Method and system for performing real-time analysis of kpis to monitor performance of network

Abstract

The present disclosure relates to method [400] and system [300] for performing real-time analysis of (KPIs) to monitor performance of network The method [400] includes rendering, by a user interface (UI) server, interface on user device. The interface enables user to select KPIs for analysis in a pre-defined time. The method further includes receiving, at IPM module [304], a user input comprising request for execution of the selected KPIs for the pre-defined time mentioned in the request. The method [400] further includes analysing, by a Computation Layer module [306] associated with IPM module [304], the selected KPIs in case the pre-defined time exceeds database retention period for past data. Further, the method [400] includes transmitting, by IPM module [304], a notification upon successful execution and analysis of the selected one or more KPIs. Furthermore, the method [400] includes rendering, by UI server, the analysis of selected KPIs on the interface.

Description

METHOD AND SYSTEM FOR PERFORMING REAL-TIME ANALYSIS OF KPIs TO MONITOR PERFORMANCE OF NETWORKTECHNICAL FIELD

[0001] Embodiments of the present disclosure generally relate to network performance management systems. More particularly, embodiments of the present disclosure relate to method and system for performing real-time analysis of Key Performance Indicators (KPIs) to monitor performance 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, combine and process such data to determine key performance indicators (KPI) of the network. 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] In the network performance management systems, the key performance indicator (KPI) values play a critical role and act as metrics for some real-world problems. Therefore, for the network operators it is important to analyse the KPI values. Also, in addition to the analysis of the KPI values, a comparison of the past KPI values with current KPI values is also important. However, currently there is no existing solution that can efficiently analyse and compare the past values of a KPI to the current values and provide delta monitoring analysis, where such delta monitoring analysis help in getting the trend in terms of the absolute change as well as the percentage change. Additionally, the existing solution of KPI analysis have various otherlimitations such as these solutions are inefficient, fail to allow the selection of multiple KPIs in a dashboard to monitor and fail to apply the understanding to the real-world scenarios.

[0005] 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

[0006] 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.

[0007] An aspect of the present disclosure may relate to a method for performing real-time analysis of Key Performance Indicators (KPIs) to monitor performance of a network. The method includes rendering, by a user interface (UI) server, an interface on a user device, wherein the interface enables a user to select one or more KPIs for the analysis in a pre-defined time period. The method further includes receiving, at an Integrated Performance Management (IPM) module, a user input comprising a request for execution of the selected one or more KPIs for the pre-defined time period mentioned in the request. Furthermore, the method encompasses analysing, by a Computation Layer (CL) module associated with IPM module, the one or more selected KPIs in case the pre-defined time period exceeds a database retention period for past data. The method further includes transmitting, by the IPM module, a notification on a notification panel in the interface upon successful execution and analysis of the selected one or more KPIs. Further, the method includes rendering, by the UI server, the analysis of the one or more selected KPIs on the interface.

[0008] In an exemplary aspect of the present disclosure, the user input further includes selection of one of a first KPI analytics procedure and a second KPI analytics procedure for the analysis of the one or more selected KPIs.

[0009] In an exemplary aspect of the present disclosure, upon selection of the first KPI analytics procedure, the method further comprises analysing, by the CL module, the one or more selected KPIs using: KPI (base date) = KPI (base date) - operation (KPI(base date - 1 day), KPI(base date - 2 days), . . . KPI(base date - n days)), wherein n corresponds to number of past days as per the predefined time period, wherein the base date is received in the request.

[0010] In an exemplary aspect of the present disclosure, upon selection of the second KPI analytics procedure, the method further comprising analysing, by the CL module, the one or more KPIs using: KPI (base date) = operation (KPI(base date), KPI(base date - 1 day), KPI(base date - 2 days), ... KPI(base date - n days)), wherein n corresponds to number of past days as per the predefined time period, wherein the base date is received in the request.

[0011] In an exemplary aspect of the present disclosure, the operation comprises use of one of an addition function, a subtraction function, a maximum function, and an average function in the first KPI analytics procedure and the second KPI analytics procedure to facilitate real-time analysis of the one or more selected KPIs.

[0012] Another aspect of the present disclosure may relate to a system for performing real-time analysis of Key Performance Indicators (KPIs) to monitor performance of a network. The system comprises a User Interface (UI) server, an Integrated Performance Management (IPM) module and a computation layer connected with each other either directly or indirectly. The system includes the user interface (UI) server. The UI server is configured to render an interface on a user device, wherein the interface enables a user to select one or more KPIs for the analysis in a pre-defined time period. The system further includes the Integrated Performance Management (IPM) module, configured to receive a user input comprising a request for execution of the selected one or more KPIs for the pre-defined time period mentioned in the request. Furthermore, the system includes the Computation Layer (CL) module associated with the IPM module, the CL module is configured to analyse the one or more selected KPIs in case the pre-defined time period exceeds database retention period for past data. Further, the IPM module is configured to transmit a notification on a notification panel in the interface upon, successful execution and analysis of the selected one or more KPIs. The UI server is further configured to render the analysis of the one or more selected KPIs on the interface.

[0013] Yet another aspect of the present disclosure may relate to a user equipment for performing real-time analysis of KPIs to monitor performance of a network. The user equipment (UE) comprising a processor. The processor is configured to render an interface on a display device, wherein the interface enables a user to select one or more KPIs for performing real-time analysis of Key Performance Indicators (KPIs) to monitor performance of a network in a pre-defined time period. The processor is further configured to receive a user input comprising a request for execution of the selected one or more KPIs for the pre-defined time period mentioned in therequest. Furthermore, the processor is configured to analyse, via a UI server, the one or more selected KPIs in case the pre-defined time period exceeds database retention period for past data. The processor is configured to receive a notification on a notification panel in the interface upon successful execution and analysis of the selected one or more KPIs. Further, the processor is configured to render the analysis of the one or more selected KPIs on the interface.

[0014] Yet another aspect of the present disclosure may relate to a non-transitory computer readable storage medium storing instructions for performing real-time analysis of Key Performance Indicators (KPIs) to monitor performance of a network, the instructions include executable code which, when executed by one or more units of a system, causes: a user interface (UI) server of the system to render an interface on a user device, wherein the interface enables a user to select one or more KPIs for the analysis in a pre-defined time period. Further, the instructions include executable code, which when executed causes an Integrated Performance Management (IPM) module of the system to receive a user input comprising a request for execution of the selected one or more KPIs for the pre-defined time period mentioned in the request. Further, the instructions include executable code, which when executed causes a Computation Layer (CL) module of the system associated with the IPM module to analyse the one or more selected KPIs in case the pre-defined time period exceeds database retention period for past data. Further, the instructions include executable code, which when executed causes the IPM module of the system to transmit a notification on a notification panel in the interface upon successful execution and analysis of the selected one or more KPIs. Further, the instructions include executable code, which when executed causes the UI server of the system to render the analysis of the one or more selected KPIs on the interface.OBJECTS OF THE INVENTION

[0015] Some of the objects of the present disclosure, which at least one embodiment disclosed herein satisfies are listed herein below.

