System and method for dynamic service and resource management

Abstract

The present disclosure relates to a system (108) and a method (500) for performing dynamic service and resource management in a network environment, comprising a receiving unit (208) configured to receive at least one request from a user (102), 5 a processing engine (210) configured to determine a type of the received at least one request that is indicative of a type of action that is to be performed on a data record corresponding to / associated with a network function in responding to the request, perform at least one action based on the determined type of request, generate a processed data on completion of the at least one action and a plurality of 10 database(s) (220) configured to store the processed data

Description

SYSTEM AND METHOD FOR DYNAMIC SERVICE AND RESOURCE MANAGEMENTRESERVATION OF RIGHTS

[0001] A portion of the disclosure of this patent document contains material, which is subject to intellectual property rights such as, but are not limited to, copyright, design, trademark, integrated circuit (IC) layout design, and / or trade dress protection, belonging to Jio Platforms Limited (JPL) or its affiliates (herein after referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner.TECHNICAL FIELD

[0002] The present disclosure relates to a field of communications network, and specifically to a system and a method for dynamic service and resource management with respect to adaptive troubleshooting operations management.BACKGROUND

[0003] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

[0004] In general, in a communications network, in order to manage troubleshooting operations and on-board a new vendor system, every time codelevel development and testing may need to be done because every new source system has its way of generating, handling, and storing data. Further, each vendor may have a different operating system, where the data is getting stored.

[0005] In general, network debugging involves overdependence on the deeptechnical knowhow of the support team who may have to dig into massive rolling logs and data records to gather information, correlate, and infer the root cause often taking hours. There is a constant need to upgrade the technical know-how of the support team through technical training on vendor products and dependency on the vendor to impart complete knowledge transfer. There is also a need for constant upgradation of network probes and hardware.

[0006] Further, root cause analysis is manual, i.e., root cause analysis is a laborious process and requires deep technical know-how and project coordination. The support team may need to be empowered through machine learning to find the concerns in the network swiftly to drive action.

[0007] However, relying solely on reactive monitoring, where issues are identified only after customers report them, creates a poor customer experience. Additionally, conventional systems and methods possess massive hardware requirements. A massive cluster of database resources to store and analyze data collected from different software and hardware probes is required. In fact, vendor products are very centric to use cases and inhibit on-the-fly changes or requirements, increasing the time to market and time to production.

[0008] There is, therefore, a need in the art to provide a system and a method that can overcome the deficiencies of the prior arts.OBJECTS OF THE PRESENT DISCLOSURE

[0009] It is an object of the present disclosure to provide a system and a method for dynamic service and resource management with respect to adaptive troubleshooting, operations, and management.

[0010] It is an object of the present disclosure to provide the system and the method for real-time resource management in a network, enabling dynamic service management and ensuring that network resources are allocated efficiently, and services are adapted to meet current demands and conditions.

[0011] It is an object of the present disclosure to save time and resources.SUMMARY

[0012] The present disclosure discloses a system for performing dynamic service and resource management in a network environment. The system comprises a receiving unit configured to receive at least one request from a user, a processing engine configured to determine a type of the received at least one request that is indicative of a type of action that is to be performed on a data record corresponding to / associated with a network function in responding to the request, perform at least one action based on the determined type of request , generate a processed data on completion of the at least one action and a plurality of database(s) configured to store the processed data

[0013] In one embodiment, the processing engine comprises an ingestion layer engine configured to ingest at least one type of the data received from the receiving unit and generate an ingested data, a normalization layer engine configured for normalizing the ingested data, a message broker platform configured to receive the normalized data and manage real-time data streams catering to a plurality of users and a scheduling layer engine configured to enable the plurality of users to execute a plurality of tasks based on user-configured intervals corresponding to the real-time data streams managed by the message broker platform.

[0014] In one embodiment, the network function includes a Virtual Network Function (VNF), a Physical Network Function (PNF), or a combination thereof.

[0015] In one embodiment, the at least one action is selected from search, view, analyze, generate reports, monitor specific error codes, service allocation, and resource management.

[0016] In one embodiment, the system is further configured to employ a configuration management, an alarm management, and a counter management.

[0017] In one embodiment, a microservice-based architecture is configured to employ machine learning as a Service (MLaaS) to enable a plurality of operations within a distributed data lake.

[0018] In one embodiment, the normalization layer engine is configured to provide the normalized data to an analysis engine, a correlation engine, a service quality manager, and a streaming engine.

