FORM 2
THE PATENTS ACT, 1970 (39 OF 1970) & THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
“METHOD AND SYSTEM FOR OUTLIER DETECTION AND ALTERNATE ROUTE SUGGESTION”
We, Jio Platforms Limited, an Indian National, of Office - 101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India.
The following specification particularly describes the invention and the manner in which it is to be performed.

METHOD AND SYSTEM FOR OUTLIER DETECTION AND ALTERNATE ROUTE SUGGESTION
5 FIELD OF INVENTION [0001] The present disclosure relates generally to the field of wireless communication systems. More particularly, the present disclosure relates to methods and systems for outlier detection and alternate route suggestion.
10 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
15 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
20 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
25 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
30 technology has become more advanced, sophisticated, and capable of delivering more services to its users.
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[0004] Existing solutions for routing outlier detection and alternate route suggestion have several shortcomings that limit their effectiveness in maintaining optimal performance and user experience in telecommunication networks. Firstly, conventional methods often rely on static routing mechanisms that lack the
5 flexibility to adapt to changing network conditions, resulting in suboptimal routing decisions. This can lead to prolonged periods of service degradation when network function failures occur, as these systems are slow to identify and respond to such issues. Furthermore, existing techniques employ rudimentary error detection methods that fail to leverage advanced analytical techniques, such as artificial
10 intelligence, to predict and mitigate potential network anomalies. This results in a reactive rather than proactive approach to network management, where problems are addressed only after they have already impacted user services. Additionally, current solutions do not provide a seamless mechanism for rerouting traffic to alternate network functions, leading to a lack of resilience in the face of network
15 failures.
[0005] Thus, there is an imperative need in the art to provide methods and systems for outlier detection and alternate route suggestion that mitigates the problems of the prior arts such as lack of adaptability, slow response to network 20 function failures, insufficient use of advanced analytical techniques, and inadequate mechanisms for traffic rerouting.
OBJECTS OF THE PRESENT DISCLOSURE [0006] Some of the objects of the present disclosure, which at least one 25 embodiment disclosed herein satisfies are listed herein below.
[0007] It is an object of the present disclosure to provide a system and method for outlier detection and alternate route suggestion.
30 [0008] It is another object of the present disclosure to provide a system and method for outlier detection and alternate route suggestion that offer a dynamic and
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adaptable approach to network routing, capable of quickly responding to network function failures and minimizing their impact on user services.
[0009] It is another object of the present disclosure to provide a system and 5 method for outlier detection and alternate route suggestion that leverage advanced analytical techniques, such as artificial intelligence, to predict network anomalies and thresholds, enabling proactive network management.
[00010] It is another object of the present disclosure to provide a system and 10 method for outlier detection and alternate route suggestion that facilitates seamless rerouting of network traffic to alternate network functions, ensuring resilience and continuity of service in the face of network failures.
[00011] It is another object of the present disclosure to provide a system and 15 method for outlier detection and alternate route suggestion that improve the overall key performance indicators (KPIs) of the network by enabling fast identification of network function failures and efficient traffic rerouting.
[00012] It is yet another object of the present disclosure to provide a system and 20 method for outlier detection and alternate route suggestion that enhance the end user experience by reducing the frequency and duration of service outliers and degradations in telecommunication networks.
SUMMARY 25 [00013] This section is provided to introduce certain aspects 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.
30 [00014] An aspect of the present method for outlier detection and alternate route suggestion. The method includes fetching, by a fetching unit, a status code from a
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response header received from a first remote server that corresponds to at least one producer network function (NF). The method further includes determining, by a determining unit, if the fetched status code corresponds to at least one error. The method further includes maintaining, by a storing unit, a count of errors
5 corresponding to each of the at least one error. The method further includes predicting, by a predicting unit using a trained model, at least one error threshold based on the count of errors corresponding to each of the at least one error. The method further includes comparing, by a comparator, the count of error with the corresponding predicted at least one error threshold. Thereafter, the method
10 includes determining, by a processing unit, a second remote server that corresponds to at least one another producer NF to serve network traffic associated with the at least one producer NF based on the comparison if the predicted at least one error threshold is breached.
15 [00015] In an aspect, the method comprises routing, by the processing unit, the network traffic from the at least one producer NF to the at least one another producer NF.
[00016] In an aspect, routing the network traffic is based on a user input. 20
[00017] In an aspect, the response header is Hypertext Transfer Protocol 2 (Http2).
[00018] In an aspect, the method further includes identifying, by an identifying 25 unit, a response timeout from the first remote server based on the determined at least one error. Further, the method includes maintaining, by the storing unit, a count of the identified response timeout.
[00019] In an aspect, predicting the at least one error threshold is based on at 30 least one key parameter.
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[00020] In an aspect, the at least one key parameter comprises at least one of a current remote server Transactions Per Second (TPS), a Key Performance Indicators (KPIs), a second remote server TPS, a second remote server KPIs, a load capacity of the first remote server, and a load capacity of the second remote server. 5 [00021] In an aspect, the trained model is trained based on a historical network data to enhance accuracy of prediction of the least one error.
[00022] Another aspect of the present disclosure provides a system for outlier
10 detection and alternate route suggestion. The system includes a fetching unit configured to fetch a status code from a response header received from a first remote server that corresponds to at least one producer network function (NF). The system further includes a determining unit configured to determine if the fetched status code corresponds to at least one error. The system further includes a storing unit
15 configured to maintain a count of errors corresponding to each of the at least one error. The system further includes a predicting unit configured to predict using a trained model, at least one error threshold based on the count of errors corresponding to each of the at least one error. The system further includes a comparator configured to compare the count of errors with the corresponding
20 predicted at least one error threshold. Further, the system includes a processing unit configured to determine a second remote server that corresponds to at least one another producer NF to serve network traffic associated with the at least one producer NF based on the comparison if the predicted at least one error threshold is breached.
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[00023] Yet another aspect of the present disclosure provides a non-transitory computer-readable storage medium storing instructions for outlier detection and alternate route suggestion. The storage medium comprising executable code which, when executed by one or more units of a system, causes: a fetching unit [202] to
30 fetch a status code from a response header received from a first remote server that corresponds to at least one producer network function (NF); a determining unit
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[204] to determine if the fetched status code corresponds to at least one error; a storing unit [206] to maintain a count of error corresponding to each of the at least one error; a predicting unit [208] to predict using a trained model [304], at least one error threshold based on the count of error corresponding to each of the at least one
5 error; a comparator [210] to compare the count of error with the corresponding predicted at least one error threshold; and based on the comparison if the predicted at least one error threshold is breached, a processing unit [212] to determine a second remote server that corresponds to at least one another producer NF to serve network traffic associated with the at least one producer NF.
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BRIEF DESCRIPTION OF DRAWINGS [00024] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same
15 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
20 drawings includes disclosure of electrical components, electronic components or circuitry commonly used to implement such components.
[00025] FIG. 1 illustrates an exemplary block diagram representation of 5th generation core (5GC) network architecture. 25
[00026] FIG. 2 illustrates an exemplary block diagram of a system for outlier detection and alternate route suggestion, in accordance with the exemplary implementations of the present disclosure.
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[00027] FIG. 3 illustrates an exemplary block diagram of an architecture for implementation of a system for outlier detection and alternate route suggestion, in accordance with exemplary implementations of the present disclosure. [00028] FIG. 4 illustrates an exemplary method flow diagram indicating the 5 process for outlier detection and alternate route suggestion, in accordance with exemplary embodiments of the present disclosure.
[00029] FIG. 5 illustrates an exemplary block diagram of a computing device upon which an embodiment of the present disclosure may be implemented. 10
[00030] The foregoing shall be more apparent from the following more detailed description of the disclosure.
DETAILED DESCRIPTION
15 [00031] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one
20 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 embodiments of the present disclosure are described below, as illustrated in various drawings in
25 which reference numerals refer to the same parts throughout the different drawings.
[00032] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those 30 skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the
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function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.
[00033] It should be noted that the terms "mobile device", "user equipment", "user device", “communication device”, “device” and similar terms are used
5 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 embodiments. 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
10 or variations thereof may be used interchangeably without departing from the scope of the invention as defined herein.
[00034] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one
15 of ordinary skills in the art that the embodiments 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 embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without
20 unnecessary detail in order to avoid obscuring the embodiments.
[00035] Also, it is noted that individual embodiments 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 25 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.
30 [00036] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the
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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
5 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.
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[00037] 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
15 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
20 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
25 the art for implementation of the features of the present disclosure.
[00038] 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 30 processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in
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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 5 the system according to the present disclosure. More specifically, the processor is a hardware processor.
[00039] As portable electronic devices and wireless technologies continue to improve and grow in popularity, the advancing wireless technologies for data
10 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),
15 and more such generations are expected to continue in the forthcoming time.
[00040] 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
20 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
25 (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
30 resources. The invention herein relates to the situations when the user equipment (UE) operates in the fifth generation (5G) communication system.
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[00041] As discussed in the background section, existing solutions for routing outlier detection and alternate route suggestion have several shortcomings that limit their effectiveness in maintaining optimal performance and user experience in telecommunication networks. Firstly, conventional methods often rely on static
5 routing mechanisms that lack the flexibility to adapt to changing network conditions, resulting in suboptimal routing decisions. This can lead to prolonged periods of service degradation when network function failures occur, as these systems are slow to identify and respond to such issues. Furthermore, existing approaches typically employ rudimentary error detection methods that fail to
10 leverage advanced analytical techniques, such as artificial intelligence, to predict and mitigate potential network anomalies. This results in a reactive rather than proactive approach to network management, where problems are addressed only after they have already impacted user services. Additionally, current solutions do not provide a seamless mechanism for rerouting traffic to alternate network
15 functions, leading to a lack of resilience in the face of network failures.
[00042] The present disclosure aims to overcome the above-mentioned and other existing problems in this field of technology.
20 [00043] The present disclosure addresses the shortcomings of existing solutions for routing outlier detection and alternate route suggestion in telecommunication networks by providing a method system that significantly enhances network performance and user experience. By leveraging advanced analytical techniques, such as artificial intelligence, the disclosed method facilitates in predicting network
25 anomalies and thresholds, enabling proactive network management and swift response to potential issues. The present disclosure introduces a fetching unit that retrieves status codes from response headers received from remote servers, allowing for the immediate identification of errors. A determining unit further assesses whether the status codes correspond to errors, while a storing unit maintains a count
30 of these errors. The real-time monitoring and error tracking enables quick detection of network function failures, a key advantage over traditional static routing
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mechanisms. A predicting unit, utilizing a trained model, predicts error thresholds based on the count of errors, allowing for a proactive approach to network management. The method then compares the count of errors with the predicted thresholds using a comparator. If a threshold is breached, indicating a potential
