Method and system for generating and provisioning a key performance indicator (KPI)

Abstract

The present disclosure relates to a method and a system for generating and provisioning a Key Performance Indicator (KPI) The method includes receiving, by a transceiver unit [302] from a User Equipment (UE) [306], a Key Performance Indicator (KPI) provisioning request. The method includes extracting, by a processing unit [304], at least one of a plurality of KPI parameters from the received list of KPI parameters. The method includes generating, by the processing unit [304], a plurality of updated KPI parameters based on the extracted at least one of the plurality of KPI parameters. The plurality of updated KPI parameters is generated based on a set of pre-defined network policies applied to the extracted at least one of the plurality of KPI parameters, the set of pre-defined policies comprising one of an inverse function, a mode function, and an erlang function.

Description

METHOD AND SYSTEM FOR GENERATING AND PROVISIONING A KEY PERFORMANCE INDICATOR (KPI)FIELD OF INVENTION

[0001] Embodiments of the present disclosure generally relate to network performance management systems. More particularly, embodiments of the present disclosure relate to generating and provisioning Key Performance Indicators (KPIs) of a network.BACKGROUND

[0002] The following description of the 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 is used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of the prior art.

[0003] Network performance management systems typically track network elements and data from network monitoring tools and then combine and process such data to determine key performance indicators (KPI) of the network. Further, integrated Performance Management Systems provide the means to visualize the network performance data so that network operators and other relevant stakeholders are able to identify the service quality of the overall network, and individual / grouped network elements. By having an overall as well as detailed view of the network performance, the network operators can detect, diagnose and remedy actual service issues, as well as predict potential service issues or failures in the network and take precautionary measures accordingly.

[0004] The integrated performance management system comprises an integrated performance management engine and a key performance indicator (KPI) engine. The integrated performance management system is designed to efficiently gather and process performance counter data from various data sources. Depending on the required aggregation, the network performance data is stored in a Distributed Data Lake. This system is responsible for the comprehensive reporting and visualization of the performance counter data, providing valuable insights into the network's performance. Additionally, the Integrated Performance Management System takes charge of managing the KPIs for all network elements. The Performance Management Engine collects andprocesses counters from different data sources, which are then utilized by the KPI Engine to calculate the KPIs. The KPIs are segregated based on the necessary aggregation and stored in the Distributed Data Lake. This component of the system is responsible for the reporting and visualization of the KPI data, enabling effective monitoring and analysis of the network's key performance indicators.

[0005] The KPIs provide metrics such as call drop rate, call set up time and voice and video quality. To provide complex and advanced KPI metrics such as average holding time in duration of call and to check pattern of KPIs in a month (Aging KPI), there is a need to add operations and functions in the formula of KPIs to obtain an advanced KPI. The KPIs known in the art which were made without using operations and functions, lead to an offline report generation which is time consuming and lacks automation in report generation.

[0006] Thus, there exists an imperative need in the art to provide a solution that can overcome these and other limitations of the existing solutions.SUMMARY

[0007] 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.

[0008] An aspect of the present disclosure may relate to a method for generating and provisioning a Key Performance Indicator (KPI). The method comprises receiving, by a transceiver unit from a User Equipment (UE), a Key Performance Indicator (KPI) provisioning request. The KPI provisioning request comprises a list of KPI parameters associated with a network. The method further comprises extracting, by a processing unit, at least one of a plurality of KPI parameters from the received list of KPI parameters. Furthermore, the method comprises generating, by the processing unit, a plurality of updated KPI parameters based on the extracted at least one of the plurality of KPI parameters. The plurality of updated KPI parameters is generated based on a set of pre-defined network policies applied to the extracted at least one of the plurality of KPI parameters, the set of pre-defined policies comprising one of an inverse function, a mode function, and an erlang function.

[0009] In an exemplary aspect of the present disclosure, the method further comprises receiving, by the transceiver unit, the KPI provisioning request from the UE via a load balancer.

[0010] In an exemplary aspect of the present disclosure, the load balancer is configured to receive the KPI provisioning request from at least one of a plurality of UEs in a round-robin scheduling.

[0011] In an exemplary aspect of the present disclosure, the method further includes receiving, by the transceiver unit, the KPI provisioning request during one of a plurality of available time intervals of the system, wherein the plurality of available time intervals is determined by the load balancer.

[0012] In an exemplary aspect of the present disclosure, the plurality of time intervals is determined by the load balancer based on at least one or more network events associated with the network, wherein the one or more network events comprise at least one of a call drop rate event, a call set up time event, a voice quality event and a video quality event.

[0013] In an exemplary aspect of the present disclosure, based on at least one of the plurality of generated updated KPI parameters, the method further includes generating, by the processing unit, an updated KPI list. The method further includes transmitting, by the transceiver unit, the updated KPI list to at least one of the plurality of UEs.

[0014] Another aspect of the present disclosure may relate to a system for generating and provisioning a Key Performance Indicator (KPI). The system comprises a transceiver unit. The transceiver unit is configured to receive, from a User Equipment (UE), a Key Performance Indicator (KPI) provisioning request. The KPI provisioning request comprises a list of KPI parameters associated with a network. The system further comprises a processing unit connected at least with the transceiver unit. The processing unit is configured to extract at least one of a plurality of KPI parameters from the received list of KPI parameters. The processing unit is further configured to generate a plurality of updated KPI parameters based on the extracted at least one of the plurality of KPI parameters. The plurality of updated KPI parameters is generated based on a set of pre-defined network policies applied to the extracted at least one of the plurality of KPI parameters, the set of pre-defined policies comprising one of an inverse function, a mode function, and an erlang function.

[0015] Yet another aspect of the present disclosure may relate to a user equipment (UE). The UE comprises a transceiver unit configured to transmit a Key Performance Indicator (KPI) provisioning request. The KPI provisioning request comprises a list of KPI parameters associated with a network to a system. The transceiver unit of the UE to further receive, from the system, a plurality of updated KPI parameters. The plurality of updated KPI parameters is generated by the system based on the extracted at least one of the plurality of KPI parameters from the list of KPI parameters included in the KPI provisioning request. The plurality of updated KPI parameters is generated by the system based on a set of pre-defined network policies applied to the extracted at least one of the plurality of KPI parameters, the set of pre-defined policies comprising one of an inverse function, a mode function, and an erlang function.

[0016] Yet another aspect of the present disclosure may relate to a non-transitory computer readable storage medium storing instruction for generating and provisioning a Key Performance Indicator (KPI), the instructions include executable code which, when executed by one or more units of a system causes a transceiver unit to receive, from a User Equipment (UE), a Key Performance Indicator (KPI) provisioning request. The KPI provisioning request comprises a list of KPI parameters associated with a network. The instruction when executed further causes a processing unit to extract at least one of a plurality of KPI parameters from the received list of KPI parameters. The instruction when executed further causes the processing unit to generate a plurality of updated KPI parameters based on the extracted at least one of the plurality of KPI parameters. The plurality of updated KPI parameters is generated based on a set of pre-defined network policies applied to the extracted at least one of the plurality of KPI parameters, the set of pre-defined policies comprising one of an inverse function, a mode function, and an erlang function.OBJECTS OF THE INVENTION

[0017] Some of the objects of the present disclosure, which at least one embodiment disclosed herein satisfies are listed herein below.