[0019] In one embodiment, the data generated by the analysis engine, the correlation engine, the service quality manager, and the streaming engine is a geographic location-based data to be presented over a map on a Graphic User Interface (GUI) by a mapping layer engine.

[0020] In one embodiment, a forecasting engine is configured to generate forecasts depicting future trends and outcomes by analyzing the processed data.

[0021] In one embodiment, the processing engine is configured as a parallel computing framework that provides the execution of computing tasks in parallel.

[0022] In accordance with one embodiment of the present disclosure, a method for performing dynamic service and resource management in a network is disclosed. The method comprises of steps: receiving, by a receiving unit, at least one request from a user, wherein the request is for a data records network function, determining by a processing engine, the type of request received, performing, by the processing engine, at least one action based on the determined type of request and storing, by a plurality of databases, the processed data.

[0023] In accordance with one embodiment of the present disclosure, a user equipment that is communicatively coupled with a network is disclosed. The coupling comprises of receiving, by a receiving unit, at least one request from a user, determining by a processing engine, a type of the at least one received requestthat is indicative of a type of action that is to be performed on a data record corresponding to / associated with a network function in responding to the request, performing, by the processing engine, at least one action based on the determined type of request, generating, by the processing engine, a processed data on completion of the at least one action, and storing, in a plurality of database(s), the processed data.

[0024] In accordance with one embodiment of the present disclosure, computer program product comprising a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to receive, by a receiving unit, at least one request from a user, wherein the request is for a data records network function, determining, by a processing engine, the type of request received, performing, by the processing engine, at least one action based on the determined type of request and storing, by a plurality of databases, the processed data.BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In the figures, similar components and / or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label with a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.

[0026] The diagrams are for illustration only, which thus is not a limitation of the present disclosure, and wherein:

[0027] FIG. 1 illustrates an exemplary network architecture in which or with which embodiments of the present disclosure may be implemented.

[0028] FIG. 2 illustrates a system for performing dynamic service and resource management in a network, in which or with which embodiments of the present disclosure may be implemented.

[0029] FIG. 3 illustrates an exemplary connection-level diagramrepresenting the interconnections of various system components, in accordance with an embodiment of the present disclosure.

[0030] FIG. 4 (a, b) illustrates an exemplary detailed system architecture, in accordance with an embodiment of the present disclosure.

[0031] FIG. 5 illustrates a flow chart depicting a method for performing dynamic service and resource management in a network, in accordance with an embodiment of the present disclosure.

[0032] FIG. 6 illustrates an exemplary computer system in which or with which embodiments of the present disclosure may be implemented.

[0033] The foregoing shall be more apparent from the following more detailed description of the disclosure.LIST OF REFERENCE NUMERALS100 - Network Architecture1 102-1, 102-2...102-N - Users104-1, 104-2. . . 104-N - User Equipments (UEs)106 - Network108 - System202 - Processor(s)204 - Memory206 - Interface(s)208 - Receiving unit210 - Processing engine220 - Database313 - Element Management System (EMS)314 - Graphic User Interface (GUI)315 - Microservice registry manager316 - Processor318 - Controller430 - Operations And Management Engine432 - Ingestion Layer Engine434 - Normalization layer436 - 5G Probe438 - Computation layer440 - Mapping Layer Engine444 - Correlation Engine446 - Caching Layer Engine448 - Load Balancer450 - Integrated Performance Management Engine452 - Analysis Engine454 - API Gateway456 - Streaming Engine458 - 5G Security Operation Centre460 - Reporting Engine462 - Distributed Data Lake466 - Anomaly Detection Module Engine468 - Graph Layer Engine470 - Forecasting Engine474 - Parallel Computing Framework476 - Distributed File System478 - Scheduling Layer Engine480 - Service Quality Manager610 - External Storage Device620 - Bus630 - Main Memory640 - Read Only Memory650 - Mass Storage Device660 - Communication Port670 - ProcessorDETAILED DESCRIPTION OF THE INVENTION

[0034] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address any of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein. Example embodiments of the present disclosure are described below, as illustrated in various drawings in which like reference numerals refer to the same parts throughout the different drawings.

[0035] 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.

[0036] 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, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

[0037] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

[0038] The word “exemplary” and / or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed 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 like the term “comprising” as an open transition word without precluding any additional or other elements.