5 network anomaly, a processing unit determines an alternate remote server that can serve the network traffic with minimal failures, thereby ensuring continuity of service and reducing the impact of network failures on user experience. The present disclosure also provides flexibility in routing network traffic, with the processing unit capable of rerouting traffic based on user input, ensuring user consent in the
10 rerouting process. Additionally, the method includes identifying response timeouts and maintaining a count of these timeouts, further enhancing the system's ability to detect and respond to network issues. By predicting error thresholds based on key parameters such as current remote server Transactions Per Second (TPS), Key Performance Indicators (KPIs), and the load capacity of remote servers, the present
15 disclosure offers a more nuanced and effective approach to network management. The trained model, based on historical network data, enhances the accuracy of predictions, ensuring that the system can adapt to changing network conditions and maintain optimal performance.
20 [00044] It would be appreciated by the person skilled in the art that the present disclosure provides a solution to the problem of routing outlier detection and alternate route suggestion in telecommunication networks. By leveraging artificial intelligence and real-time monitoring, the present disclosure offers a proactive, adaptable, and user-centric approach to network management, significantly
25 improving overall network performance and user experience.
[00045] Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the accompanying drawings.
30 [00046] FIG. 1 illustrates an exemplary block diagram representation of 5th generation core (5GC) network architecture, in accordance with exemplary
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embodiment 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],
5 an Authentication Server Function (AUSF) [112], a Network Slice Specific Authentication and Authorization Function (NSSAAF) [114], a Network Slice Selection Function (NSSF) [116], a Network Exposure Function (NEF) [118], a Network Repository Function (NRF) [120], a Policy Control Function (PCF) [122], a Unified Data Management (UDM) [124], an application function (AF) [126], a
10 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.
[00047] Radio Access Network (RAN) [104] is the part of a mobile 15 telecommunications system that connects User Equipment (UE) [102] 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.
20 [00048] 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.
25 [00049] 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 Internet Protocol (IP) address allocation and Quality of service (QoS) enforcement.
30
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[00050] Service Communication Proxy (SCP) [110]: Service Communication Proxy (SCP) in the 5G system architecture is configured to offer a variety of functions related to network communication. The functions include indirect communication, delegated discovery, and message routing and forwarding to
5 destination network functions or other SCPs. Additionally, SCP supports communication security aspects like authorization, load balancing, monitoring, and overload control. SCPs can also interact with UDR, to resolve the UDM Group ID/UDR Group ID/AUSF Group ID/PCF Group ID/CHF Group ID/HSS Group ID based on UE identity, e.g. SUPI or IMPI/IMPU. SCPs can be deployed at PLMN
10 level, shared-slice level and slice-specific level. The SCPs can communicate with relevant NRFs. In order to enable SCPs to route messages through several SCPs, an SCP may register its profile in the NRF. Alternatively, local configuration may be used. 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,
15 communication security (e.g., authorization of the NF Service Consumer to access the NF Service Producer API), load balancing, monitoring, overload control and the like.
[00051] Authentication Server Function (AUSF) [112] is a network function in 20 the 5G core responsible for authenticating UEs during registration and providing security services. It generates and verifies authentication vectors and tokens.
[00052] Network Slice Specific Authentication and Authorization Function (NSSAAF) [114] is a network function that provides authentication and 25 authorization services specific to network slices. It ensures that UEs can access only the slices for which they are authorized.
[00053] Network Slice Selection Function (NSSF) [116] is a network function responsible for selecting the appropriate network slice for a UE based on factors 30 such as subscription, requested services, and network policies.
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[00054] 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.
5 [00055] 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.
[00056] Policy Control Function (PCF) [122] is a network function responsible 10 for policy control decisions, such as QoS, charging, and access control, based on subscriber information and network policies.
[00057] Unified Data Management (UDM) [124] is a network function that centralizes the management of subscriber data, including authentication, 15 authorization, and subscription information.
[00058] Application Function (AF) [126] is a network function that represents external applications interfacing with the 5G core network to access network capabilities and services. 20
[00059] User Plane Function (UPF) [128] is a network function responsible for handling user data traffic, including packet routing, forwarding, and QoS enforcement.
25 [00060] Data Network (DN) [130] represents external networks or services that users connect to through the mobile network, such as the internet or enterprise networks.
[00061] Referring to FIG. 2, an exemplary block diagram of a system [200] for
30 outlier detection and alternate route suggestion is shown, in accordance with the
exemplary implementations of the present disclosure. The system [200] comprises
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a fetching unit [202], a determining unit [204], a storing unit [206], a predicting unit [208], a comparator [210], a processing unit [212] and an identifying unit [214]. 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
5 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 at a network level to implement the features of the present disclosure. In an implementation, the system [200] may reside in a server, a network
10 entity or a SCP controller [302] as disclosed in FIG. 3.
[00062] The system [200] includes the fetching unit [202], that is configured to retrieve a status code from a response header received from a first remote server. The first remote server is associated with at least one producer network function
15 (NF) within a telecommunication network. The response header is a part of the data packet sent by the first remote server in response to a request. The status code may indicate the outcome of the request, such as whether the request was successful, if there was an error, and the type of error if one occurred. The fetching unit [202] is configured to extract the status code from the response header, enabling the system
20 [200] to assess the health and performance of the first remote server and to detect any anomalies or errors that may require further action, such as rerouting network traffic to an alternative server.
[00063] The system [200] further includes the determining unit [204]. The 25 determining unit [204] may be communicatively coupled to the fetching unit [202] such that to receive the fetched status code. The determining unit [204] is configured to determine if the fetched status code corresponds to at least one error. The determining unit [204] analyses the status code to identify whether it represents a normal operation or an error condition. If the status code indicates an error, such 30 as a server failure or a communication issue, the determining unit [204] recognizes this as an error condition. The at least one error can include at least one error
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response code, such as 408 (which corresponds to a request timeout), 429 (indicating too many requests), 500 (indicating an internal server error), and 505 (indicating a Hypertext Transfer Protocol (HTTP) version not supported). These error codes are standard HTTP2 response status codes that indicate various types of 5 errors encountered by the server while processing the request. By identifying these error codes, the determining unit [204] can ascertain whether there has been a failure in the network function or server.
[00064] The disclosure specifically details the use of the HTTP2 protocol for 10 the described functionalities and systems. However, it should be understood that the principles and operations described herein are equally applicable to other communication protocols. The choice of HTTP2 is for illustration purposes only and does not limit the applicability of the described technologies to HTTP2 alone. Other protocols, both current and future, which facilitate similar functionalities and 15 achieve similar outcomes are considered within the scope of the disclosure.
[00065] The system [200] further includes the storing unit [206] communicatively coupled to the determining unit [204]. The storing unit [206] is configured to maintain a count of errors corresponding to each of the at least one
20 error detected by the determining unit [204]. The storing unit [206] keeps track of the number of times each specific type of error has occurred. For example, if the determining unit [204] identifies an error in the status code fetched by the fetching unit [202], the storing unit [206] increments the count associated with that particular error. Thus, monitoring the frequency of different errors to help in identifying
25 patterns or trends in the network's performance. By maintaining a count of errors, the system [200] can facilitate in assessing the severity and impact of various issues on the network, enabling more informed decision-making for troubleshooting and network optimization.
30 [00066] The system [200] further includes the predicting unit [208] communicatively coupled to the storing unit [206]. The predicting unit [208] is
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configured to predict, using a trained model, at least one error threshold based on the count of errors corresponding to each of the at least one error stored at the storing unit [206]. The trained model is trained based on historical network data, including past occurrences of errors, their frequency, and the conditions under
5 which they occurred. The trained model may utilize machine learning techniques to analyse data corresponding to count of errors and identify patterns or trends that can be used to predict the at least one error threshold. The predicted at least one threshold represents the limits beyond which the occurrence of an error is considered abnormal or indicative of a potential network issue. By predicting the
10 error thresholds, the predicting unit [208] enables the system [200] to identify when the network is approaching or has exceeded the limits, allowing for timely intervention and mitigation of potential network failures or disruptions.
[00067] The predicting unit [208] is further configured to predict the at least one 15 error threshold based on at least one key parameter to assess the current state and performance of the network, and to make informed predictions about potential errors. The at least one key parameter can include a variety of factors that are indicative of the network's health and efficiency.
20 [00068] Another key parameter is the Key Performance Indicators (KPIs). KPIs are a set of quantifiable measurements used to gauge the overall performance of the network. They can include metrics such as latency, packet loss, transactions per second (TPS), and throughput. These indicators help in assessing the quality of service provided by the network.
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[00069] Additionally, the Key Performance Indicators (KPIs) of the second remote server are taken into account. These KPIs provide a comprehensive view of the second server's ability to handle network traffic and its overall performance, which is crucial for making informed decisions about routing traffic in case of an
30 error.
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[00070] The load capacity of the first remote server is another key parameter. This refers to the maximum amount of traffic or workload that the server can handle without compromising its performance. Understanding the load capacity helps in assessing whether the server is operating within its limits or if it is at risk of being 5 overloaded.
[00071] Lastly, the load capacity of the second remote server is considered as a key parameter. This metric is important for evaluating the alternative server's capability to handle additional traffic in case the primary server encounters an error. 10 It ensures that the backup server has sufficient capacity to provide uninterrupted service.
[00072] By analysing the key parameters, the predicting unit [208] can accurately predict the error threshold, which indicates when the system should 15 consider rerouting traffic to an alternate server to maintain optimal network performance and minimize disruptions.
[00073] The system [200] further includes the comparator [210] communicatively coupled to the predicting unit [208] and the storing unit [206].
20 The comparator [210] is configured to compare the count of error, maintained by the storing unit [206], with the corresponding predicted at least one error threshold determined by the predicting unit [208]. The comparison facilitates in assessing whether the current network conditions have exceeded the normal operational thresholds, indicating a potential issue that needs to be addressed. If the count of a
25 particular error exceeds the predicted threshold, it suggests that the error is occurring more frequently than expected under normal conditions, signalling an outlier event or a network anomaly. In such cases, the comparator [210] triggers an alert or initiates further actions, such as routing adjustments or notifications to network administrators, to mitigate the detected issue and ensure the continued
30 reliability and performance of the telecommunication network.
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[00074] The system [200] further includes the processing unit [212] communicatively coupled to the comparator [210]. The processing unit [212] is configured to determine a second remote server that corresponds to at least one another producer NF to serve network traffic associated with the at least one
5 producer NF based on the comparison of the count of error with the corresponding predicted at least one error threshold. If the comparator [210] determines that the predicted error threshold is breached, the processing unit [212] identifies an alternative routing path. The processing unit [212] assesses available set of second remote servers that can potentially take the traffic from the affected producer NF to
10 ensure continuous and uninterrupted network services. The selection of the second remote server is based on various criteria such as its current load, capacity, proximity to the source of traffic, and performance indicators. By rerouting the traffic to the second remote server, the system [200] aims to minimize the impact of the detected error on the network's overall performance and maintain a high
15 quality of service for the users. This dynamic rerouting capability enhances the resilience and reliability of the telecommunication network by providing a quick and efficient response to potential outages or anomalies.
[00075] The system [200] further comprises the identifying unit [214] 20 configured to identify a response timeout from the first remote server based on the determined at least one error. When the status code fetched by the fetching unit [202] suggests a failure or delay in the server's response, the identifying unit [214] identifies the error as a response timeout. Further, the storing unit [206], is configured to maintain a record of each identified response timeout. 25
[00076] In an exemplary embodiment, the trained model [304] utilizes artificial intelligence (AI) to analyze historical data over a defined period (such as the past month or year), comparing the current percentage of timeouts with historical values. The analysis facilitates proactive network management decisions, particularly in 30 adjusting routing protocols to maintain service continuity. Additionally, the trained model is equipped to assess past data concerning HTTP 408 (Request Timeout)
21