[0018] It is an object of the present disclosure to automate the analysis and generation of the KPI parameters by providing advanced KPI formulas.

[0019] It is an object of the present disclosure to increase network effectiveness as the advanced KPI formula may help to know how much traffic can be handled in a network.

[0020] It is another object of the present disclosure to provide an automated analysis of the network as a KPI made by using complex operations such as an erlang function and an inverse function, to provide flexibility of automated report generation which was previously done offline.

[0021] It is an object of the present disclosure to optimize network performance and resource allocation.DESCRIPTION OF THE DRAWINGS

[0022] 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 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. Also, the embodiments shown in the figures are not to be construed as limiting the disclosure, but the possible variants of the method and system according to the disclosure are illustrated herein to highlight the advantages of the disclosure. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components or circuitry commonly used to implement such components.

[0023] FIG. 1 illustrates an exemplary block diagram of a network performance management system.

[0024] FIG. 2 illustrates an exemplary block diagram of a computing device upon which the features of the present disclosure may be implemented in accordance with exemplary implementation of the present disclosure.

[0025] FIG. 3 illustrates an exemplary block diagram of a system for generating and provisioning a Key Performance Indicator (KPI), in accordance with exemplary implementations of the present disclosure.

[0026] FIG. 4 illustrates a method flow diagram for generating and provisioning a Key Performance Indicator (KPI), in accordance with exemplary implementations of the present disclosure.

[0027] FIG. 5 illustrates an exemplary implementation of the method for generating and provisioning a Key Performance Indicator (KPI), in accordance with exemplary implementations of the present disclosure.

[0028] The foregoing shall be more apparent from the following more detailed description of the disclosure.DETAILED DESCRIPTION

[0029] 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 may each be used independently of one another or with any combination of other features. An individual feature may not address any of the problems discussed above or might address only some of the problems discussed above.

[0030] 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 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 function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

[0031] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail.

[0032] 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 operations as a sequential process, many of the operations may 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.

[0033] The word “exemplary” and / or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and / or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive — in a manner similar to the term “comprising” as an open transition word — without precluding any additional or other elements.

[0034] As used herein, a “processing unit” or “processor” or “operating processor” includes one or more processors, wherein processor refers to any logic circuitry for processing instructions. A processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a (Digital Signal Processing) 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 or processing unit is a hardware processor.

[0035] As used herein, “a user equipment”, “a user device”, “a smart-user-device”, “a smartdevice”, “an electronic device”, “a mobile device”, “a handheld device”, “a wireless communication device”, “a mobile communication device”, “a communication device” may be any electrical, electronic and / or computing device or equipment, capable of implementing the features of the present disclosure. The user equipment / device may include, but is not limited to, a mobile phone, smart phone, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, wearable device or any other computing device which is capable of implementing the features of the present disclosure. Also, the user device may contain at least one input means configured to receive an input from at least one of a transceiver unit, a processing unit, a storage unit, a detection unit and any other such unit(s) which are required to implement the features of the present disclosure.

[0036] As used herein, “storage unit” or “memory unit” refers to a machine or computer-readable medium including any mechanism for storing information in a form readable by a computer orsimilar machine. For example, a computer-readable medium includes read-only memory (“ROM”), random access memory (“RAM”), magnetic disk storage media, optical storage media, flash memory devices or other types of machine-accessible storage media. The storage unit stores at least the data that may be required by one or more units of the system to perform their respective functions.

[0037] As used herein “interface” or “user interface” refers to a shared boundary across which two or more separate components of a system exchange information or data. The interface may also be refer to a set of rules or protocols that define communication or interaction of one or more modules or one or more units with each other, which also includes the methods, functions, or procedures that may be called.

[0038] All modules, units, components used herein, unless explicitly excluded herein, may be software modules or hardware processors, the processors being a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASIC), Field Programmable Gate Array circuits (FPGA), any other type of integrated circuits, etc.

[0039] As used herein the transceiver unit includes at least one receiver and at least one transmitter configured respectively for receiving and transmitting data, signals, information or a combination thereof between units / components within the system and / or connected with the system.

[0040] As discussed in the background section, the current known solutions have several shortcomings. The KPIs provide metrics such as call drop rate, call set up time and voice and video quality. To provide complex and advanced KPI metrics such as average holding time in duration of call and to check pattern of KPI in a month (Aging KPI), there is a need to add operations and functions in the formula of KPI to obtain an advanced KPI. The KPIs known in the art which were made without using operations and functions, lead to an offline report generation which is time consuming and lacks automation in report generation. The present disclosure aims to overcome the above-mentioned and other existing problems in this field of technology by providing a method and a system of generating and provisioning a Key Performance Indicator (KPI).

[0041] FIG. 1 illustrates an exemplary block diagram of a network performance management system

[0100] , in accordance with the exemplary embodiments of the present invention. Referringto FIG. 1, the network performance management system

[0100] comprises various sub-systems such as: an integrated performance management (IPM) system

[0102] , a normalization layer

[0104] , a computation layer

[0106] , an anomaly detection layer

[0108] , a streaming engine

[0110] , a load balancer

[0112] , an operation and management system

[0114] , an API gateway system

[0116] , an analysis engine

[0118] , a parallel computing framework

[0120] , a forecasting engine

[0122] , a distributed file system

[0124] , a mapping layer

[0126] , a distributed data lake

[0128] , a scheduling layer

[0130] , a reporting engine

[0132] , a message broker

[0134] , a graph layer

[0136] , a caching layer

[0138] , a service quality manager

[0140] , and a correlation engine

[0142] , Exemplary connections between the above-mentioned subsystems are also as shown in FIG. 1. However, it will be appreciated by those skilled in the art that the present disclosure is not limited to the connections shown in the diagram, and any other connections between the different subsystems that are needed to realize the effects of the network performance management system

[0100] are within the scope of this disclosure.

[0042] Further, the integrated performance management (IPM) system

[0102] comprises a performance management engine

[0150] , a Key Performance Indicator (KPI) Engine

[0152] , and an ingestion layer

[0154] ,

[0043] The following section describes some of the different sub-systems of the system

[0100] :

[0044] Performance Management Engine

[0150] : The Performance Management engine

[0150] is a crucial component of the integrated performance management system

[0102] , and is responsible for collecting, processing, and managing performance counter data from various data sources within the network. The gathered data includes metrics such as, connection speed, latency, data transfer rates, etc. This raw data is then processed and aggregated as required, forming a comprehensive overview of network performance. The processed information is then stored in the Distributed Data Lake

[0128] , which is a centralized, scalable, and flexible storage medium, allowing for easy access and further analysis. The Performance Management engine

[0150] also enables the reporting and visualization of this performance counter data, thus providing network administrators with a real-time, insightful view of the network's operation. Through these visualizations, operators can monitor the network's performance, identify potential issues, and make informed decisions to enhance network efficiency and reliability.