[0016] It is an object of the present disclosure to provide delta monitoring analysis of KPI values in a network performance management system.

[0017] It is an object of the present disclosure to provide a solution that analyse and compare the past values of a KPI to the current values, to provide Delta Monitoring Analysis for getting the trend in terms of the absolute change as well as the percentage change.

[0018] It is an object of the present disclosure to provide a solution at the KPI level, hence one can select multiple KPIs in a dashboard to monitor and apply the understanding to the real-world scenarios.

[0019] It is yet another object of the present disclosure to provide a solution that, when a plan is launched, can gauge its impact on the lives of people in order to understand how far in near future this plan could generate a good revenue and add in more customers to the base.DESCRIPTION OF THE DRAWINGS

[0020] 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.

[0021] FIG. 1 illustrates an exemplary block diagram of a network performance management system

[0100] in accordance with exemplary implementations of the present disclosure.

[0022] 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.

[0023] FIG. 3 illustrates an exemplary block diagram of a system for performing real-time analysis of Key Performance Indicators (KPIs) to monitor performance of a network, in accordance with exemplary implementations of the present disclosure.

[0024] FIG. 4 illustrates a method flow diagram for performing real-time analysis of Key Performance Indicators (KPIs) to monitor performance of a network in accordance with exemplary implementations of the present disclosure.

[0025] FIG. 5 illustrates an exemplary architecture of a delta monitoring analysis system

[0500] , in accordance with the exemplary embodiments of the present invention, in accordance with the exemplary implementations of the present disclosure.

[0026] FIG. 6 illustrates an implementation of the exemplary process of performing real-time analysis of Key Performing Indicators (KPIs) to monitor performance of a network, in accordance with the exemplary implementations of the present disclosure.

[0027] The foregoing shall be more apparent from the following more detailed description of the disclosure.DETAILED DESCRIPTION

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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 oneinput 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.

[0035] 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 or similar 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.

[0036] 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 referred 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.

[0037] 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.

[0038] As discussed in the background section, the current known solutions have several shortcomings. The present disclosure aims to overcome the problems mentioned in the background section and other existing problems in this field of technology by providing method and system of providing delta monitoring analysis of KPI values in a network performance management system. Generally, the people, who perform the monitoring and observation tasks, take note of every kind of changes happening in the KPIs. Normally, to take note of said changes happening in the KPIs, a user would download an excel report from a dashboard page and perform some calculations in excel to get an increment or decrement or none type changes for the date the user has chosen. Graph can be used to visualize the increment or decrement. The delta monitoring analysis system or the delta monitoring analysis feature / procedure as disclosed in the present disclosure efficientlyreduces time spent in any tedious work. On top of providing a delta for all the chosen dates, the delta monitoring analysis procedure utilizes the stored pre-computed KPI data to perform the realtime calculation and output delivery. The logic therefore is executed for the stored values of the counters in the database before displaying the output. Conclusively, this allows the user to point out any anomaly or abnormality in the network.

[0039] Particularly, to analyse and compare the past values of a KPI to the current values of the KPI, a Delta Monitoring Analysis procedure is provided for getting the trend in terms of the absolute change as well as the percentage change. The “Delta” to be calculated is not just on the past absolute values of the KPI, rather their average (or the selected operation from the options) are taken along with the difference with the current value of the KPI. In addition, the delta can be computed for all the past dates by taking the base date as the past date. Also, getting the real-time output makes the delta monitoring analysis procedure unique and technically advanced over the existing systems.

[0040] Moreover, this delta monitoring analysis procedure is applicable at the KPI level, hence the user may select multiple KPIs in a dashboard to monitor and apply the understanding to the real-world scenarios. For example, when a plan is launched, it is essential to gauge its impact on the lives of people in order to understand how far in near future this plan could generate a good revenue and add in more customers.

[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. Referring to Fig. 1, the network performance management system

[0100] comprises various sub-systems such as: an integrated performance management system [100a], a normalization layer [100b], a computation layer [lOOd], an anomaly detection layer [lOOo], a streaming engine

[1001] , a load balancer [100k], an operations and management system [1 OOp], an API gateway system [ 1 OOr], an analysis engine [lOOh], a parallel computing framework [lOOi], a forecasting engine [lOOt], a distributed file system, a mapping layer [100s], a distributed data lake [lOOu], a scheduling layer [100g], a reporting engine [100m], a message broker [1 OOe], a graph layer [1 OOf], a caching layer [100c], a service quality manager [lOOq] and a correlation engine[100n]. Exemplary connections between these 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 various subsystems that are needed to realise the effects are within the scope of this disclosure.

[0042] Following are the various components of the network performance management system

[0100] , the various components may include:

[0043] Integrated performance management system [100a] comprise of a 5G performance management engine [lOOv] and a 5G Key Performance Indicator (KPI) Engine [100w],

[0044] 5G Performance Management Engine [100v]: The 5G Performance Management engine [lOOv] is a crucial component of the integrated performance management system [100a], responsible for collecting, processing, and managing performance counter data from various data sources within the network. The counter data includes metrics such as connection speed, latency, data transfer rates, and many others. The counter 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 [100u], The Distributed data lake [lOOu] is a centralized, scalable, and flexible storage solution, allowing for easy access and further analysis. The 5G Performance Management engine [lOOv] also enables the reporting and visualization of the 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. An operator of the integrated performance monitoring system [100a] may be an individual, a device, an administrator, and the like who may interact with various components of the network performance management system

[0100] or may manage the network.