[0039] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0040] The terminology used herein is to describe particular embodiments only and is not intended to be limiting the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term “and / or” includes any combinations of one or more of the associated listed items. It should be noted that the terms “mobile device”, “user equipment”, “user device”, “communication device”, “device” and similar terms are used interchangeably for the purpose of describing the invention. These terms are not intended to limit the scope of the invention or imply any specific functionality or limitations on the described embodiments. The use of these terms is solely for convenience and clarity of description. The invention is not limited to any particular type of device or equipment, and it should be understood that other equivalent terms or variations thereof may be used interchangeably without departing from the scope of the invention as defined herein.

[0041] As used herein, an “electronic device”, or “portable electronic device”, or “user device” or “communication device” or “user equipment” or “device” refers to any electrical, electronic, electromechanical, and computing device. The user device is capable of receiving and / or transmitting one or parameters, performing function / s, communicating with other user devices, and transmitting data to the other user devices. The user equipment may have a processor, a display, a memory, a battery, and an input-means such as a hard keypad and / or a soft keypad. The user equipment may be capable of operating on any radio access technology including but not limited to IP-enabled communication, Zig Bee, Bluetooth, Bluetooth Low Energy, Near Field Communication, Z-Wave, Wi-Fi, Wi-Fi direct, etc. For instance, the user equipment may include, but not limited to, a mobile phone, smartphone, Virtual reality (VR) devices, Augmented reality (AR)devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other device as may be obvious to a person skilled in the art for implementation of the features of the present disclosure.

[0042] Further, the user device may also comprise a “processor” or “processing unit” includes processing unit, wherein processor refers to any logic circuitry for processing instructions. The processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Array circuits, any other type of integrated circuits, etc. The processor may perform signal coding data processing, input / output processing, and / or any other functionality that enables the working of the system according to the present disclosure. More specifically, the processor is a hardware processor.

[0043] As portable electronic devices and wireless technologies continue to improve and grow in popularity, the advancing wireless technologies for data transfer are also expected to evolve and replace the older generations of technologies. In the field of wireless data communications, the dynamic advancement of various generations of cellular technology are also seen. The development, in this respect, has been incremental in the order of second generation (2G), third generation (3G), fourth generation (4G), and now fifth generation (5G), and more such generations are expected to continue in the forthcoming time.

[0044] While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment, as well as other embodiments of the disclosure, will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoingdescriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.

[0045] The disclosure presents a system and a method for performing dynamic service and resource management within a network, aimed at refining the efficiency and intelligence of network operations. The system includes a data ingestion layer designed to assimilate a broad spectrum of data types, such as alarms, counters, configurations, data records, infrastructural metrics, logs, and inventory data. The data ingestion layer collects the data and also validates and channels it toward a normalization layer for standardization.

[0046] Post-normalization, the data is systematically deposited across a plurality of databases that include a distributed data lake, caching, computation, and graph layers, effectively segregating the data for optimal utilization. A message broker is employed to adeptly oversee the flow of real-time data streams adeptly, ensuring seamless communication between data producers and the users within this high-end, new-generation distributed application platform.

[0047] A scheduling layer is incorporated to manage and execute a multitude of tasks at intervals predefined by the user, reflecting the adaptability of the system to various operational requirements. The scheduling layer extends to tasks like service calls, API calls to microservices, or executions of complex queries, all of which may be dynamically configured.

[0048] The system is built on a microservice-based architecture, specially tailored for leveraging Machine Learning as a Service (MLaaS) within a disaggregated, cloud-native data lake platform. This architecture facilitates smart operations by analyzing vast data streams to generate insights, reports, alerts, and notifications. Furthermore, the system is equipped with various engines including an analysis engine, a correlation engine, a service quality manager, and a streaming engine, all working in unison to process geographic location-based data which can be visually represented on a user interface map via a mapping layer.

[0049] Additionally, the system features a forecasting engine capable of generating precise and accurate predictions, underpinned by a parallel computing framework that allows for scalable, fault-tolerant parallel task execution. The framework supports the creation of task chains and their execution, ensuring that the system can handle various computing tasks efficiently.

[0050] The service quality manager component of the system is tasked with orchestrating workflows for services provided to customers and is capable of responding to service outages reported by customers.

[0051] The various embodiments throughout the disclosure will be explained in more detail with reference to FIG. 1- FIG. 5.