[0045] Key Performance Indicator (KPI) Engine

[0152] : The Key Performance Indicator (KPI) Engine

[0152] is a dedicated component tasked with managing the KPIs of all the network elements.It uses the performance counters, which are collected and processed by the Performance Management engine

[0150] from various data sources. These counters, which indicate crucial performance data, are harnessed by the KPI engine

[0152] to calculate essential KPIs. These KPIs may include, without limitations, data throughput, latency, packet loss rate, etc. Once the KPIs are computed, they are segregated based on the aggregation requirements, offering a multi-layered and detailed understanding of network performance. The processed KPI data is then stored in the Distributed Data Lake

[0128] , ensuring a highly accessible, centralized, and scalable data repository for further analysis and utilization. Similar to the Performance Management engine

[0150] , the KPI engine

[0152] is also responsible for reporting and visualization of KPI data. This functionality allows network administrators to gain a comprehensive, visual understanding of the network's performance, thus supporting informed decision-making and efficient network management.

[0046] Ingestion layer

[0154] : The Ingestion layer

[0154] forms a key part of the Integrated Performance Management system

[0102] , and functions to establish an environment capable of handling diverse types of incoming data. This data may include, without limitations, Alarms, Counters, Configuration parameters, Call Detail Records (CDRs), Infrastructure metrics, Logs, and Inventory data, all of which are crucial for maintaining and optimizing the performance of the network. Upon receiving the data, the Ingestion layer

[0154] validates integrity and correctness of the data to ensure that the data is fit for further processing. Following the step of validation, the data is routed to various components of the system

[0100] , including the Normalization layer

[0104] , the Streaming Engine

[0110] , the analysis engine

[0118] , and the Message Broker

[0134] , The destination is chosen based on where the data is required for further analytics and / or processing. By serving as the first point of contact for incoming data, the Ingestion layer

[0154] plays a vital role in managing the data flow within the system

[0100] , thus supporting comprehensive and accurate network performance analysis.

[0047] Normalization layer

[0104] : The Normalization Layer

[0104] serves to standardize, enrich, and store data into the appropriate databases. The normalization layer

[0104] receives data from the ingestion layer

[0154] and adjusts it to a common standard, making it easier to compare and analyse. This process of "normalization" reduces redundancy and improves data integrity. Upon completion of normalization, the data is stored in various databases like the Distributed Data Lake

[0128] , Caching Layer

[0138] , and Graph Layer

[0136] , depending on the intended use for the data. The choice of storage determines how the data can be accessed and used in the future. Additionally, the Normalization Layer

[0104] produces data for the Message Broker

[0134] , which is configured to enable communication between different parts of the network performance management system

[0100] through the exchange of data messages. Moreover, the Normalization Layer

[0104] supplies the standardized data to several other subsystems. These include the Analysis Engine

[0118] for detailed data examination, the Correlation Engine

[0012] for detecting relationships among various data elements, the Service Quality Manager

[0140] for maintaining and improving the quality of services, and the Streaming Engine

[0110] for processing real-time data streams. These subsystems depend on the normalized data to perform their operations effectively and accurately.

[0048] Caching layer

[0138] : The Caching Layer

[0138] plays a significant role in data management and optimization in the network performance management system

[0100] , During the initial phase, the Normalization Layer

[0104] processes incoming raw data to create a standardized format, enhancing consistency and comparability. The Normalizer Layer

[0104] then inserts this normalized data into various databases, such as the Caching Layer

[0138] , The Caching Layer

[0138] is a highspeed data storage layer, which temporarily holds data that is likely to be reused, to improve speed and performance of data retrieval. By storing frequently accessed data in the Caching Layer

[0138] , the network performance management system

[0100] significantly reduces the time taken to access this data, improving overall efficiency and performance of the network performance management system

[0100] , Further, the Caching Layer

[0138] serves as an intermediate layer between the data sources and other sub-systems, such as the Analysis Engine

[0118] , the Correlation Engine

[0142] , the Service Quality Manager

[0140] , and the Streaming Engine

[0110] , The Normalization Layer

[0104] is responsible for providing these sub-systems with the necessary data from the Caching Layer

[0138] ,

[0049] Computation layer

[0106] : The Computation Layer

[0106] serves as the main hub for complex data processing tasks. In the initial stages, raw data is gathered, normalized, and enriched by the Normalization Layer

[0104] , The Normalization Layer

[0104] then inserts this normalized data into multiple databases including the Distributed Data Lake

[0128] , the Caching Layer

[0138] , and the Graph Layer

[0136] , and also feeds it to the Message Broker

[0134] , Within the Computation Layer

[0106] , several powerful sub-systems such as the Analysis Engine

[0118] , the Correlation Engine

[0142] , the Service Quality Manager

[0140] , and the Streaming Engine

[0110] , utilize the normalized data. These systems are designed to execute various data processing tasks. The Analysis Engine

[0118] performs in-depth data analytics to generate insights from the data. The Correlation Engine

[0142] identifies and understands the relations and patterns within the data. The Service Quality Manager

[0140] assesses and ensures the quality of the services. The Streaming Engine

[0110] processes and analyses the real-time data feeds. In essence, the Computation Layer

[0106] is where all major computation and data processing tasks occur. It uses the normalized dataprovided by the Normalization Layer

[0104] , processing it to generate useful insights, ensure service quality, understand data patterns, and facilitate real-time data analytics.

[0050] Message broker

[0134] : The Message Broker

[0134] operates as a publish-subscribe messaging system. It orchestrates and maintains the real-time flow of data from various sources and applications. At its core, the Message Broker

[0134] facilitates communication between data producers and consumers through message-based topics. This creates an advanced platform for contemporary distributed applications. With the ability to accommodate a large number of permanent or ad-hoc consumers, the Message Broker

[0134] demonstrates immense flexibility in managing data streams. Moreover, the message broker

[0134] leverages the filesystem for storage and caching, boosting its speed and efficiency. The design of the Message Broker

[0134] is centred around reliability and is engineered to be fault-tolerant and mitigate data loss, ensuring the integrity and consistency of the data. With its robust design and capabilities, the Message Broker

[0134] forms a critical component in managing and delivering real-time data in the network performance management system

[0100] ,

[0051] Graph layer

[0136] : The Graph Layer

[0136] , serving as the Relationship Modeler, plays a pivotal role in the network Performance Management system

[0100] , It can model a variety of data types, including alarm, counter, configuration, CDR data, Infra-metric data, Probe Data, and Inventory data. Equipped with the capability to establish relationships among diverse types of data, the graph layer

[0136] offers extensive modelling capabilities. For instance, the graph layer

[0136] can model Alarm and Counter data, Vprobe and Alarm data, elucidating their interrelationships. Moreover, the graph layer

[0136] is adept at processing steps provided in the model and delivering the results to the sub-system requested, such as the Parallel Computing framework

[0120] , Workflow Engine, Query Engine, the Correlation engine

[0142] , Performance Management Engine

[0150] , or KPI Engine

[0152] , With its powerful modelling and processing capabilities, the Graph Layer

[0136] forms an essential part of the network performance management system

[0100] , enabling the processing and analysis of complex relationships between various types of network data.

