[0097] The computer system

[0400] encompasses a wide range of electronic devices capable of processing data and performing computations. Examples of computer system

[0400] include, but are not limited only to, personal computers, laptops, tablets, smartphones, user equipment (UE), servers, and embedded systems. The devices may operate independently or as part of a network and can perform a variety of tasks such as data storage, retrieval, and analysis. Additionally, computer system

[0400] may include peripheral devices, such as monitors, keyboards, and printers, as well as integrated components within larger electronic systems, showcasing their versatility in various technological applications.

[0098] The computer system

[0400] may include a bus

[0402] or other communication mechanism for communicating information, and a processor

[0404] coupled with bus

[0402] for processing information. The processor

[0404] may be, for example, a general-purpose microprocessor. The computer system

[0400] may also include a main memory

[0406] , such as a random-access memory (RAM), or other dynamic storage device, coupled to the bus

[0402] for storing information and instructions to be executed by the processor

[0404] , The main memory

[0406] also may be used for storing temporary variables or other intermediate information during execution of the instructions to be executed by the processor

[0404] . Such instructions, when stored in non-transitory storage media accessible to the processor

[0404] , render the computer system

[0400] into a special-purpose machine that is customized to perform the operations specified in the instructions. The computer system

[0400] further includes a read only memory (ROM)

[0408] or other static storage device coupled to the bus

[0402] for storing static information and instructions for the processor

[0404] .

[0099] A storage device

[0410] , such as a magnetic disk, optical disk, or solid-state drive is provided and coupled to the bus

[0402] for storing information and instructions. The computer system

[0400] may be coupled via the bus

[0402] to a display

[0412] , such as a cathode ray tube (CRT), for displaying information to a computer user. An input device

[0414] , including alphanumeric and other keys, may be coupled to the bus

[0402] for communicating information and command selections to the processor

[0404] . Another type of user input device may be a cursor control

[0416] , such as a mouse, a trackball, or cursor direction keys, for communicating direction information and command selections to the processor

[0404] , and for controlling cursor movement on the display

[0412] , 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.

[0100] The computer system

[0400] 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

[0400] causes or programs the computer system

[0400] to be a special-purpose machine. According to one embodiment, the techniques herein are performed by the computer system

[0400] in response to the processor

[0404] executing one or more sequences of one or more instructions contained in the main memory

[0406] . Such instructions may be read into the main memory

[0406] from another storage medium, such as the storage device

[0410] , Execution of the sequences of instructions contained in the main memory

[0406] causes the processor

[0404] to perform the process steps described herein. In alternative embodiments, hardwired circuitry may be used in place of or in combination with software instructions.

[0101] The computer system

[0400] also may include a communication interface

[0418] coupled to the bus

[0402] , The communication interface

[0418] provides a two-way data communication coupling to a network link

[0420] that is connected to a local network

[0422] , For example, the communication interface

[0418] 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

[0418] 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

[0418] sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

[0102] The computer system

[0400] can send messages and receive data, including program code, through the network(s), the network link

[0420] and the communication interface 418. In the Internet example, a server

[0430] might transmit a requested code for an application program through the Internet

[0428] , the Internet Service Provider (ISP)

[0426] , the local network

[0422] andthe communication interface

[0418] , The received code may be executed by the processor

[0404] as it is received, and / or stored in the storage device

[0410] , or other non-volatile storage for later execution.

[0103] An aspect of the present disclosure relates to a non-transitory computer-readable storage medium storing instruction for scaling up network nodes, the storage medium comprising executable code which, when executed by one or more units of a system, causes: a receiving unit

[0102] to receive a current load data associated with each of a plurality of network nodes; a processing unit

[0108] to predict, via a trained model

[0206] , a load threshold value for each of the plurality of network nodes; a comparing unit

[0104] to compare the current load data with the corresponding load threshold value of each of the plurality of network nodes to forecast overload conditions at the plurality of network nodes; and an alerting unit

[0106] to alert a Network Management System (NMS) to scale up the network nodes in an event the current load data breaches the corresponding load threshold value of the plurality of network nodes.

[0104] The present disclosure aims to overcome the problems in this field of technology by introducing a method and system that enhances the scalability management of 5G network nodes, particularly Service Communication Proxies (SCPs). The method significantly improves upon the prior art by integrating a predictive mechanism that uses artificial intelligence to anticipate and address overload conditions before they lead to network congestion and service degradation. In the disclosed method, a receiving unit collects current load data from a plurality of network nodes, which include SCPs for determining real-time network usage and potential stress points. A processing unit, using a trained model, predicts a load threshold value for each network node. The predictive capability of the trained model is a substantial improvement over prior systems as it is based on a historical set of data comprising past traffic load patterns, traffic distribution trends, peak traffic times, and historical overload events. This means the system can recognize potential overload conditions much earlier. When the comparing unit assesses the current load data against the load threshold values and identifies a potential overload situation, it does not simply wait for the congestion to occur. Instead, an alerting unit proactively informs the Network Management System (NMS) of the need to scale up the network nodes. This early warning system enables the NMS to implement scale-up measures in a timely fashion, thereby avoiding the reactive delays seen in prior art systems.