[0045] 5G Key Performance Indicator (KPI) Engine [100w]: The 5G Key Performance Indicator (KPI) Engine [lOOw] is a dedicated component tasked with managing the KPIs of all the network elements. The 5G Key Performance Indicator (KPI) Engine [lOOw] uses the performance counters, which are collected and processed by the 5G Performance Management engine [lOOv] from various data sources. These counters, encapsulating crucial performance data, are harnessed by the 5G KPI engine [lOOw] to calculate essential KPIs. These KPIs may include at least one of: data throughput, latency, packet loss rate, and more. Once the KPIs are computed, the KPIs are segregated based on the aggregation requirements, offering a multi-layered and detailed understanding of the network performance. The processed KPI data is then stored in the Distributed Data Lake [lOOu], ensuring a highly accessible, centralized, and scalable data repository for further analysis and utilization. Similar to the 5G Performance Management engine [lOOv], the 5G KPI engine [lOOw] 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: The Ingestion layer forms a key part of the Integrated Performance Management system [100a], The ingestion layer primarily performs the function to establish an environment capable of handling diverse types of incoming data. This data may include Alarms, Counters, Configuration parameters, Call Detail Records (CDRs), Infrastructure metrics, Logs, and Inventory data, all of which are crucial for maintaining and optimizing the network's performance. Upon receiving this data, the Ingestion layer processes the data by validating the data integrity and correctness to ensure the data fit for further use. Following validation, the data is routed to various components of the Integrated Performance Management system [100a], including the Normalization layer, Streaming Engine, Streaming Analytics, and Message Brokers. The destination is chosen based on where the data is required for further analytics and processing. By serving as the first point of contact for incoming data, the Ingestion layer plays a vital role in managing the data flow within the system, thus supporting comprehensive and accurate network performance analysis.

[0047] Normalization layer [100b]: The Normalization Layer [100b] serves to standardize, enrich, and store data into the appropriate databases. It takes in data that has been ingested 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 [lOOu], Caching Layer, and Graph Layer, depending on its intended use. The choice of storage determines how the data can be accessed and used in the future. Additionally, the Normalization Layer [100b] produces data for the Message Broker, a system that enables communication between different parts of the performance management system through the exchange of data messages. Moreover, the Normalization Layer [100b] supplies the standardized data to several other subsystems. These include the Analysis Engine for detailed data examination, the Correlation Engine [lOOn] for detecting relationships among various data elements, the Service Quality Manager for maintaining and improving the quality of services, and the Streaming Engine for processing real-time data streams. These subsystems depend on the normalized data to perform their operations effectively and accurately, demonstrating the Normalization Layer's [100b] critical role in the entire system.

[0048] Caching layer [100c]: The Caching Layer [100c] in the Integrated Performance Management system [100a] plays a significant role in data management and optimization. During the initial phase, the Normalization Layer [100b] processes incoming raw data to create a standardized format, enhancing consistency and comparability. The Normalizer Layer then inserts this normalized data into various databases. One such database is the Caching Layer [100c], The Caching Layer [100c] is a high-speed 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 [100c], the system significantly reduces the time taken to access this data, improving overall system efficiency and performance. Further, the Caching Layer [100c] serves as an intermediate layer between the data sources and the sub-systems, such as the Analysis Engine, Correlation Engine [lOOn], Service Quality Manager, and Streaming Engine. The Normalization Layer [100b] is responsible for providing these sub-systems with the necessary data from the Caching Layer [100c],

[0049] Computation layer [100d]: The Computation Layer [lOOd] in the Integrated Performance Management system 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 [100b], The Normalizer Layer then inserts this standardized data into multiple databases including the Distributed Data Lake [lOOu], Caching Layer [100c], and Graph Layer, and also feeds it to the Message Broker. Within the Computation Layer [ 1 OOd], several powerful sub-systems such as the Analysis Engine, Correlation Engine [lOOn], Service Quality Manager, and Streaming Engine, utilize the normalized data. These systems are designed to execute various data processing tasks. The Analysis Engine performs in-depth data analytics to generate insights from the data. The Correlation Engine [lOOn] identifies and understands the relations and patterns within the data. The Service Quality Manager assesses and ensures the quality of the services. And the Streaming Engine processes and analyses the real-time data feeds. In essence, the Computation Layer [lOOd] is where all major computation and data processing tasks occur. It uses the normalized data provided by the Normalization Layer [100b], processing it to generate useful insights, ensure service quality, understand data patterns, and facilitate real-time data analytics.

[0050] Message broker [100e]: The Message Broker [lOOe], an integral part of the Integrated Performance Management system [100a], 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 [lOOe] facilitates communication between data producers and consumers through message-based topics. This creates an advanced platform for contemporary distributedapplications. With the ability to accommodate a large number of permanent or ad-hoc consumers, the Message Broker [lOOe] demonstrates immense flexibility in managing data streams. Moreover, it leverages the file system for storage and caching, boosting its speed and efficiency. The design of the Message Broker [lOOe] is centred around reliability. It 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 [lOOe] forms a critical component in managing and delivering real-time data in the system.

[0051] Graph layer

[1001] : The Graph Layer [ 1 OOf], serving as the Relationship Modeler, plays a pivotal role in the Integrated Performance Management system [100a], It can model a variety of data types, including alarm, counter, configuration, CDR data, Infra-metric data, 5G Probe Data, and Inventory data. Equipped with the capability to establish relationships among diverse types of data, the Relationship Modeler offers extensive modelling capabilities. For instance, it can model Alarm and Counter data, Probe and Alarm data, elucidating their interrelationships. Moreover, the Modeler should be adept at processing steps provided in the model and delivering the results to the system requested, whether it be a Parallel Computing system, Workflow Engine, Query Engine, Correlation System [lOOn], 5G Performance Management Engine, or 5G KPI Engine [lOOw], With its powerful modelling and processing capabilities, the Graph Layer [1 OOf] forms an essential part of the system, enabling the processing and analysis of complex relationships between various types of network data.

[0052] Scheduling layer [100g]: The Scheduling Layer [100g] serves as a key element of the Integrated Performance Management System [100a], endowed with the ability to execute tasks at predetermined intervals set according to user preferences. A task might be an activity 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 [lOOu] or Distributed File System or sending it to another micro- service. The versatility of the Scheduling Layer [100g] extends to facilitating graph traversals via the Mapping Layer to execute tasks. This crucial capability enables seamless and automated operations within the system, ensuring that various tasks and services are performed on schedule, without manual intervention, enhancing the system's efficiency and performance. In sum, the Scheduling Layer [100g] orchestrates the systematic and periodic execution of tasks, making it an integral part of the efficient functioning of the entire system.