[0052] FIG. 1 illustrates an exemplary network architecture in which or with which a system (108) for performing dynamic service and resource management in a network is implemented, in accordance with embodiments of the present disclosure.

[0053] Referring to FIG. 1, the network architecture (100) includes one or more computing devices or user equipments (104-1, 104-2...104-N) associated with one or more users (102-1, 102-2...102-N) in an environment. A person of ordinary skill in the art will understand that one or more users (102-1, 102-2. . . 102- N) may be individually referred to as the user (102) and collectively referred to as the users (102). Similarly, a person of ordinary skill in the art will understand that one or more user equipments (104-1, 104-2. . .104-N) may be individually referred to as the user equipment (104) and collectively referred to as the user equipments (104). A person of ordinary skill in the art will appreciate that the terms “computing device(s)” and “user equipment” may be used interchangeably throughout the disclosure. Although three user equipments (104) are depicted in FIG. 1, however any number of the user equipments (104) may be included without departing from the scope of the ongoing description.

[0054] In an embodiment, the user equipment (104) includes smart devices operating in a smart environment, for example, an Internet of Things (loT) system. In such an embodiment, the user equipment (104) may include, but is not limited to, smart phones, smart watches, smart sensors (e.g., mechanical, thermal, electrical, magnetic, etc.), networked appliances, networked peripheral devices, networked lighting system, communication devices, networked vehicle accessories, networked vehicular devices, smart accessories, tablets, smart television (TV), computers, smart security system, smart home system, other devices for monitoring or interacting with or for the users (102) and / or entities, or any combination thereof. A person of ordinary skill in the art will appreciate that the user equipment (104) may include, but is not limited to, intelligent, multi-sensing, network-connected devices, that can integrate seamlessly with each other and / or with a central server or a cloud-computing system or any other device that is network-connected.

[0055] In an embodiment, the user equipment (104) includes, but is not limited to, a handheld wireless communication device (e.g., a mobile phone, a smart phone, a phablet device, and so on), a wearable computer device(e.g., a headmounted display computer device, a head-mounted camera device, a wristwatch computer device, and so on), a Global positioning system (GPS) device, a laptop computer, a tablet computer, or another type of portable computer, a media playing device, a portable gaming system, and / or any other type of computer device with wireless communication capabilities, and the like. In an embodiment, the user equipment (104) includes, but is not limited to, any electrical, electronic, electromechanical, or an equipment, or a combination of one or more of the above devices such as virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other computing device, wherein the user equipment (104) may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as a camera, an audio aid, a microphone, a keyboard, and input devices for receiving input from the user (102), or the entity (110) such as touch pad, touch-enabled screen, electronic pen, and the like. Aperson of ordinary skill in the art will appreciate that the user equipment (104) may not be restricted to the mentioned devices and various other devices may be used.

[0056] Referring to FIG. 1, the user equipment (104) communicates with a system (108), for example, through a network (106). In an embodiment, the network (106) may include at least one of a Fifth Generation (5G) network, 6G network, or the like. The network (106) may enable the user equipment (104) to communicate with other devices in the network architecture (100) and / or with the system (108). The network (106) may include a wireless card or some other transceiver connection to facilitate this communication. In another embodiment, the network (106) is implemented as, or include any of a variety of different communication technologies such as a wide area network (WAN), a local area network (LAN), a wireless network, a mobile network, a Virtual Private Network (VPN), the Internet, the Public Switched Telephone Network (PSTN), or the like.

[0057] Although FIG. 1 shows exemplary components of the network architecture (100), in other embodiments, the network architecture (100) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1. Additionally, or alternatively, one or more components of the network architecture (100) may perform functions described as being performed by one or more other components of the network architecture (100).

[0058] FIG. 2 illustrates an exemplary block diagram of the system (108) for performing dynamic service and resource management in a network. The system (108) may include a receiving unit (208), one or more processors (202), a memory (204), communicably coupled to the one or more processors (202). The one or more processor(s) (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, edge or fog microcontrollers, digital signal processors, central processing units, logic circuitries, and / or any devices that process data based on operational instructions. Among other capabilities, one or more processor(s) (202) may be configured to fetch and execute computer-readableinstructions stored in the memory (204) of the system (108). The memory (204) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory (204) may include any non-transitory storage device including, for example, volatile memory such as Random- Access Memory (RAM), or non-volatile memory such as Erasable Programmable Read-Only Memory (EPROM), flash memory, and the like.