[0105] 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 thedisclosure. 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.

[0106] 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 Claim1. A method for scaling up network nodes, the method comprising: receiving, by a receiving unit [102], a current load data associated with each of a plurality of network nodes; predicting, by a processing unit [108] using a trained model [206], a load threshold value for each of the plurality of network nodes; comparing, by a comparing unit [104], the current load data with the corresponding load threshold value of each of the plurality of network nodes to forecast overload conditions at the plurality of network nodes; and alerting, by an alerting unit [106], a Network Management System (NMS) to scale up the network nodes in an event the current load data breaches the corresponding load threshold value of the plurality of network nodes.

2. The method as claimed in claim 1, wherein each of the plurality of network nodes is a Service Communication Proxy (SCP) of a 5thGeneration (5G) network.

3. The method as claimed in claim 1, wherein the trained model [206] is trained based on of a historical set of data associated with the plurality of network nodes, the historical set of data comprises past traffic load patterns, traffic distribution trends, peak traffic times, and historical overload events at the plurality of network nodes.

4. The method as claimed in claim 1, wherein the trained model [206] is an artificial intelligence (Al) based model.

5. The method as claimed in claim 1, wherein the current load data associated with the plurality of network nodes comprises information indicative of increase and decrease of traffic at the plurality of network nodes, information indicative of peak traffic data and low traffic data at the plurality of network nodes in past, historical trend of traffic at the plurality of network nodes, reason and causes of increase and decrease of traffic at the plurality of network nodes.

6. The method as claimed in claim 1, further comprises notifying, by the processing unit [108], network node scale-up data to the NMS, wherein the network node scale-up data comprises site details, network function (NF) type details, number of required network nodes.

7. The method as claimed in claim 6, wherein the scale-up corresponds to addition of at least one SCP node in the 5G network.

8. A system for scaling up network nodes, said system comprising: a receiving unit [102] configured to receive a current load data associated with each of a plurality of network nodes; a processing unit [108] configured to predict, viaatrained model [206], a load threshold value for each of the plurality of network nodes; a comparing unit [104] configured to compare the current load data with the corresponding load threshold value of each of the plurality of network nodes to forecast overload conditions at the plurality of network nodes; and an alerting unit [106] configured to alert a Network Management System (NMS) to scale up the network nodes in an event the current load data breaches the corresponding load threshold value of the plurality of network nodes.

9. The system as claimed in claim 8, wherein each of the plurality of network nodes is a Service Communication Proxy (SCP) of a 5thGeneration (5G) network.

10. The system as claimed in claim 8, wherein the trained model [206] is trained based on of a historical set of data associated with the plurality of network nodes, the historical set of data comprises past traffic load patterns, traffic distribution trends, peak traffic times, and historical overload events at the plurality of network nodes.

11. The system as claimed in claim 8, wherein the trained model [206] is an artificial intelligence (Al) based model.

12. The system as claimed in claim 8, wherein the current load data associated with the plurality of network nodes comprises information indicative of increase and decrease of traffic at the plurality of network nodes, information indicative of peak traffic data and low traffic data at the plurality of network nodes in past, historical trend of traffic at the plurality of network nodes, reason and causes of increase and decrease of traffic at the plurality of network nodes.

13. The system as claimed in claim 8, wherein the processing unit [108] is configured to notify network node scale-up data to the NMS, wherein the network node scale-up data comprises site details, network function (NF) type details, number of required network nodes.

14. The system as claimed in claim 13, wherein the network node scale-up data corresponds to addition of at least one SCP node in the 5G network.

15. A non-transitory computer-readable storage medium storing instruction for scaling up network nodes, the storage medium comprising executable code which, when executed by one or more units of a system, causes: a receiving unit [102] to receive a current load data associated with each of a plurality of network nodes; a processing unit [108] to predict, via a trained model [206], a load threshold value for each of the plurality of network nodes; a comparing unit [104] to compare the current load data with the corresponding load threshold value of each of the plurality of network nodes to forecast overload conditions at the plurality of network nodes; and an alerting unit [106] to alert a Network Management System (NMS) to scale up the network nodes in an event the current load data breaches the corresponding load threshold value of the plurality of network nodes.

Images