[0053] Analysis Engine [100h]: The Analysis Engine [lOOh] forms a crucial part of the Integrated Performance Management System [100a], designed to provide an environment where users canconfigure 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 [ 1 OOh], 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 system's flexibility allows users to configure specific policies aimed at identifying anomalies within the data. When these policies detect abnormal behaviour or policy breaches, the system sends notifications, ensuring swift and responsive action. In essence, the Analysis Engine [lOOh] provides a robust analytical environment for systematic data interrogation, facilitating efficient problem identification and resolution, thereby contributing significantly to the system's overall performance management.

[0054] Parallel Computing Framework [lOOi] : The Parallel Computing Framework [lOOi] is a key aspect of the Integrated Performance Management System [100a], providing a user-friendly yet advanced platform for executing computing tasks in parallel. The parallel computing framework [lOOi] highlights both scalability and fault tolerance, crucial for managing vast amounts of data. Users can input data via Distributed File System (DFS) [lOOj] locations or Distributed Data Lake (DDL) indices. The framework supports the creation of task chains by interfacing with the Service Configuration Management (SCM) Sub-System. Each task in a workflow is executed sequentially, but multiple chains can be executed simultaneously, optimizing processing time. To accommodate varying task requirements, the service supports the allocation of specific host lists for different computing tasks. The Parallel Computing Framework [lOOi] is an essential tool for enhancing processing speeds and efficiently managing computing resources, significantly improving the system's performance management capabilities.

[0055] Distributed File System [lOOj] : The Distributed File System (DFS) [lOOj] is a critical component of the Integrated Performance Management System [100a], enabling multiple clients to access and interact with data seamlessly. The Distributed File system [lOOj] 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 [lOOj] effectively allows for the distribution of data across multiple nodes. This architecture enhances both the scalability and redundancy of the system, ensuring optimal performance even with large data sets. DFS [lOOj] also supports diverse operations, facilitating the flexible interaction with and manipulation of data. This accessibility is paramount for a system that requires constant data input and output, as is the case in a robust performance management system.

[0056] Load Balancer [100k]: The Load Balancer (LB) [100k] is a vital component of the Integrated Performance Management System [100a], designed to efficiently distribute incoming network traffic across a multitude of backend servers or microservices. Its purpose is to ensure the even distribution of data requests, leading to optimized server resource utilization, reduced latency, and improved overall system performance. The LB [100k] implements various routing strategies to manage traffic. The LB [100k] includes round-robin 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. Header-based dispatching routes requests based on data contained within the headers of the Hypertext Transfer Protocol (HTTP) requests. Contextbased dispatching routes traffic based on the contextual information about the incoming requests. For example, in an event-driven architecture, the LB [100k] manages event and event acknowledgments, forwarding requests or responses to the specific microservice that has requested the event. This system ensures efficient, reliable, and prompt handling of requests, contributing to the robustness and resilience of the overall performance management system.

[0057] Streaming Engine

[1001] : The Streaming Engine

[1001] , also referred to as Stream Analytics, is a critical subsystem in the Integrated Performance Management System [100a], This engine is specifically designed for high-speed data pipelining to the User Interface (UI). Its core objective is to ensure real-time data processing and delivery, enhancing the system's ability to respond promptly to dynamic changes. Data is received from various connected subsystems and processed in real-time by the Streaming Engine

[1001] , After processing, the data is streamed to the UI, fostering rapid decision-making and responses. The Streaming Engine

[1001] cooperates with the Distributed Data Lake [lOOu], Message Broker [lOOe], and Caching Layer [100c] to provide seamless, real-time data flow. Stream Analytics 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, this system can also retrieve data from the Distributed Data Lake [lOOu], Message Broker [lOOe], and Caching Layer [100c] as per the requirement and deliver it to the UI in real-time. The streaming engine's

[1001] is configured to provide fast, reliable, and efficient data streaming, contributing to the overall performance of the Integrated Performance Management System [100a],

[0058] Reporting Engine [100m]: The Reporting Engine [100m] is a key subsystem of the Integrated Performance Management System [100a], The fundamental purpose of designing the Reporting Engine [100m] is to dynamically create report layouts of API data, catered to individualclient requirements, and deliver these reports via the Notification Engine. The REM serves as the primary interface for creating custom reports based on the data visualized through the client's dashboard. These custom dashboards, created by the client through the User Interface (UI), provide the basis for the Reporting Engine [100m] to process and compile data from various interfaces. The main output of the Reporting Engine [100m] is a detailed report generated in Excel format. The Reporting Engine’s [100m] unique capability 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 this performance management system. Furthermore, the Reporting Engine [100m] integrates seamlessly with the Notification Engine to ensure timely and efficient delivery of reports to clients via email, ensuring the information is readily accessible and usable, thereby improving overall client satisfaction and system usability.

[0059] Further, a computing device on which the units of the integrated performance management system [100a] may be implemented is illustrated in FIG. 2.

[0060] 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 performing real-time analysis of Key Performance Indicators (KPIs) to monitor performance of a network utilising the system. In another implementation, the computing device

[0200] itself implements the method for performing real-time analysis of Key Performance Indicators (KPIs) to monitor performance of a network 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.

[0061] 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 theinstructions. 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] ,

[0062] 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 computing device

[0200] may be coupled via the bus

[0202] to a display

[1012] , 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

[1014] , 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.

[0063] 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.

[0064] 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] maybe 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 several types of information.

[0065] 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 host

[0224] , the local network

[0222] 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.

[0066] The functionalities of the computing device

[0200] may be performed by a system of FIG. 3.

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

[0300] for performing realtime analysis of Key Performance Indicators (KPIs) to monitor performance of a network, is shown, in accordance with the exemplary implementations of the present disclosure. The system

[0300] comprises at least one user interface server

[0302] , at least one integrated performance management (IPM) module

[0304] , and at least one computation layer module

[0306] , Also, all 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

[0300] 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] may reside 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. The system

[0300] may be associated with a user equipment (UE)

[0308] ,

[0068] The system

[0300] is configured for performing real-time analysis of Key Performance Indicators (KPIs) to monitor performance of a network, with the help of the interconnection between the components / units of the system

[0300] ,

[0069] The system

[0300] includes the user interface (UI) server

[0302] , The UI server

[0302] is configured to render an interface on a user device. The user device may include a smartphone, a laptop, a computer, and the like. The interface enables a user to select one or more KPIs for the analysis in a pre-defined time period. The KPIs may include a packet loss, a throughput, a jitter, a latency, and the like. The packet loss is when small packets fail to reach their destination. The throughput refers to the duration of time in which the packets of data that may be sent to the destination. The jitter refers to variation in the time during which data packets may reach their destination. The latency refers to the time taken by a data packet to from one place to another destination. The user may be at least one of an individual, a group of individuals, an administrator, and the like. The user may interact with the one or more units of the system

[0300] via the network or the user may monitor the network. Further, the user may provide a user input. The user input includes selection of one of a first KPI analytics procedure and a second KPI analytics procedure for the analysis of the one or more selected KPIs. The KPIs are selected from user interface (UI) via a drop-down menu.