[0059] In some embodiments, the system (108) may include an interface(s) (206). The interface(s) (208) may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I / O devices, storage devices, and the like. The interface(s) (206) may facilitate communication of the system (108). The interface(s) (206) may also provide a communication pathway for one or more components of the system (108). Examples of such components include, but are not limited to, processing unit / engine(s) (210) and a database (220).

[0060] The processing unit / engine(s) (210) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (210). In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (210) may be processor-executable instructions stored on a non-transitory machine -readable storage medium and the hardware for the processing engine(s) (210) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine -readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (210). In such examples, the system (108) may include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine -readable storage medium may be separate butaccessible to the system (108) and the processing resource. In other examples, the processing engine(s) (210) may be implemented by an electronic circuitry.

[0061] The processing engine(s) (210) is configured to determine a type of the received at least one request that is indicative of a type of action that is to be performed on a data record corresponding to / associated with a network function in responding to the request. In one embodiment, the network function includes a Virtual Network Function (VNF), a Physical Network Function (PNF), or a combination thereof.

[0062] The processing engine(s) (210) is configured to perform at least one action based on the determined type of request. In one embodiment, the at least one action is selected from search, view, analyze, generate reports, monitor specific error codes, service allocation, and resource management. Upon completion of the selected action(s), the processing engine(s) (210) generates processed data from the data record associated with the network function. For instance, if the user (102) initiates a request to search for network traffic patterns, the processing engine(s) (210) may analyze data from the user equipment (UE) (104) to identify peak usage times or potential bottlenecks. These peak usage times or potential bottlenecks are examples of the processed data. The processed data is then stored in one or more databases (220) for future reference and use.

[0063] In one embodiment, the processing engine comprises an ingestion layer engine, and a normalization layer engine. The ingestion layer engine is configured to ingest at least one type of the data received from the receiving unit and generate an ingested data. The normalization layer engine is configured for normalizing the ingested data. In one embodiment, the normalization layer engine is configured to provide the normalized data to an analysis engine, a correlation engine, a service quality manager, and a streaming engine.

[0064] Further, a message broker platform is configured to receive the normalized data and manage real-time data streams catering to a plurality of users. The processing engine comprises a scheduling layer engine that is configured toenable the plurality of users to execute a plurality of tasks based on user-configured intervals corresponding to the real-time data streams managed by the message broker platform.

[0065] In one embodiment, the system is further configured to employ a configuration management, an alarm management, and a counter management. In one embodiment, the system is further configured to employ a microservice-based architecture is configured to employ machine learning as a Service (MLaaS) to enable a plurality of operations within a distributed data lake.

[0066] In one embodiment, the data generated by the analysis engine, the correlation engine, the service quality manager, and the streaming engine is a geographic location-based data to be presented over a map on a Graphic User Interface (GUI) by a mapping layer engine.

[0067] In one embodiment, the system includes a forecasting engine that is configured to generate forecasts depicting future trends and outcomes by analysing the processed data.

[0068] In one embodiment, the processing engine is configured as a parallel computing framework that provides the execution of computing tasks.

[0069] In an aspect, the processing unit(s) (210) may be configured to send the processed data to the database (220).

[0070] In an embodiment, the database (220) may include any computer- readable medium known in the art including, for example, volatile memory, such as Static Random Access Memory (SRAM) and Dynamic Random Access Memory (DRAM) and / or non-volatile memory, such as Read Only Memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.

[0071] Although FIG. 2 shows an exemplary block diagram (200) of the system (108), in other embodiments, the system (108) may include fewercomponents, different components, differently arranged components, or additional functional components than depicted in FIG. 2. Additionally, or alternatively, one or more components of the (108) may perform functions described as being performed by one or more other components of the system (108).

[0072] FIG. 3 illustrates a connection-level diagram representing interconnections of various components of the system (108) in accordance with an embodiment of the present disclosure. The system (108) relates to performing dynamic service and resource management in the network environment and encompasses various functionalities, ranging from proactive, reactive, and adaptive monitoring to internode correlation and log analysis.

[0073] The system (108) mainly includes, but may not be limited to, a Graphical User Interface (GUI) (314) communicatively coupled to an edge load balancer microservice (312), an Element Management System (EMS) (313) communicatively coupled to a microservice registry manager (315), a processor (316), and a controller (318). The microservice registry manager (315), the edge load balancer microservice (312), the controller (118) and the processor (116) may be communicatively coupled to each other.