errors. By establishing predictive thresholds from this data, the system can pre-emptively detect and respond to unacceptable error rates, thereby enabling automated or semi-automated rerouting processes to optimize network performance and user experience. For example, if over the past year the timeout error rate during 5 peak times was 5%, and the current rate unexpectedly rises to 7%, the model identifies this as an anomaly. It predicts a threshold of 6%, beyond which it triggers automatic rerouting of traffic to less burdened servers to alleviate the overload and maintain optimal network performance.
10 [00077] In an exemplary embodiment, thresholds for different types of network errors can be established either manually through a configuration file or automatically using AI. Manual configuration involves setting predefined thresholds based on historical data, anticipated network traffic, and risk assessments. For example, a network administrator might decide that more than 50
15 HTTP 500 (Internal Server Error) occurrences per hour should trigger an alert for potential server issues. On the other hand, AI-driven configuration leverages historical analysis over periods like the past month or year to dynamically adjust thresholds. This method allows the system to adapt to changing network behaviors and conditions. For instance, if an AI model observes that HTTP 408 (Request
20 Timeout) errors typically peak at 5% during high-traffic periods and then detects a spike to 8%, it can automatically initiate measures such as increasing server capacity or rerouting traffic to maintain network stability and performance. This flexible, data-driven approach ensures that the network can efficiently respond to anomalies and maintain high service quality.
25
[00078] Referring to FIG. 3, an exemplary block diagram of a system architecture [300] for outlier detection and alternate route suggestion is shown, in accordance with the exemplary implementations of the present disclosure. The system architecture [300] comprises a Service Communication Proxy (SCP)
30 controller [302], a trained model [304], a network interface [306], a Session Management Function (SMF) [108], a Policy Control Function (PCF 1) [122A], a
22