[0070] In an implementation of the present disclosure, Key Performance Indicators (KPIs) facilitate in monitoring and analysing multiple aspects of operations, service delivery, and user satisfaction in a telecommunication network. For instance, a person who may monitor the performance of a telecommunication network, may use various KPIs to analyse the performance of a telecommunication network. The KPIs include throughput, reliability, availability, and the like. For example- a user, who monitors the telecommunication network, may choose a KPI through the user interface

[0302] which the user wants to monitor. The user may also mention the time period, for instance 2 months, for which the monitoring is to be done.

[0071] The UI server

[0302] is configured to render the analysis of the one or more selected KPIs on the interface. In an implementation of the present disclosure, when a user accesses the UI server

[0302] , the UI server

[0302] presents the user with a list of KPIs to select from that allows them to select the KPIs they want to analyse. For example, the user may choose to analyse “call drop rate” KPIs.

[0072] The Integrated Performance Management (IPM) module

[0304] is configured to receive the user input. The user input may be received by the user device via the interface. The user input comprises a request or data request for execution of the selected one or more KPIs for the predefined time period mentioned in the request. For example, the user input includes three KPIswhich are to be monitored for a period of 15 days. The KPI’s may be selected from download speed, upload speed, latency rate, signal strength and the like. The IPM module

[0304] is configured to transmit a notification on a notification panel in the interface upon successful execution and analysis of the selected one or more KPIs.

[0073] In an implementation of the present disclosure, the IPM module

[0304] may receive the selected KPI and the time period of which the monitoring needs to be performed, i.e., call drop rate for 2 months. On completion of the analysis, monitoring, and calculation of a delta calculation, the IPM module

[0304] is further configured to send a notification regarding the delta calculation. The delta calculation refers to calculating the difference between the KPIs. The notification may notify the user of the successful execution and analysis of the selected one or more KPIs. The user may click on the notification to get the result from the IPM module

[0304] ,

[0074] The Computation Layer (CL) module

[0306] is associated with the IPM module

[0304] , In addition, the CL module

[0306] is configured to analyse the one or more selected KPIs in case the pre-defined time period exceeds database retention period for past data. To analyse the one or more selected KPIs, the CL module

[0306] checks for the selected KPIs from a precomputed data in hot cache and if not available in a hot cache (contains data that has been recently or frequently accessed) then the CL module

[0306] checks in a Data Definition Language (DDL) (such as SQL database) and provide the data. Further, the selected KPIs are not found in the DDL, the CL module

[0306] computes the given KPI and stores it in the hot cache and the DDL and further provides output to the user. Upon selection of the first KPI analytics procedure, the CL module

[0306] may analyse the one or more selected KPIs using a KPI base date which is equal to result of an operation performed on KPI values from the base date and several preceding days (“n” days), wherein n corresponds to number of past days as per the predefined time period. The predefined time period may be defined by the user to access the KPI data for the said time period. For instance, if the user mentions the predefined time period as 5 days, then the system

[0300] may compute the KPI data for 5 days. The base date is received in the request. Upon selection of the second KPI analytics procedure, the CL module

[0306] is configured to analyse the one or more selected KPIs using the Key Performance Indicator (KPI) value on a specified base date as the result of an operation involving the KPI values from the base date, subtracting 1 day from the KPI values from base date and several preceding days up to 'n' days, wherein n corresponds to number of past days as per the predefined time period, wherein the base date is received in the request. The operation comprises use of one of addition function, subtraction function, maximum function, and average function inthe first KPI analytics procedure and the second KPI analytics procedure to facilitate real-time analysis of the one or more selected KPIs.

[0075] In an implementation of the present disclosure, the data request may be forwarded to the CL module

[0306] in cases where the pre-defined time period in the data request is more than the database retention period in the IPM module

[0304] , After the data request is received by the computation layer module

[0306] , the computation layer module

[0306] may send an acknowledgement message back to the IPM module

[0304] , The acknowledgement message may indicate that the delta monitoring is being performed for the data request. For instance, a threshold period of data storage in the IPM module

[0304] is 1 month, but the data request is for 2 months data.

[0076] The predefined time period set by the user exceeds or breaches the threshold of the IPM module

[0304] , The IPM module

[0304] in this case, may forward the data request to the Computation Layer module

[0306] which stores all the data.

[0077] The delta is calculated by determining:1. a KPI base date which is equal to result of an operation performed on KPI values from the base date and several preceding days (“n” days), where ‘n’ as used in the analysing, corresponds to number of past days as per the predefined time period.2. the Key Performance Indicator (KPI) value on a specified base date as the result of an operation involving the KPI values from the base date, subtracting 1 day from the KPI values from base date and several preceding days up to 'n' days, where “n” corresponds to number of past days as per the predefined time period.

[0078] The working of the one or more units of the system

[0300] of FIG. 3, is explained elaborately in FIG. 4.

[0079] Referring to FIG. 4, an exemplary method flow diagram

[0400] for performing real-time analysis of Key Performance Indicators (KPIs) to monitor performance of a network, 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] ,

[0080] At step

[0404] , the method comprises rendering, by the user interface (UI) server

[0302] , the interface on the user device, wherein the interface enables the user to select one or more KPIs for the analysis in a pre-defined time period. The KPIs may include a packet loss, a throughput, a jitter, a latency, and the like. The packet loss refers to when small packets fail to reach their destination. The throughput refers to the duration of time in which the packets of data that may be sent to the destination. The jitter refers to variation in the time during which data packets may reach their destination. The latency refers to the time taken by a data packet to from one place to another destination. The user input further includes selection of one of a first KPI analytics procedure and a second KPI analytics procedure for the analysis of the one or more selected KPIs.

[0081] In an implementation of the present disclosure, Key Performance Indicators (KPIs) play an important role in monitoring and analysing multiple aspects of operations, service delivery, and user satisfaction in a telecommunication network. For instance, a person who may monitor the performance of a telecommunication network, may use various KPIs to analyse the performance. The KPIs include throughput, reliability, availability, and the like. For example- a user, who monitors the telecommunication network, may choose a KPI through the user interface

[0302] which they have to monitor. The user device may include a smartphone, a laptop, a computer, an internet of things (loT), and the like. The interface enables a user to select one or more KPIs for the analysis in a pre-defined time period. The user in the network performance monitoring system may be an individual, a device, an administrator, and the like who may interact with or monitor the network. The user may also mention the time period, for instance 2 months, for which the monitoring is to be done.