[0052] Scheduling layer

[0130] : The Scheduling Layer

[0130] is endowed with the ability to execute tasks at predetermined intervals set according to user preferences. A task might be an activity, such as performing a service call, an API call to another microservice, the execution of an Elastic Search query, and storing its output in the Distributed Data Lake

[0128] or Distributed File System

[0124] or sending it to another micro-service. The versatility of the Scheduling Layer

[0130] extends to facilitating graph traversals via the Mapping Layer

[0126] to execute tasks. This crucial capability enables seamless and automated operations within the network performance management system

[0100] , ensuring that various tasks and services are performed on schedule, without manual intervention, thereby enhancing the efficiency and performance of the network performance management system

[0100] , Thus, the Scheduling Layer

[0130] orchestrates the systematic and periodic execution of tasks.

[0053] Analysis Engine

[0118] : The Analysis Engine

[0118] is adapted to provide an environment where users can configure and execute workflows for a wide array of use-cases. This facility aids in the debugging process and facilitates a better understanding of call flows. With the Analysis Engine

[0118] , users can perform queries on data sourced from various subsystems or external gateways. This capability allows for an in-depth overview of data and aids in pinpointing issues. The flexibility of the analysis engine

[0118] allows users to configure specific policies aimed at identifying anomalies within the data. When these policies detect abnormal behaviour or policy breaches, the analysis engine

[0118] sends notifications, ensuring swift and responsive action. In essence, the Analysis Engine

[0118] provides a robust analytical environment for systematic data interrogation, facilitating efficient problem identification and resolution.

[0054] Parallel Computing Framework

[0120] : The Parallel Computing Framework

[0120] is adapted to provide a user-friendly yet advanced platform for executing computing tasks in parallel. The parallel computing framework

[0120] showcases both scalability and fault tolerance, crucial for managing vast amounts of data. Users can input data via the Distributed File System (DFS)

[0124] or Distributed Data Lake (DDL)

[0128] , The parallel computing framework

[0120] supports the creation of task chains by interfacing with the Service Configuration Management (SCM) SubSystem. Each task in a workflow is executed sequentially, but multiple chains can be executed simultaneously, optimizing processing time. To accommodate varying task requirements, the parallel computing framework

[0120] supports the allocation of specific host lists for different computing tasks. The Parallel Computing Framework

[0120] is an essential tool for enhancing processing speeds and efficiently managing computing resources.

[0055] Distributed File System

[0124] : The Distributed File System (DFS)

[0124] is adapted to enable multiple clients to access and interact with data seamlessly. The DFS

[0124] is designed to manage data files that are partitioned into numerous segments known as chunks. In the context of a network with vast data, the DFS

[0124] effectively allows for the distribution of data across multiple nodes. The DFS

[0124] architecture enhances both the scalability and redundancy of thenetwork performance management system

[0100] , ensuring optimal performance even with large data sets. The DFS

[0124] also supports diverse operations, facilitating the flexible interaction with and manipulation of data.

[0056] Load balancer

[0112] : The Load Balancer (LB)

[0112] is configured to efficiently distribute incoming network traffic across a multitude of backend servers or microservices. The LB

[0112] ensures even distribution of data requests, leading to optimized server resource utilization, reduced latency, and improved overall performance of the network performance management system

[0100] , The LB

[0112] implements various routing strategies to manage traffic, including roundrobin scheduling, header-based request dispatch, and context-based request dispatch. Round-robin scheduling is a simple method of rotating requests evenly across available servers. In contrast, header and context-based dispatching allow for more intelligent, request-specific routing. Headerbased dispatching routes requests based on data contained within the headers of the Hypertext Transfer Protocol (HTTP) requests. Context-based dispatching routes traffic based on the contextual information about the incoming requests. For example, in an event-driven architecture, the LB

[0112] manages event and event acknowledgments, forwarding requests or responses to the specific microservice that has requested the event.

[0057] Streaming Engine

[0110] : The Streaming Engine

[0110] , also referred to as Stream Analytics, is a critical subsystem configured for high-speed data pipelining to the User Interface (UI). The objective of the streaming engine

[0110] is to ensure real-time data processing and delivery. Data is received from various connected subsystems and processed in real-time by the Streaming Engine

[0110] , After processing, the data is streamed to the UI, fostering rapid decisionmaking and responses. The Streaming Engine

[0110] cooperates with the Distributed Data Lake

[0128] , the Message Broker

[0134] , and the Caching Layer

[0138] to provide seamless, real-time data flow. The streaming engine

[0110] is designed to perform required computations on incoming data instantly, ensuring that the most relevant and up-to-date information is always available at the UI. Furthermore, the streaming engine

[0110] can also retrieve data from the Distributed Data Lake

[0128] , the Message Broker

[0134] , and the Caching Layer

[0138] as per the requirement and deliver it to the UI in real-time. The goal of the streaming engine

[0110] is to provide fast, reliable, and efficient data streaming.

[0058] Reporting Engine

[0132] : The Reporting Engine

[0132] is configured to dynamically create report layouts of API data, catered to individual client requirements, and deliver these reports via the Notification Engine. The reporting engine

[0132] serves as the primary interface for creatingcustom reports based on the data visualized through the client's dashboard. The dashboard, created by the client through the User Interface (UI), provides the basis for the reporting engine

[0132] to process and compile data from various interfaces. The main output of the Reporting Engine

[0132] is a detailed report generated in Excel format. The capacity of the Reporting Engine

[0132] to parse data from different subsystem interfaces, process it according to the client's specifications and requirements, and generate a comprehensive report makes it an essential component of the network performance management system

[0100] , Furthermore, the Reporting Engine

[0132] integrates seamlessly with the Notification Engine to ensure timely and efficient delivery of reports to clients via email.

[0059] FIG. 2 illustrates an exemplary block diagram of a computing device

[0200] upon which the features of the present disclosure may be implemented in accordance with exemplary implementation of the present disclosure. In an implementation, the computing device

[0200] may also implement a method for generating and provisioning a Key Performance Indicator (KPI) utilising the system. In another implementation, the computing device

[0200] itself implements the method generating and provisioning a Key Performance Indicator (KPI) using one or more units configured within the computing device

[0200] , wherein said one or more units are capable of implementing the features as disclosed in the present disclosure.