PCF 2 and the SCP [110]. Also, all of the components/ units of the system architecture [300] are assumed to be connected to each other unless otherwise indicated below. Also, in FIG. 3 only a few units are shown, however, the system architecture [300] may comprise multiple such units or the system architecture 5 [300] may comprise any such numbers of said units, as required to implement the features of the present disclosure.
[00079] As illustrated, the SCP [110] receives a response header (such as Http2) from a first producer NF (such as PCF 1 [122A]). The response header, containing
10 status codes, is then transmitted to the SCP controller [302]. The SCP controller [302] extracts the status code from the response header, which serves as an indicator of the network function's health and performance. If this status code reflects an error, the SCP controller [302] determines the error and also maintains a record of such errors, effectively keeping a count for trend analysis and pattern recognition.
15
[00080] The network interface [306] facilitates in communication between the network components and administrator, ensuring seamless data exchange and coordination across the telecommunication network. The SMF [108] manages session establishment, maintenance, and termination for maintaining continuous
20 and efficient network service delivery.
[00081] In an alternative embodiment, the SCP controller [302], the SCP [110], and the trained model [304] are merged together to form a SCP entity. The unified SCP entity facilitates in simplifying the overall design of the system architecture 25 [300]. Further, the SCP entity facilitates in enhancing performance, and reducing latency by eliminating the need for inter-component communication over the network.
[00082] The trained model [304] continuously collects error/timeout counters 30 from all SCP instances across the network. The collection involves receiving real¬time traffic data flow from each SCP instance such that the trained model is
23