[0082] At step

[0406] , the method includes receiving, at an Integrated Performance Management (IPM) module

[0304] , a user input comprising a request for execution of the selected one or more KPIs for the pre-defined time period mentioned in the request.

[0083] In an implementation of the present disclosure, when a user accesses the UI server

[0302] , the UI server

[0302] presents them with a list of KPIs to select from that allows them to select the KPIs they want to analyse. For example, the user may choose to analyse the call drop rate KPIs. For example, the user input includes the 3 KPIs which are to be monitored for a period of 15 days. The KPI’s may be selected from download speed, upload speed, latency rate, signal strength and the like.

[0084] Next, at step

[0408] , the method includes analysing, by a Computation Layer (CL) module

[0306] associated with the IPM module

[0304] , the one or more selected KPIs in case the pre-defined time period exceeds database retention period for past data.

[0085] In an implementation of the present disclosure, the IPM module

[0304] may receive the selected KPI and the time period of which the monitoring needs to be performed, i.e., call drop rate for 2 months. Upon selection of the first KPI analytics procedure, the method further comprises analysing, by the CL module

[0306] , the one or more selected KPIs using a KPI base date which is equal to result of an operation performed on KPI values from the base date and several preceding days (“n” days), where ‘n’ as used in the analysing, corresponds to number of past days as per the predefined time period. The base date is received in the request. Upon selection of the second KPI analytics procedure, the method further comprising analysing, by the CL module

[0306] , the one or more KPIs based on the Key Performance Indicator (KPI) value on a specified base date as the result of an operation involving the KPI values from the base date, subtracting 1 day from the KPI values from base date and several preceding days up to 'n' days, where “n” corresponds to number of past days as per the predefined time period. The base date is received in the request. The operation comprises use of one of addition function, subtraction function, maximum function, and average function in the first KPI analytics procedure and the second KPI analytics procedure to facilitate real-time analysis of the one or more selected KPIs.

[0086] In an implementation of the present disclosure, the data request may be forwarded to the CL module

[0306] in cases where the pre-defined time period in the data request is more than the retention time period of the database in the IPM module

[0304] , After the data request is received by the computation layer module

[0306] , it may send an acknowledgement message back to the IPM module

[0304] , The acknowledgement message may indicate that the delta monitoring is being performed for the data request. For instance, a threshold period of data storage in the IPM module

[0304] is 1 month, but the data request is for 2 months data. A threshold period may be defined as a predefined limit which may be used for evaluating the performance of a specific Key Performance Indicator (KPI). When the measured performance metric is valued to be above the threshold period, it may trigger a set of action, and if the measured performance metric is below the threshold period, another set of actions may be initiated. The predetermined time period set by the user exceeds or breaches the threshold of the IPM module

[0304] , The IPM module

[0304] in this case, may forward the data request to the Computation Layer module

[0306] which stores all the data.

[0087] The delta is calculated by determining -1. a KPI base date which is equal to result of an operation performed on KPI values from the base date and several preceding days (“n” days), where ‘n’ as used in the analysing, corresponds to number of past days as per the predefined time period.2. the Key Performance Indicator (KPI) value on a specified base date as the result of an operation involving the KPI values from the base date, subtracting 1 day from the KPI values from base date and several preceding days up to 'n' days, where “n” corresponds to number of past days as per the predefined time period.

[0088] Further, at step

[0410] , the method includes transmitting, by the IPM module

[0304] , a notification on a notification panel in the interface upon successful execution and analysis of the selected one or more KPIs.

[0089] In an implementation of the present disclosure, on completion of the analysis and monitoring, and calculation of a delta calculation, a notification regarding the delta calculation is sent by the IPM module

[0304] to the user interface. The notification may notify the user of the successful execution and analysis of the selected one or more KPIs. The user may click on the notification to get the result from the IPM module

[0304] ,

[0090] Next, at step

[0412] , the method encompasses rendering, by the UI server

[0302] , the analysis of the one or more selected KPIs on the interface. In an implementation of the present disclosure, the analysis of the one or more selected KPIs may be displayed on the interface via the UI server

[0302] ,

[0091] At step

[0414] , the method is terminated.

[0092] Further, an exemplary architecture for the delta monitoring analysis is shown in FIG. 5.

[0093] Referring to FIG. 5, it illustrates an exemplary architecture of a delta monitoring analysis system

[0500] , in accordance with the exemplary embodiments of the present invention, in accordance with the exemplary implementations of the present disclosure.

[0094] The components of the delta monitoring analysis system

[0500] include the user interface (UI) server

[0302] , a load balancer

[0504] , the Integrated Performance Management (IPM) module

[0304] , the Computation Layer (CL) module

[0306] , a distributed file system

[0508] , and adatabase / distributed data lake

[0510] , Also, the delta monitoring analysis system

[0500] comprises a processing unit and a storage unit (not shown in the FIG. 5). The storage unit is configured to store the data required by the various units / modules of the delta monitoring analysis system

[0500] , for instance the units / modules as shown in FIG. 5, to implement the features of the present disclosure. The processing unit is configured to enable the various units / modules of the delta monitoring analysis system

[0500] , for instance the units / modules as shown in FIG. 5, to implement the features of the present disclosure.

[0095] A user may send a data request to the User Interface (UI) server

[0302] , The data request includes selecting one or more key performance indicators (KPIs) which the user may want the delta monitoring to be performed. The data request includes the information of the selected KPIs and a pre-defined time period for which the delta monitoring is to be performed. The KPI’s may be selected from download speed, upload speed, latency rate, signal strength and the like.

[0096] The UI server

[0302] further requests the Load Balancer

[0504] to perform delta monitoring for the selected KPIs. The Load Balancer

[0504] forwards the request to the Integrated Performance Management module

[0304] ,

[0097] The IPM module

[0304] receives the request from the UI server

[0302] , It may start executing the KPI request for the data range mentioned in the request by sending the data request to the Computation Layer module

[0306] , The request is forwarded to the CL module

[0306] in cases where the pre-defined time period in the data request is more than the retention time period of the database in the IPM module

[0304] , The IPM module

[0304] may not forward the request to the CL module

[0306] and fetch the required data from the distributed data lake

[0510] in cases where the predetermined time period is within the threshold limit.