[0060] The computing device

[0200] may include a bus

[0202] or other communication mechanism for communicating information, and a hardware processor

[0204] coupled with bus

[0202] for processing information. The hardware processor

[0204] may be, for example, a general-purpose microprocessor. The computing device

[0200] may also include a main memory

[0206] , such as a random-access memory (RAM), or other dynamic storage device, coupled to the bus

[0202] for storing information and instructions to be executed by the processor

[0204] , The main memory

[0206] also may be used for storing temporary variables or other intermediate information during execution of the instructions to be executed by the processor

[0204] , Such instructions, when stored in non-transitory storage media accessible to the processor

[0204] , render the computing device

[0200] into a special-purpose machine that is customized to perform the operations specified in the instructions. The computing device

[0200] further includes a read only memory (ROM)

[0208] or other static storage device coupled to the bus

[0202] for storing static information and instructions for the processor

[0204] ,

[0061] A storage device

[0210] , such as a magnetic disk, optical disk, or solid-state drive is provided and coupled to the bus

[0202] for storing information and instructions. The computingdevice

[0200] may be coupled via the bus

[0202] to a display

[0212] , such as a cathode ray tube (CRT), Liquid crystal Display (LCD), Light Emitting Diode (LED) display, Organic LED (OLED) display, etc. for displaying information to a computer user. An input device

[0214] , including alphanumeric and other keys, touch screen input means, etc. may be coupled to the bus

[0202] for communicating information and command selections to the processor

[0204] , Another type of user input device may be a cursor controller

[0216] , such as a mouse, a trackball, or cursor direction keys, for communicating direction information and command selections to the processor

[0204] , and for controlling cursor movement on the display

[0212] , The 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.

[0062] The computing device

[0200] 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 computing device

[0200] causes or programs the computing device

[0200] to be a special-purpose machine. According to one implementation, the techniques herein are performed by the computing device

[0200] in response to the processor

[0204] executing one or more sequences of one or more instructions contained in the main memory

[0206] , Such instructions may be read into the main memory

[0206] from another storage medium, such as the storage device

[0210] , Execution of the sequences of instructions contained in the main memory

[0206] causes the processor

[0204] to perform the process steps described herein. In alternative implementations of the present disclosure, hard-wired circuitry may be used in place of or in combination with software instructions.

[0063] The computing device

[0200] also may include a communication interface

[0218] coupled to the bus

[0202] , The communication interface

[0218] provides a two-way data communication coupling to a network link

[0220] that is connected to a local network

[0222] , For example, the communication interface

[0218] 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

[0218] 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

[0218] sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

[0064] The computing device

[0200] can send messages and receive data, including program code, through the network(s), the network link

[0220] and the communication interface

[0218] , In the Internet example, a server

[0230] might transmit a requested code for an application program through the Internet

[0228] , the ISP

[0226] , the local network

[0222] , host

[0224] and the communication interface

[0218] , The received code may be executed by the processor

[0204] as it is received, and / or stored in the storage device

[0210] , or other non-volatile storage for later execution.

[0065] Further, in accordance with the present disclosure, it is to be acknowledged that the functionality described for the various the components / units can be implemented interchangeably. While specific embodiments may disclose a particular functionality of these units for clarity, it is recognized that various configurations and combinations thereof are within the scope of the disclosure. The functionality of specific units as disclosed in the disclosure should not be construed as limiting the scope of the present disclosure. Consequently, alternative arrangements and substitutions of units, provided they achieve the intended functionality described herein, are considered to be encompassed within the scope of the present disclosure.

[0066] The present disclosure is implemented by a system

[0300] (as shown in FIG. 3). In an implementation, the system

[0300] may be implemented on the computing device

[0200] (as shown in FIG. 2). It is further noted that the computing device

[0200] is able to perform the steps of a method

[0400] (as shown in FIG. 4).

[0067] Referring to FIG. 3, an exemplary block diagram of a system

[0300] for generating and provisioning a Key Performance Indicator (KPI), is shown, in accordance with the exemplary implementations of the present disclosure. The system

[0300] comprises at least one transceiver unit

[0302] , at least one processing unit

[0304] and a Distributed File System (DFS)

[0124] , The system

[0300] is in communication with a User Equipment

[0306] , Also, all of the components / units of the system

[0300] are assumed to be connected to each other unless otherwise indicated below. As shown in the figures all units shown within the system should also be assumed to be connected to each other. Also, in FIG. 3 only a few units are shown, however, the system

[0300] may comprise multiple such units or the system

[0300] may comprise any such numbers of said units, as required to implement the features of the present disclosure. Further, in an implementation, the system

[0300] may be present in a user device to implement the features of the present disclosure. The system

[0300] may be a part of the user device / or may be independent of but in communication with the user device (may also referred herein as a UE). In another implementation, the system

[0300] mayreside in a server or a network entity. In yet another implementation, the system

[0300] may reside partly in the server / network entity and partly in the user device.

[0068] The system

[0300] is configured for generating and provisioning a Key Performance Indicator (KPI), with the help of the interconnection between the components / units of the system

[0300] ,

[0069] The transceiver unit

[0302] of the system

[0300] is configured to receive, from a User Equipment (UE)

[0306] , a Key Performance Indicator (KPI) provisioning request. The KPI provisioning request comprises a list of KPI parameters associated with a network. The KPI provisioning request may be related to a request for generation of an advanced KPI formula / updated KPI parameter using functions like the erlang function, the mode function, the inverse function, and the like. The advanced KPI formula / updated KPI parameter may enable automated analysis of the KPIs. The KPI provisioning request may be for a 5thGeneration network, a 4thGeneration network, a 6thGeneration network, and any other future generations of network. In an implementation of the present disclosure, the list of KPI parameters may include latency, packet loss, network availability, and the like. The latency refers to delay in time between sending a request and receiving a response. The packet loss refers to the number of data packets lost in a communication. The network availability refers to the duration of time when the network is available or accessible to the user.

[0070] The transceiver unit

[0302] is further configured to receive the KPI provisioning request from the UE

[0306] via a load balancer

[0308] , The load balancer

[0308] receives the KPI provisioning request from at least one of a plurality of UEs in a round-robin scheduling. The round-robin scheduling refers to when the load balancer

[0308] receives the KPI provisioning request from at least one of the plurality of UEs in a sequential manner. The round-robin scheduling assists in even distribution of the KPI provisioning request to the load balancer

[0308] ,

[0071] The transceiver unit

[0302] is further configured to receive the KPI provisioning request during one of a plurality of available time intervals of the system. The plurality of available time intervals is determined by the load balancer

[0308] , The plurality of time intervals is determined by the load balancer

[0308] based on at least one or more network events associated with the network. The one or more network events comprise at least one of a call drop rate event, a call set up time event, a voice quality event and a video quality event. The call drop rate event refers to the number of times a call is cut off before either party has ended the call. The call set up time event refers tothe duration of time required to establish the call between the user and the network terminal. The voice quality event refers to checking of the characteristics of voice like lagging, high frequency, low frequency, and the like. The video quality event refers to checking of the quality of video during a video call, like video quality, colour accuracy, frame rates, etc.