equipped with the latest network performance metrics. The continuous data flow allows the trained model to maintain an up-to-date and comprehensive view of the network's health, facilitating more accurate predictions and timely rerouting decisions.
5 [00083] Using the trained model [304], the SCP controller [302] then predicts error thresholds that correspond to tolerable limits of error occurrences. The trained model [304] continuously collects error/timeout counters from a plurality of SCP instances across the network. The collection comprises receiving real-time traffic
10 data flow from each SCP instance of the plurality of SCP instances. Thus, the trained model is equipped with the latest network performance metrics. The continuous data flow allows the trained model to maintain an up-to-date and comprehensive view of the network's health, facilitating more accurate predictions and timely rerouting decisions. If the frequency of errors breaches the predicted
15 threshold, the SCP controller [302] is configured to reroute network traffic to ensure service continuity and to maintain the network's performance. Further, the SCP controller [302] identifies the second remote server (such as PCF 2 [122B]), as a suitable candidate to handle the network load of the first remote server (such as PCF 1 [122A], thereby mitigating the risk of service disruption and preserving the user
20 experience. The suitable candidate is selected based on criteria such as current load, capacity, proximity to the source of traffic, and KPIs.
[00084] Referring to FIG. 4, an exemplary method flow diagram [400], for outlier detection and alternate route suggestion, in accordance with exemplary 25 embodiments of the present disclosure is shown. In an implementation, the method [400] is performed by the system [200], the system architecture [300], or the SCP controller [302]. As shown in FIG. 4, the method [400] starts at step [402].
[00085] At step [404], the method comprises fetching, by the fetching unit
30 [202], a status code from a response header received from a first remote server that
corresponds to at least one producer network function (NF). The fetching unit [202]
24

is configured to retrieve a status code that is included within the response header of data packets sent by a first remote server. The status code within the response header signifies the outcome of a request processed by the NF. It may indicate a successful operation, an error, or the type of error if it occurred. The extraction of 5 the status code by the fetching unit [202] allows the system [200] to evaluate the operation's success and promptly identify any errors or anomalies. If the response signifies an error, the system is thus alerted to a potential disruption in service or performance degradation.
10 [00086] Further, at step [406] the method comprises determining, by the determining unit [204], if the fetched status code corresponds to at least one error. The determining unit [204] is configured to determine if the fetched status code corresponds to at least one error. The determining unit [204] analyses the status code to identify whether it represents a normal operation or an error condition. If
15 the status code indicates an error, such as a server failure or a communication issue, the determining unit [204] recognizes this as an error condition. The at least one error can include at least one error response code, such as 408 (which corresponds to a request timeout), 429 (indicating too many requests), 500 (indicating an internal server error), and 505 (indicating an HTTP version not supported). These error
20 codes are standard HTTP response status codes that indicate various types of errors encountered by the server while processing the request. By identifying these error codes, the determining unit [204] can ascertain whether there has been a failure in the network function or server.
25 [00087] Further, at step [408] the method [400] comprises maintaining, by the storing unit [206], a count of errors corresponding to each of the at least one error. The storing unit [206] is configured to maintain a count of errors corresponding to each of the at least one error detected by the determination unit [204]. The storing unit [206] keeps track of the number of times each specific type of error has
30 occurred. For example, if the determining unit [204] identifies an error in the status code fetched by the fetching unit [202], the storing unit [206] increments the count
25