[0098] Where request is forwarded to the CL module

[0306] , CL module

[0306] may access the distributed file system

[0508] to request for the monitoring data for the mentioned pre-defined time period. After the data request is received by the computation layer module

[0306] , it may send an acknowledgement message back to the IPM module

[0304] to provide the calculated and analysed data to the user.

[0099] Furthermore, the present disclosure for delta monitoring analysis of KPI values in the network performance management system

[0100] discloses that:Connections are formed between UI

[0102] to IPM

[0106] , IPM

[0106] to DB, IPM

[0106] to CL

[0108] , CL

[0108] to IPM

[0106] , IPM

[0106] to UI

[0102] ,

[0100] To calculate the current value of a KPI in delta monitoring, facilitating the user to choose one option out of two ways possible using a flag. When past n days delta is needed, these options are:1. Determining a KPI base date which is equal to result of an operation performed on KPI values from the base date and several preceding days (“n” days), where ‘n’ as used in the analysing, corresponds to number of past days as per the predefined time period.2. Determining the Key Performance Indicator (KPI) value on a specified base date as the result of an operation involving the KPI values from the base date, subtracting 1 day from the KPI values from base date and several preceding days up to 'n' days, where “n” corresponds to number of past days as per the predefined time period.

[0101] The IPM

[0106] calculates the KPI using one of the above-mentioned options for the given time interval. Also, the above-mentioned options are for day interval, but same can be applied for hour and minute level.

[0102] The method as explained in FIG. 4 is further explained as an exemplary implementation of performing a real-time analysis of KPI as shown in FIG. 6.

[0103] Referring to FIG. 6, it illustrates an implementation of the exemplary process of performing real-time analysis of Key Performing Indicators (KPIs) to monitor performance of a network, in accordance with the exemplary implementations of the present disclosure.

[0104] At step 1, the user

[0602] may send the data request to the User Interface (UI) server

[0302] , The user request includes selecting one or more key performance indicators (KPIs) which the user may want the delta monitoring to be performed. The KPI’s may be selected from download speed, upload speed, latency rate, signal strength and the like.

[0105] At step 2, the UI server

[0302] further requests the Load Balancer

[0504] to perform delta monitoring for the selected KPIs. This request includes the information of the selected KPIs and a pre-defined time period for which the delta monitoring is to be performed.

[0106] At step 3, the Load Balancer

[0504] forwards the request to the Integrated Performance Management module

[0304] ,

[0107] At step 4, once the IPM module

[0304] receives the request from the UI server

[0302] , it may start executing the KPI request for the data range mentioned in the request by sending the data request to the Computation Layer module

[0306] , The request is forwarded to the CL module

[0306] in cases where the pre-defined time period in the data request is more than the retention time period of the database in the IPM module

[0304] ,

[0108] At step 5, after the data request is received by the computation layer module

[0306] , it may send an acknowledgement message back to the IPM module

[0304] , The acknowledgement message may indicate that the delta monitoring is being performed for the request.

[0109] At step 6, the CL module

[0306] accesses the distributed file system

[0508] to request for the monitoring data for the mentioned pre-defined time period.

[0110] At step 7, the distributed file system

[0508] in response the access request, sends back the data requested to the CL module

[0306] , Connections are formed between UI server

[0302] to IPM module

[0304] , IPM module

[0304] to DB, IPM module

[0304] to CL module

[0306] , CL module

[0306] to IPM module

[0304] , IPM module

[0304] to UI server

[0302] ,[OHl] At step 8, data computation is done by the CL module

[0306] on the basis of received data. While calculating the current value of a KPI in delta monitoring, user chooses one option out of two ways possible using a flag. When past n days delta is needed, these options are:1. Determining a KPI base date which is equal to result of an operation performed on KPI values from the base date and several preceding days (“n” days), where ‘n’ as used in the analysing, corresponds to number of past days as per the predefined time period.2. Determining the Key Performance Indicator (KPI) value on a specified base date as the result of an operation involving the KPI values from the base date, subtracting 1 day from the KPI values from base date and several preceding days up to 'n' days, where “n” corresponds to number of past days as per the predefined time period.

[0112] At step 9, the computation layer module

[0306] the calculated KPI data is sent to the IPM module

[0304] ,

[0113] In cases where the pre-defined time period in the data request is within the limit of the retention time period of the database in the IPM module

[0304] , the IPM module

[0304] may not forward the request to the CL module

[0306] , rather the IPM module

[0304] itself fetches the counter data from the distributed data lake

[0510] ,

[0114] At step 10, the IPM module

[0304] may fetch the required data from distributed data lake

[0510] in cases where the predetermined time period is within the threshold limit.

[0115] At step 11, the distributed data lake

[0510] sends back the required data back to the IPM module

[0304] ,

[0116] At step 12, the IPM module

[0304] performs the delta calculation for the received data for KPI calculation.

[0117] Next, at stepl3, the delta computed data is sent to the Load balancer

[0504] , Where the CL module

[0306] is used for calculating delta, a notification is also sent along with the computed data.

[0118] At step 14, the computed data is further forwarded to the user interface module

[0302] by the load balancer

[0504] ,

[0119] Next, at step 15, the user interface module

[0302] may send the output to the user

[0602] , The output includes the analysed KPIs. Where a notification is being sent along with the computed data, the notification is sent to the user

[0602] first. If the user

[0602] clicks on the notification, at 16, the user

[0602] send a request to the get the result to the UI server

[0302] ,

[0120] At step 17, the UI server

[0302] sends the request further to the load balancer

[0504] ,

[0121] At step 18, the load balancer

[0504] forwards the request to the IPM module

[0304] , where the IPM module

[0304] calculates the data.

[0122] Further, at step 19, sends the computed delta KPI data to the load balancer

[0504] ,

[0123] At step 20, the computed delta KPI data is forwarded to further to the UI server

[0302] ,

[0124] Lastly, at step 21, the UI server

[0302] shows the computed, analysed data to the user

[0602] ,

[0125] The present disclosure further discloses relates to a user equipment

[0308] for performing real-time analysis of KPIs to monitor performance of a network. The user equipment (UE)

[0308] comprising a processor. The processor is configured to render an interface on a display device, wherein the interface enables a user to select one or more KPIs for performing real-time analysis of Key Performance Indicators (KPIs) to monitor performance of a network in a pre-defined time period. The processor is further configured to receive a user input comprising a request for execution of the selected one or more KPIs for the pre-defined time period mentioned in the request. Furthermore, the processor is configured to analyse, via a UI server

[0302] , the one or more selected KPIs in case the pre-defined time period exceeds database retention period for past data. The processor is configured to receive a notification on a notification panel in the interface upon successful execution and analysis of the selected one or more KPIs. Further, the processor is configured to render the analysis of the one or more selected KPIs on the interface.