[0072] The system

[0300] further includes a processing unit

[0304] connected at least with the transceiver unit

[0302] , The processing unit

[0304] is configured to extract at least one of a plurality of KPI parameters from the received list of KPI parameters. To extract, the processing unit

[0304] may check the purpose of the KPIs based on the KPI provisioning request and extract the KPIs based on the relevance to the purpose. For instance, the KPI provisioning request may be for the purpose of increasing network availability, and accordingly, the processing unit

[0304] may extract the network availability parameter KPIs. The processing unit

[0304] may store the KPI parameters at the Distributed File System (DFS)

[0124] in the network. In an implementation of the present solution, an Adaptive Management (AM) unit

[0506] as shown in FIG. 5, may initiate storing a set of data associated with the KPI parameters at the DFS

[0124] , The AM unit

[0506] platform leverages machine learning to detect anomalous network patterns and create reports and alerts based on these patterns. The troubleshooting helps in proactive root cause analysis and resolution before the network symptoms start affecting operations.

[0073] The processing unit

[0304] is further configured to generate a plurality of updated KPI parameters based on the extracted at least one of the plurality of KPI parameters. The plurality of updated KPI parameters is generated based on a set of pre-defined network policies applied to the extracted at least one of the plurality of KPI parameters. The set of pre-defined network policies comprises one of an inverse function, a mode function, an erlang function. In an exemplary embodiment, the plurality of updated KPI parameters may be generated based on a logical function, an inverse function, a supporting function, and the like. The mode function refers to a function that may provide statistical operation support to compute mode of the KPI parameters. Further, erlang is a unique formula in which computation of data is performed by a numerical method. The erlang function is used to calculate the total number of servers that may be required for a specific volume of traffic. The logical function refers to a logical condition which may check whether a specific condition is true or false. For a true and false condition, a further action may be defined in the KPI parameter. The supporting function refers to a scenario where the KPI is computed for a particular time duration which was computed using other supporting KPIs. The inverse KPI refers to monitoring an average holding time in duration of a call and calculating the pattern of the KPI in a predefined duration. In an exemplary implementation, the predefinedduration may be a month. In other exemplary implementations, the predefined duration may be any, such as, a week, a fortnight, a month, three-months, a quarter, a year, etc.

[0074] Based on at least one of the plurality of generated updated KPI parameters, the processing unit

[0304] is configured to generate an updated KPI list. Further, the transceiver unit

[0302] is configured to transmit the updated KPI list to at least one of the plurality of UEs

[0306] ,

[0075] In an embodiment of the present disclosure, the plurality of updated KPI parameters may be the advanced KPI formula. The advanced KPI formula includes at least one of the set of policies added to the plurality of KPI parameters. For instance, the advanced KPI formula comprises a function like the inverse function in the KPI parameter. The inverse function may provide advanced KPI metrics for average holding time in a call duration and compute the data of pattern of the KPI in the predefined duration.

[0076] Referring to FIG. 4, an exemplary method flow diagram

[0400] for generating and provisioning a Key Performance Indicator (KPI), in accordance with exemplary implementations of the present disclosure is shown. In an implementation the method

[0400] is performed by the system

[0300] , Further, in an implementation, the system

[0300] may be present in a server device to implement the features of the present disclosure. Also, as shown in FIG. 4, the method

[0400] starts at step

[0402] ,

[0077] At step

[0404] , the method includes receiving, by a transceiver unit

[0302] from a User Equipment (UE)

[0306] , a Key Performance Indicator (KPI) provisioning request. The KPI provisioning request comprises a list of KPI parameters associated with a network. The KPI provisioning request may be related to a request for generation of an advanced KPI formula / updated KPI parameter using functions like the erlang function, the mode function, the inverse function, and the like. The advanced KPI formula / updated KPI parameter may enable automated analysis of the KPIs. The KPI provisioning request may be for a 5thGeneration network, a 4thGeneration network, a 6thGeneration network, and any other future generations of network. In an implementation of the present disclosure, the list of KPI parameters may include latency, packet loss, network availability, and the like. The latency refers to delay in time between sending a request and receiving a response. The packet loss refers to the number of data packets lost in a communication. The network availability refers to the duration of time when the network is available or accessible to the user. The method further includes receiving, by the transceiver unit

[0302] , the KPI provisioning request during one of a plurality of available time intervals of thesystem. The plurality of available time intervals is determined by the load balancer

[0308] , The plurality of time intervals is determined by the load balancer

[0308] based on at least one or more network events associated with the network. The one or more network events comprise at least one of a call drop rate event, a call set up time event, a voice quality event and a video quality event. The call drop rate event refers to the number of times a call is cut off before either party has ended the call. The call set up time event refers to the duration of time required to establish the call between the user and the network terminal. The voice quality event refers to checking of the characteristics of voice like lagging, high frequency, low frequency, and the like. The video quality event refers to checking of the quality of video during a video call, like video quality, colour accuracy, frame rates, etc.

[0078] Next, at step

[0406] , the method comprises extracting, by a processing unit

[0304] , at least one of a plurality of KPI parameters from the received list of KPI parameters. To extract, the processing unit

[0304] may check the purpose of the KPIs based on the KPI provisioning request and extract the KPIs based on the relevance to the purpose. For instance, if the KPI provisioning request is for the purpose of increasing network availability, then the processing unit

[0304] may extract the network availability parameter KPI. The processing unit

[0304] may store the KPI parameter at the Distributed File System (DFS)

[0124] in the network. In an implementation of the present disclosure, the Adaptive Management (AM) unit

[0506] may initiate storing a set of data associated with the KPI parameter at the DFS

[0124] , The AM unit

[0506] platform leverages machine learning to detect anomalous network patterns and create reports and alerts based on these patterns. The troubleshooting helps in proactive root cause analysis and resolution before the network symptoms start affecting operations.

[0079] Next, at step

[0408] , the method comprises generating, by the processing unit

[0304] , a plurality of updated KPI parameters based on the extracted at least one of the plurality of KPI parameters. The plurality of updated KPI parameters is generated based on a set of pre-defined network policies applied to the extracted at least one of the plurality of KPI parameters, the set of pre-defined network policies comprising one of an inverse function, a mode function, and an erlang function. In an exemplary embodiment, the plurality of updated KPI parameters may be generated based on a logical function, an inverse function, a supporting function, and the like. The mode function refers to a function that may provide statistical operation support to compute mode of the KPI parameters. Further, erlang is a unique formula in which computation of data is performed by a numerical method. The erlang function is used to calculate the total number of servers that may be required for a specific volume of traffic. The logical function refers to a logical condition whichmay check whether a specific condition is true or false. For a true and false condition, a further action may be defined in the KPI parameter. The supporting function refers to a scenario where the KPI is computed for a particular time duration which was computed using other supporting KPIs. The inverse KPI refers to monitoring an average holding time in duration of a call and calculating the pattern of the KPI in the predefined duration.

[0080] The method further comprises receiving, by the transceiver unit

[0302] , the KPI provisioning request from the UE

[0306] via a load balancer

[0308] , The load balancer

[0308] is configured to receive the KPI provisioning request from at least one of a plurality of UEs in a round-robin scheduling. The round-robin scheduling refers to when the load balancer

[0308] receives the KPI provisioning request from at least one of the plurality of UEs in a sequential manner. The round-robin scheduling assists in even distribution of the KPI provisioning request to the load balancer

[0308] ,

[0081] Based on at least one of the plurality of generated updated KPI parameters, the method further comprises generating, by the processing unit

[0304] , an updated KPI list. Further, based on at least one of the plurality of generated updates KPI parameters, the method includes transmitting, by the transceiver unit

[0302] , the updated KPI list to at least one of the plurality of UEs.