associated with that particular error. Thus, monitoring the frequency of different errors to help in identifying patterns or trends in the network's performance. By maintaining a count of errors, the system [200] can facilitate in assessing the severity and impact of various issues on the network, enabling more informed 5 decision-making for troubleshooting and network optimization.
[00088] Now, at step [410] the method comprises predicting, by the predicting unit [208] using a trained model [304], at least one error threshold based on the count of error corresponding to each of the at least one error. The trained model is
10 trained based on historical network data, including past occurrences of errors, their frequency, and the conditions under which they occurred. The trained model may utilize machine learning techniques to analyse data corresponding to count of errors and identify patterns or trends that can be used to predict the at least one error threshold. The predicted at least one threshold represents the limits beyond which
15 the occurrence of an error is considered abnormal or indicative of a potential network issue. By predicting the error thresholds, the predicting unit [208] enables the system [200] to proactively identify when the network is approaching or has exceeded these limits, allowing for timely intervention and mitigation of potential network failures or disruptions.
20
[00089] The predicting unit [208] is further configured to predict the at least one error threshold based on at least one key parameter to assess the current state and performance of the network, and to make informed predictions about potential errors. The at least one key parameter can include a variety of factors that are
25 indicative of the network's health and efficiency.
[00090] Another key parameter is the Key Performance Indicators (KPIs). KPIs are a set of quantifiable measurements used to gauge the overall performance of the network. They can include metrics such as latency, packet loss, transactions per 30 second (TCP), and throughput. These indicators help in assessing the quality of service provided by the network.
26

[00091] Additionally, the Key Performance Indicators (KPIs) of the second remote server are taken into account. These KPIs provide a comprehensive view of the second server's ability to handle network traffic and its overall performance, which is crucial for making informed decisions about routing traffic in case of an 5 error.
[00092] The load capacity of the first remote server is another key parameter. This refers to the maximum amount of traffic or workload that the server can handle without compromising its performance. Understanding the load capacity helps in 10 assessing whether the server is operating within its limits or if it is at risk of being overloaded.
[00093] Lastly, the load capacity of the second remote server is considered as a key parameter. This metric is important for evaluating the alternative server's 15 capability to handle additional traffic in case the primary server encounters an error. It ensures that the backup server has sufficient capacity to provide uninterrupted service.
[00094] Next, at step [412] the method comprises comparing, by the comparator 20 [210], the count of errors with the corresponding predicted at least one error threshold. The comparison facilitates in assessing whether the current network conditions have exceeded the normal operational thresholds, indicating a potential issue that needs to be addressed. If the count of a particular at least one error exceeds the corresponding predicted threshold, it suggests that the error is occurring more 25 frequently than expected under normal conditions, signalling an outlier event or a network anomaly. In such cases, the comparator [210] triggers an alert or initiates further actions, such as routing adjustments or notifications to network administrators, to mitigate the detected issue and ensure the continued reliability and performance of the telecommunication network.
30
27

[00095] Thereafter, at step [414] based on the comparison if the predicted at least one error threshold is breached the method [400] comprises determining, by a processing unit [212], a second remote server that corresponds to at least one another producer NF to serve network traffic associated with the at least one
5 producer NF. If the comparator [210] indicates that the predicted error threshold has been breached, the processing unit [212] activates its decision-making process to identify an alternative routing path. It assesses the set of available second remote servers that can potentially take over the traffic from the affected producer NF to ensure continuous and uninterrupted network services. The selection of the second
10 remote server is based on various criteria such as its current load, capacity, proximity to the source of traffic, and its performance indicators. By rerouting the traffic to the second server, the system [200] aims to minimize the impact of the detected error on the network's overall performance and maintain a high quality of service for the end users. This dynamic rerouting capability enhances the resilience
15 and reliability of the telecommunication network by providing a quick and efficient response to potential outages or anomalies.
[00096] It would be appreciated by the person skilled in the art that the aforementioned method is operational irrespective of the capability of the NF
20 instances to support capacity and current load reporting in their registration or heartbeat requests, thereby enabling proactive real-time monitoring and efficient management of network resources. i.e., technique of the proposed disclosure allows for real-time, proactive monitoring of network resources and their usage, helping to prevent potential network failures due to resource overload. It does so irrespective
25 of whether a network function supports capacity and current load reporting in its registration or heartbeat requests, thus ensuring more robust and reliable network performance.
[00097] The method further comprises identifying, by the identifying unit [214],
30 a response timeout from the first remote server based on the determined at least one
error. When the status code fetched by the fetching unit [202] suggests a failure or
28