[0126] The present disclosure further discloses a non-transitory computer readable storage medium storing instructions for performing real-time analysis of Key Performance Indicators (KPIs) to monitor performance of a network, the instructions include executable code which, when executed by one or more units of a system, causes: a user interface (UI) server

[0302] of the system

[0300] to render an interface on a user device, wherein the interface enables a user to select one or more KPIs for the analysis in a pre-defined time period. The instructions include executable code which, when executed, causes an Integrated Performance Management (IPM) module

[0304] of the system

[0300] to receive a user input comprising a request for execution of the selected one or more KPIs for the pre-defined time period mentioned in the request. The instructions further include executable code which, when executed, causes a Computation Layer (CL) module

[0306] of the system

[0300] associated with the IPM to analyse the one or more selected KPIs in case the predefined time period exceeds database retention period for past data and the IPM module

[0304] of the system to transmit a notification on a notification panel in the interface upon successful execution and analysis of the selected one or more KPIs. The instructions further include executable code which, when executed causes the UI server

[0302] of the system to render the analysis of the one or more selected KPIs on the interface.

[0127] As is evident from the above, the present disclosure provides a technically advanced solution of providing delta monitoring analysis of KPI values in a network performance management system. The delta monitoring analysis procedure is applicable at the KPI level. Hence, one can select multiple KPIs in a dashboard to monitor and apply the understanding to the real-world scenarios. Also, getting the real-time output makes the delta monitoring analysis procedure unique and technically advanced over the existing systems. Further, the Delta monitoring analysis allows user to track or monitor KPI changes over two different time periods. Also, the Delta monitoring analysis enables efficient, quick, and robust monitoring of the changes in KPI behaviour on the UI powered by AI / ML engine (which is part of computation layer).

[0128] 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.

[0129] Further, in accordance with the present disclosure, it is to be acknowledged that the functionality described for the various 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.

Claims

We Claim:

1. A method [400] for performing real-time analysis of Key Performance Indicators (KPIs) to monitor performance of a network, the method comprising: rendering, by a user interface (UI) server [302], an interface on a user device, wherein the interface enables a user to select one or more KPIs for the analysis in a pre-defined time period; receiving, at an Integrated Performance Management (IPM) module [304], a user input comprising a request for execution of the selected one or more KPIs for the pre-defined time period mentioned in the request; analysing, by a Computation Layer (CL) module [306] associated with the IPM module [304], the one or more selected KPIs in case the pre-defined time period exceeds a database retention period for past data; transmitting, by the IPM module [304], a notification on a notification panel in the interface upon successful execution and analysis of the selected one or more KPIs; and rendering, by the UI server [302], the analysis of the one or more selected KPIs on the interface.

2. The method [400] as claimed in claim 1, wherein the user input further comprises selection of one of a first KPI analytics procedure and a second KPI analytics procedure for the analysis of the one or more selected KPIs.

3. The method [400] as claimed in claim 2, wherein upon selection of the first KPI analytics procedure, the method further comprises analysing, by the CL module [306], the one or more selected KPIs.

4. The method [400] as claimed in claim 2, wherein upon selection of the second KPI analytics procedure, the method further comprising analysing, by the CL module [306], the one or more selected KPIs.

5. A system [300] for performing real-time analysis of Key Performance Indicators (KPIs) to monitor performance of a network, the system [300] comprising: a user interface (UI) server [302], configured to render an interface on a user device, wherein the interface enables a user to select one or more KPIs for the analysis in a pre-defined time period;an Integrated Performance Management (IPM) module [304] connected to at least the UI server [302], the IPM module [304] configured to receive a user input comprising a request for execution of the selected one or more KPIs for the predefined time period mentioned in the request; a Computation Layer (CL) module [306] associated with the IPM module [304], the CL module [306] is configured to analyse the one or more selected KPIs in case the pre-defined time period exceeds database retention period for past data; the IPM module [304], configured to transmit a notification on a notification panel in the interface, upon successful execution and analysis of the selected one or more KPIs; and the UI server [302], configured to render the analysis of the one or more selected KPIs on the interface.

6. The system [300] as claimed in claim 5, wherein the user input further includes selection of one of a first KPI analytics procedure and a second KPI analytics procedure for the analysis of the one or more selected KPIs.

7. The system [300] as claimed in claim 6, wherein upon selection of the first KPI analytics procedure, the CL module [306] analyse the one or more selected KPIs.

8. The system [300] as claimed in claim 6, wherein upon selection of the second KPI analytics procedure, the CL module [306] is configured to analyse the one or more selected KPIs.

9. A user equipment (UE) [308] comprising: a processor configured to:- render an interface on a display device, wherein the interface enables a user to select one or more KPIs for performing real-time analysis of Key Performance Indicators (KPIs) to monitor performance of a network in a pre-defined time period;- receive a user input comprising a request for execution of the selected one or more KPIs for the pre-defined time period mentioned in the request;- analyse, via a UI server [302], the one or more selected KPIs in case the pre-defined time period exceeds database retention period for past data;- receive a notification on a notification panel in the interface upon successful execution and analysis of the selected one or more KPIs; and- render the analysis of the one or more selected KPIs on the interface.

10. A non-transitory computer-readable storage medium storing instructions for performing real-time analysis of Key Performance Indicators (KPIs) to monitor performance of a network, the storage medium comprising executable code which, when executed by one or more units of a system, causes: - a user interface (UI) server [302] of the system [300], to render an interface on a user device, wherein the interface enables a user to select one or more KPIs for the analysis in a pre-defined time period; an Integrated Performance Management (IPM) module [304] of the system [300], to receive a user input comprising a request for execution of the selected one or more KPIs for the pre-defined time period mentioned in the request; a Computation Layer (CL) module [306] of the system [300] associated with the IPM module [304], the CL module [306] to analyse the one or more selected KPIs in case the pre-defined time period exceeds database retention period for past data; the IPM module [304] to transmit a notification on a notification panel in the interface, upon successful execution and analysis of the selected one or more KPIs; and the UI server [302] to render the analysis of the one or more selected KPIs on the interface.

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