[0082] In an embodiment of the present disclosure, the plurality of updated KPI parameters may include the advanced KPI formula. The advanced KPI formula comprises at least one of the set of policies added to the plurality of KPI parameters. For instance, the advanced KPI formula comprises a function like the inverse function in the KPI parameter. The inverse function may provide advanced KPI metrics for average holding time in a call duration and compute the data of pattern of the KPI in the predefined duration.

[0083] Thereafter, the method terminates at step

[0010] ,

[0084] Referring to FIG. 5, it illustrates an exemplary sequence flow

[0500] implementation of the method for generating and provisioning a KPI. The sequence flow

[0500] includes a User Interface (UI) server

[0504] , the Load Balancer

[0308] , the Integrated Performance Management System

[0102] , the Adaptive Management (AM) unit

[0506] , and the Distributed File System

[0124] ,

[0085] At step 1, a user

[0502] may send the KPI provisioning request to the UI Server

[0504] , The UI Server

[0504] performs the same function as the User Interface server

[0504] shown in FIG. 5.The KPI provisioning request comprises a list of KPI parameters associated with a network. The KPI provisioning request refers to a request to monitor KPIs, wherein the KPIs are parameters to measure and evaluate performance of the network. The KPI provisioning request may be for a 5thGeneration network, a 4thGeneration network, a 6thGeneration network, and any other future generations of network. In an implementation of the present disclosure, the list of KPI parameters may include latency, packet loss, network availability, and the like. The latency refers to delay in time between sending a request and receiving a response. The packet loss refers to the number of data packets lost in a communication. The network availability refers to the duration of time when the network is available or accessible to the user

[0502] ,

[0086] At step 2, the UI server

[0504] may forward the KPI provisioning request to the Load Balancer

[0308] , The load balancer

[0308] receives the KPI provisioning request from at least one of a plurality of UEs in a round-robin scheduling. The round-robin scheduling refers to when the load balancer

[0308] receives the KPI provisioning request from at least one of the plurality of UEs in a sequential manner. The round-robin scheduling assists in even distribution of the KPI provisioning request to the load balancer

[0308] ,

[0087] At step 3, the AM unit

[0506] platform may extract the KPI parameter from the User Interface Server

[0504] via the Load Balancer

[0308] , The AM unit

[0506] platform leverages machine learning to detect anomalous network patterns and create reports and alerts based on these patterns. The troubleshooting helps in proactive root cause analysis and resolution before the network symptoms start affecting operations. In an implementation of the present disclosure, the UI server

[0504] transmits the KPI parameter received from the user

[0502] to the AM UNIT

[0506] via the load balancer

[0308] , wherein the load balancer

[0308] is configured to identify the available instance for receiving the KPI parameter at the AM unit

[0506] platform from the UI Server

[0504] , In an implementation of the present disclosure, the UI server

[0504] transmits the KPI parameter received from the user

[0502] to the AM unit

[0506] via the load balancer

[0308] in a pre-defined format such as the round-robin scheduling. In an implementation of the present disclosure, one or more KPI parameters from the KPI parameter list may be determined at the UI server

[0504] in the network based on the KPI provisioning request from the user

[0502] ,

[0088] At step 4, the AM unit

[0506] platform may store the KPI parameters at the Distributed File System (DFS)

[0124] in the network. In an implementation of the present disclosure, the AM unit

[0506] platform may initiate storing a set of data associated with the KPI parameters at the DFS

[0124] , Furthermore, in another implementation of the present disclosure, the IPM

[0102] may store the set of data associated with the KPI parameter at the DFS

[0124] ,

[0089] At step 5, at the AM unit

[0506] platform, sends a plurality of updated KPI parameters based on the KPI provisioning request. The plurality of updated KPI parameters is generated based on a set of pre-defined network policies applied to the extracted at least one of the plurality of KPI parameters. The set of pre-defined network policies comprise one of an inverse function, a mode function, and an erlang function. In an exemplary embodiment, the plurality of updated KPI parameters may be generated based on a logical function, an inverse function, a supporting function, and the like. The mode function refers to a function that may provide statistical operation support to compute mode of the KPI parameters. Further, erlang is a unique formula in which computation of data is performed by a numerical method. The erlang function is used to calculate the total number of servers that may be required for a specific volume of traffic. The logical function refers to a logical condition which may check whether a specific condition is true or false. For a true and false condition, a further action may be defined in the KPI parameter. The supporting function refers to a scenario where the KPI is computed for a particular time duration which was computed using other supporting KPIs. The inverse KPI refers to monitoring an average holding time in duration of a call and calculating the pattern of the KPI in the predefined duration.

[0090] At step 6, the AM unit

[0506] may send an acknowledgment to the UI Server

[0504] of an updated KPI parameter list. Further the plurality of updated KPI parameters may be provisioned by the UI Server

[0504] and displayed to the user

[0502] in the network, based on at least the acknowledgement message received at the UI Server

[0504] ,

[0091] In an embodiment of the present disclosure, the plurality of updated KPI parameters may be the advanced KPI formula. The advanced KPI formula comprises at least one of the set of policies added to the plurality of KPI parameters. For instance, the advanced KPI formula comprises a function like the inverse function in the KPI parameter. The inverse function may provide advanced KPI metrics for average holding time in a call duration and compute the data of pattern of the KPI in the predefined duration.

[0092] The present disclosure further discloses a user equipment (UE). The UE comprises a transceiver unit

[0302] configured to transmit a Key Performance Indicator (KPI) provisioning request. The KPI provisioning request comprises a list of KPI parameters associated with a network to a system. The transceiver unit

[0302] of the UE to further receive, from the system, aplurality of updated KPI parameters. The plurality of updated KPI parameters is generated by the system based on the extracted at least one of the plurality of KPI parameters from the list of KPI parameters included in the KPI provisioning request. The plurality of updated KPI parameters is generated by the system based on a set of pre-defined network policies applied to the extracted at least one of the plurality of KPI parameters, the set of pre-defined policies comprising one of an inverse function, a mode function, and an erlang function.

[0093] The present disclosure further discloses a non-transitory computer readable storage medium storing instruction for generating and provisioning a Key Performance Indicator (KPI), the instructions including executable code which, when executed by one or more units of a system causes a transceiver unit

[0302] of the system to receive, from a User Equipment (UE)

[0306] , a Key Performance Indicator (KPI) provisioning request. The KPI provisioning request comprises a list of KPI parameters associated with a network. The instructions when executed by the system further cause a processing unit

[0304] of the system to extract at least one of a plurality of KPI parameters from the received list of KPI parameters. The instructions when executed by the system further cause the processing unit

[0304] of the system to generate a plurality of updated KPI parameters based on the extracted at least one of the plurality of KPI parameters. The plurality of updated KPI parameters is generated based on a set of pre-defined network policies applied to the extracted at least one of the plurality of KPI parameters, the set of pre-defined policies comprising one of an inverse function, a mode function, and an erlang function.