delay in the server's response, the identifying unit [214] identifies the error as a response timeout. Further, the storing unit [206], is configured to maintain a record of each identified response timeout.
[00098] The method includes a step where the routing of network traffic is done 5 based on input from a user. This means that if there is a need to change the path of the network traffic, the system can do so with the user's permission or based on the user's instructions. In an embodiment, the user can provide an input through a graphical user interface (GUI) or a command or a voice-based input and the like.
10 [00099] The method terminates at step [416].
[000100] As is evident from the above, the present disclosure provides a technically advanced solution prediction of threshold for various KPI using artificial intelligence (AI) at any given time and conditions. Using that threshold 15 value to detect whether any anomaly has happened to the first Remote server. In case of anomaly, re-routing the message to second Remote server either automatically or consent based.
[000101] In an example, in a telecommunication network where the goal is to
20 detect outages quickly and suggest alternative routing paths to maintain seamless
service. Firstly, the system captures a status code from the communication
information sent back by a server within the network. Secondly, the captured status
code is then analysed to determine if it signifies any operational errors. This analysis
helps to understand if the server is functioning properly or if there are issues that
25 need attention. Subsequently, the system records the frequency of these errors by
maintaining a log. This log is crucial in understanding the pattern and frequency of
network issues over time. With the help of a specially designed model that has been
previously 'trained' with historical data on network performance, the system
predicts a threshold for errors. When the logged error count surpasses this predictive
30 threshold, it suggests that the network performance is deviating from normal
operating conditions, potentially indicating a serious issue. If such a threshold is
29

exceeded, the system then takes proactive measures. It identifies another server within the network that can take over the network functions of the troubled server, effectively re-routing the data flow to ensure continued service and minimal disruption to users.
5 [000102] This process might also involve user interaction, where rerouting decisions can be made based on user input, allowing for flexible and user-driven network management. For example, consider a mobile network experiencing a server failure that disrupts customer connectivity. The system identifies this issue
10 when error counts—like timeouts or internal errors—exceed normal levels. Before users experience a service drop, the system promptly redirects traffic to a standby server with enough capacity to handle the extra load, thus preventing a widespread outage.
15 [000103] FIG. 5 illustrates an exemplary block diagram of a computer system [500] upon which an embodiment of the present disclosure may be implemented. In an implementation, the computing device implements the method for outlier detection and alternate route suggestion using the system [200]. In another implementation, the computing device itself implements the method for outlier
20 detection and alternate route suggestion 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.
[000104] The computer system [500] encompasses a wide range of electronic 25 devices capable of processing data and performing computations. Examples of computer system [500] 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, 30 computer system [500] may include peripheral devices, such as monitors,
30

keyboards, and printers, as well as integrated components within larger electronic systems, showcasing their versatility in various technological applications.
[000105] The computer system [500] may include a bus [502] or other 5 communication mechanism for communicating information, and a processor [504] coupled with bus [502] for processing information. The processor [504] may be, for example, a general-purpose microprocessor. The computer system [500] may also include a main memory [506], such as a random-access memory (RAM), or other dynamic storage device, coupled to the bus [502] for storing information and 10 instructions to be executed by the processor [504]. The main memory [506] also may be used for storing temporary variables or other intermediate information during execution of the instructions to be executed by the processor [504]. Such instructions, when stored in non-transitory storage media accessible to the processor [504], render the computer system [500] into a special-purpose machine that is 15 customized to perform the operations specified in the instructions. The computer system [500] further includes a read only memory (ROM) [508] or other static storage device coupled to the bus [502] for storing static information and instructions for the processor [504].
20 [000106] A storage device [510], such as a magnetic disk, optical disk, or solid-state drive is provided and coupled to the bus [502] for storing information and instructions. The computer system [500] may be coupled via the bus [502] to a display [512], such as a cathode ray tube (CRT), for displaying information to a computer user. An input device [514], including alphanumeric and other keys, may
25 be coupled to the bus [502] for communicating information and command selections to the processor [504]. Another type of user input device may be a cursor control [516], such as a mouse, a trackball, or cursor direction keys, for communicating direction information and command selections to the processor [504], and for controlling cursor movement on the display [512]. This input device
30 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.
31

[000107] The computer system [500] may implement the techniques described herein using customized hard-wired logic, one or more Application-Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs),
5 firmware and/or program logic which in combination with the computer system [500] causes or programs the computer system [500] to be a special-purpose machine. According to one embodiment, the techniques herein are performed by the computer system [500] in response to the processor [504] executing one or more sequences of one or more instructions contained in the main memory [506]. Such
10 instructions may be read into the main memory [506] from another storage medium, such as the storage device [510]. Execution of the sequences of instructions contained in the main memory [506] causes the processor [504] to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.
15
[000108] The computer system [500] also may include a communication interface [518] coupled to the bus [502]. The communication interface [518] provides a two-way data communication coupling to a network link [520] that is connected to a local network [522]. For example, the communication interface
20 [518] 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 [518] may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be
25 implemented. In any such implementation, the communication interface [518] sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
[000109] The computer system [500] can send messages and receive data,
30 including program code, through the network(s), the network link [520] and the
communication interface 518. In the Internet example, a server [530] might transmit
32

a requested code for an application program through the Internet [528], the Internet Service Provider (ISP) [526], the local network [522] and the communication interface [518]. The received code may be executed by the processor [504] as it is received, and/or stored in the storage device [510], or other non-volatile storage for 5 later execution.
[000110] According to an aspect the present disclosure provides a non-transitory computer-readable storage medium storing instructions for outlier detection and alternate route suggestion. The storage medium comprising executable code which,
10 when executed by one or more units of a system, causes: a fetching unit [202] to fetch a status code from a response header received from a first remote server that corresponds to at least one producer network function (NF); a determining unit [204] to determine if the fetched status code corresponds to at least one error; a storing unit [206] to maintain a count of error corresponding to each of the at least
15 one error; a predicting unit [208] to predict using a trained model [304], at least one error threshold based on the count of error corresponding to each of the at least one error; a comparator [210] to compare the count of error with the corresponding predicted at least one error threshold; and based on the comparison if the predicted at least one error threshold is breached, a processing unit [212] to determine a
20 second remote server that corresponds to at least one another producer NF to serve network traffic associated with the at least one producer NF.
[000111] The present disclosure addresses the shortcomings of existing solutions for routing outlier detection and alternate route suggestion in telecommunication
25 networks by providing a method system that significantly enhances network performance and user experience. By leveraging advanced analytical techniques, such as artificial intelligence, the disclosed method facilitates in predicting network anomalies and thresholds, enabling proactive network management and swift response to potential issues. The present disclosure introduces a fetching unit that
30 retrieves status codes from response headers received from remote servers, allowing for the immediate identification of errors. A determining unit further assesses
33