[0094] As is evident from the above, the present invention has several technical advantages for. Firstly, it enhances Network Effectiveness by generating reports on KPIs and counters that measure the health and quality of service of a network. The incorporation of the Advanced formula KPI, specifically the erlang KPI, enables a precise estimation of the network's capacity to handle traffic. This information is crucial for optimizing network performance and resource allocation. Secondly, the present invention introduces Automated Analysis capabilities to the KPI framework. Complex operations such as erlang and inverse calculations are seamlessly integrated into the formula, providing enhanced flexibility in automated report generation. Previously, these tasks were conducted offline, but with the Advanced KPI Formula, the process is streamlined, saving time and effort. Furthermore, the Advanced KPI Formula excels in Pattern Finding. Its advanced KPIs enable the identification of complex patterns over specific time periods. Additionally, statistical parameters like mode can be derived, offering valuable insights into the data distribution. These pattern-finding capabilities empower users to detect trends, anomalies, and performance fluctuations, facilitating proactive decision-making and troubleshooting. In summary, the presentinvention’s technical advantages lie in the network effectiveness, automated analysis, and pattern finding features offered by the advanced KPI formula. These advancements contribute to improved network optimization, streamlined analysis processes, and the ability to identify meaningful patterns within performance data.

[0095] While considerable emphasis has been placed herein on the disclosed implementations, it will be appreciated that many implementations can be made and that many changes can be made to the implementations without departing from the principles of the present disclosure. These and other changes in the implementations of the present disclosure will be apparent to those skilled in the art, whereby it is to be understood that the foregoing descriptive matter to be implemented is illustrative and non-limiting.

Claims

We Claim:

1. A method [400] for generating and provisioning a Key Performance Indicator (KPI), the method [400] comprising: receiving, by a transceiver unit [302] from a User Equipment (UE) [306], a Key Performance Indicator (KPI) provisioning request, wherein the KPI provisioning request comprises a list of KPI parameters associated with a network; extracting, by a processing unit [304], at least one of a plurality of KPI parameters from the received list of KPI parameters; and generating, by the processing unit [304], a plurality of updated KPI parameters based on the extracted at least one of the plurality of KPI parameters, wherein the plurality of updated KPI parameters is generated based on a set of pre-defined network policies applied to the extracted at least one of the plurality of KPI parameters, the set of pre-defined policies comprising one of an inverse function, a mode function, and an erlang function.

2. The method [400] as claimed in claim 1, wherein the method [400] further comprises receiving, by the transceiver unit [302], the KPI provisioning request from the UE [306] via a load balancer [308],3. The method [400] as claimed in claim 2, wherein the load balancer [308] is configured to receive the KPI provisioning request from at least one of a plurality of UEs in a roundrobin scheduling.

4. The method [400] as claimed in claim 2, wherein the method [400] further comprises: receiving, by the transceiver unit [302], the KPI provisioning request during one of a plurality of available time intervals of a system [300], and wherein the plurality of available time intervals is determined by the load balancer [308],5. The method [400] as claimed in claim 4, wherein the plurality of available time intervals is determined by the load balancer based on at least one or more network events associated with the network, and wherein the one or more network events comprise at least one of a call drop rate event, a call set up time event, a voice quality event, and a video quality event.

6. The method [400] as claimed in claim 1, wherein, based on at least one of the plurality of generated updated KPI parameters, the method [400] further comprises: generating, by the processing unit [304], an updated KPI list; and- transmitting, by the transceiver unit [302], the updated KPI list to at least one of the plurality of UEs.

7. A system [300] for generating and provisioning a Key Performance Indicator (KPI), the system [300] comprising: a transceiver unit [302], wherein the transceiver unit [302] is configured to: o receive, from a User Equipment (UE), a Key Performance Indicator (KPI) provisioning request, wherein the KPI provisioning request comprises a list of KPI parameters associated with a network; and a processing unit [304] connected at least with the transceiver unit [302], wherein the processing unit [304] is configured to: o extract at least one of a plurality of KPI parameters from the received list of KPI parameters; and o generate a plurality of updated KPI parameters based on the extracted at least one of the plurality of KPI parameters, wherein the plurality of updated KPI parameters is generated based on a set of pre-defined network policies applied to the extracted at least one of the plurality of KPI parameters, the set of pre-defined policies comprising one of an inverse function, a mode function, and an erlang function.

8. The system [300] as claimed in claim 7, wherein the transceiver unit [302] is further configured to receive the KPI provisioning request from the UE [306] via a load balancer [308],9. The system [300] as claimed in claim 8, wherein the load balancer [308] receives the KPI provisioning request from at least one of a plurality of UEs in a round-robin scheduling.

10. The system [300] as claimed in claim 8, wherein the transceiver unit [302] is further configured to: receive the KPI provisioning request during one of a plurality of available time intervals of the system, wherein the plurality of available time intervals is determined by the load balancer.

11. The system [300] as claimed in claim 10, wherein the plurality of available time intervals is determined by the load balancer based on at least one or more network events associated with the network, wherein the one or more network events comprise at least one of a call drop rate event, a call set up time event, a voice quality event, and a video quality event.

12. The system [300] as claimed in claim 7, wherein based on at least one of the plurality of generated updated KPI parameters,- the processing unit [304] is configured to generate an updated KPI list; and- the transceiver unit [302] is configured to transmit the updated KPI list to at least one of the plurality of UEs [306],13. A user equipment (UE), comprising: a transceiver unit [302] configured to: o transmit a Key Performance Indicator (KPI) provisioning request, wherein the KPI provisioning request comprises a list of KPI parameters associated with a network to a system; and o receive, from a system, a plurality of updated KPI parameters, wherein the plurality of updated KPI parameters is generated by the system based on the extracted at least one of the plurality of KPI parameters from the list of KPI parameters included in the KPI provisioning request, and wherein the plurality of updated KPI parameters is generated by the system based on a set of pre-defined network policies applied to the extracted at least one of the plurality of KPI parameters, the set of pre-defined policies comprising one of an inverse function, a mode function, and an erlang function.

14. A non-transitory computer-readable storage medium, storing instructions for generating and provisioning a key performance indicator (KPI), the instruction comprising executable code which, when executed by one or more units of a system, causes:- a transceiver unit [302] to: o receive, from a User Equipment (UE), a Key Performance Indicator (KPI) provisioning request, wherein the KPI provisioning request comprises a list of KPI parameters associated with a network; and- a processing unit [304] to: o extract at least one of a plurality of KPI parameters from the received list of KPI parameters; andgenerate a plurality of updated KPI parameters based on the extracted at least one of the plurality of KPI parameters, wherein the plurality of updated KPI parameters is generated based on a set of pre-defined network policies applied to the extracted at least one of the plurality of KPI parameters, the set of pre- defined policies comprising one of an inverse function, a mode function, and an erlang function.

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