whether the status codes correspond to errors, while a storing unit maintains a count of these errors. The real-time monitoring and error tracking enables quick detection of network function failures, a key advantage over traditional static routing mechanisms. A predicting unit, utilizing a trained model, predicts error thresholds
5 based on the count of errors, allowing for a proactive approach to network management. The method then compares the count of errors with the predicted thresholds using a comparator. If a threshold is breached, indicating a potential network anomaly, a processing unit determines an alternate remote server that can serve the network traffic with minimal failures, thereby ensuring continuity of
10 service and reducing the impact of network failures on user experience. The present disclosure also provides flexibility in routing network traffic, with the processing unit capable of rerouting traffic based on user input, ensuring user consent in the rerouting process. Additionally, the method includes identifying response timeouts and maintaining a count of these timeouts, further enhancing the system's ability to
15 detect and respond to network issues. By predicting error thresholds based on key parameters such as first remote server Transactions Per Second (TPS), Key Performance Indicators (KPIs), and the load capacity of remote servers, the present disclosure offers a more nuanced and effective approach to network management. The trained model, based on historical network data, enhances the accuracy of
20 predictions, ensuring that the system can adapt to changing network conditions and maintain optimal performance.
[000112] Further, in accordance with the present disclosure, it is to be acknowledged that the functionality described for the various the components/units
25 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
30 arrangements and substitutions of units, provided they achieve the intended
34

functionality described herein, are considered to be encompassed within the scope of the present disclosure.
[000113] While considerable emphasis has been placed herein on the disclosed 5 embodiments, it will be appreciated that many embodiments can be made and that many changes can be made to the embodiments without departing from the principles of the present disclosure. These and other changes in the embodiments 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 10 and non-limiting.
35

CLAIMS We Claim:
1. A method for outage detection and suggesting alternate route, the method
comprising:
fetching, by a fetching unit [202], a status code from a response header received from a first remote server that corresponds to at least one producer network function (NF);
determining, by a determining unit [204], if the fetched status code corresponds to at least one error;
maintaining, by a storing unit [206], a count of error corresponding to each of the at least one error;
predicting, by a predicting unit [208] using a trained model [304], at least one error threshold based on the count of error corresponding to each of the at least one error;
comparing, by a comparator [210], the count of error with the corresponding predicted at least one error threshold; and
based on the comparison if the predicted at least one error threshold is breached, determining, by a processing unit [212], a second remote server that corresponds to at least one another producer NF to serve network traffic associated with the at least one producer NF.
2. The method as claimed in claim 1, wherein the method comprises routing, by the processing unit [212], the network traffic from the at least one producer NF to the at least one another producer NF.
3. The method as claimed in claim 1, wherein routing the network traffic is based on a user input.
4. The method as claimed in claim 1, wherein the response header is Hypertext Transfer Protocol 2 (Http2).
5. The method as claimed in claim 1, wherein the method comprises:
identifying, by an identifying unit [214], a response timeout from the first remote server based on the determined at least one error; and
36

maintaining, by the storing unit [206], a count of the identified response timeout.
6. The method as claimed in claims 1, wherein predicting the at least one error threshold is based on at least one key parameter.
7. The method as claimed in claim 6, wherein the at least one key parameter comprises at least one of a current remote server Transactions Per Second (TPS), a Key Performance Indicators (KPIs), a second remote server TPS, a second remote server KPIs, a load capacity of the first remote server, and a load capacity of the second remote server.
8. The method as claimed in claim 1, wherein the trained model [304] is trained based on a historical network data to enhance accuracy of prediction of the least one error.
9. A system for outage detection and suggesting alternate route, the system comprises:
a fetching unit [202] configured to fetch a status code from a response header received from a first remote server that corresponds to at least one producer network function (NF);
a determining unit [204] configured to determine if the fetched status code corresponds to at least one error;
a storing unit [206] configured to maintain a count of error corresponding to each of the at least one error;
a predicting unit [208] configured to predict using a trained model [304], at least one error threshold based on the count of error corresponding to each of the at least one error;
a comparator [210] configured to compare the count of error with the corresponding predicted at least one error threshold; and
based on the comparison if the predicted at least one error threshold is breached, a processing unit [212] is configured to determine a second remote server that corresponds to at least one another producer NF to serve network traffic associated with the at least one producer NF.
37

10. The system as claimed in claim 9, wherein the processing unit is configured to rout the network traffic from the at least one producer NF to the at least one another producer NF.
11. The system as claimed in claim 9, wherein routing the network traffic is based on a user input.
12. The system as claimed in claim 9, wherein the response header is Hypertext Transfer Protocol (Http2).
13. The system as claimed in claim 9, comprises:
an identifying unit configured to identify a response timeout from the first remote server based on the determined at least one error; and
the storing unit configured to maintain a count of the identified response timeout.
14. The system as claimed in claims 9, wherein predicting the at least one error threshold is based on at least one key parameter.
15. The system as claimed in claim 14, wherein the at least one key parameter comprises at least one of a current remote server Transactions Per Second (TPS), a Key Performance Indicators (KPIs), a second remote server TPS, a second remote server KPIs, a load capacity of the first remote server, and a load capacity of the second remote server.
16. The system as claimed in claim 9, wherein the trained model [304] is trained based on a historical network data to enhance accuracy of prediction of the least one error.
Dated this 8th day of July 2023
(GARIMA SAHNEY) IN/PA-1826 AGENT FOR THE APPLICANT(S) OF SAIKRISHNA & ASSOCIATES