[0074] The system (108) may integrate various components that work together to provide a comprehensive solution for network management. The GUI (314) may serve as an access point of the user (102) to the system (108), offering an interactive and visual interface to monitor and control the various elements of the network (106). The user (102) may observe system performance, receive notifications, and interact with the system (108) for tasks such as troubleshooting, configuration, and report generation using the GUI (314).

[0075] The EMS (313) may be configured to manage network elements at the granular level. The EMS (313) communicates with the microservice registry manager (315), which likely maintains a registry of various microservices available in the system (108). The EMS (313) may be responsible for various tasks, such asmonitoring the status of network elements, executing configuration changes, and handling alarms and performance data.

[0076] The processor (316) may be configured to execute the instructions and process the data necessary for the operation of the entire system. The processor (316) may perform computations, run analytics, and ensure that tasks are carried out efficiently.

[0077] The controller (318) may be configured to orchestrate the interactions between different parts of the system (108). The controller (318) may handle tasks like routing data, managing workflows, and ensuring that the system (108) responds correctly to inputs given by the user (102).

[0078] The edge load balancer microservice (312) may be configured to distribute network traffic and requests efficiently across various servers or services.

[0079] Each component of the system (108), the microservice registry manager (315), the edge load balancer microservice (312), the controller (318), and the processor (316), are communicatively coupled, indicating a high degree of interoperability and information exchange. This architecture allows for a seamless operation where changes in one part of the system (108) are immediately known and responded to by others, maintaining the integrity and performance of the system (108).

[0080] In one exemplary embodiment of the system (108), the EMS (313) may provide the necessary functions to manage Network Elements (NEs) on the Network Element-Management Layer (NEL) of the Telecommunications Management Network (TMN) model.

[0081] Further, the system (108) may include functionalities for proactive, reactive, and adaptive monitoring, configuration management, performance management, and various other operational tasks. The EMS (313) may be configured for the management of specific types of network elements, like theVirtual Network Functions (VNFs) or Physical Network Functions (PNFs) mentioned.

[0082] In an embodiment, the EMS (313) may be configured to interact with various components such as the data ingestion layer, normalization layer, processing units, and service quality manager, among others, to manage the network elements effectively. The EMS (313) may be configured to collect and analyze performance data, fault management, configuration, accounting, performance, and security management, and respond to network events and alarms, as outlined by the correlation engine and anomaly detection functionalities within the system (108).

[0083] Further, the system (108) may be a disaggregated and cloud-native data lake platform tailored for operators to enable smarter operations through Machine Learning (ML) as a Service (MLaaS). In particular, the system (108) may encompass a range of functionalities ranging from pro-active, reactive, and adaptive monitoring to inter-node correlation and log analysis.

[0084] Further, the processor (316), may be capable of pulling in data records of different virtualized network elements running as virtual machines in the cloud. These data records may be of different data formats which may be sanitized and normalized before being used for analysis. Dynamic workflows and tasks provisioned on the fly may adapt themselves to the data sources and churn out proactive and reactive notifications for different user-defined scenarios. Further, proactive notifications and alerts may be generated on the basis of pre-configured policies. Furthermore, the system may provide numerous ways for the user (102) to search, view, analyze, generate reports, monitor specific error codes, and tons of other information that may be coming in the data records of the VNFs or PNFs.

[0085] FIG. 4(a) and 4(b) illustrate an exemplary detailed system architecture (400) in accordance with an embodiment of the present disclosure.

[0086] The system (108) may include an ingestion layer engine (432), a normalization layer engine (434), and one or more sub-systems.

[0087] The ingestion layer engine (432) may be configured to define an environment that may be capable of consuming various types of incoming data, such as alarms, counter, configuration, data records, infra-metric data, logs, and inventory data. The ingestion layer engine (432) may gather data and forward it to the data processing systems. The ingestion layer engine (432) may process incoming data, validate data, and route it to the normalization layer engine (434), streaming engine, streaming analytics engine, and a message broker platform (464) based on the requirements for further analytics.

[0088] The normalization layer engine (434) may normalize, enrich, and store data received from the ingestion layer engine (432) in a database (220 ref. Fig. 2). The normalization layer engine (434) may insert normalized data into various databases such as, but not limited to, a distributed data lake (DDL) (462), a caching layer engine (446), and graph layer module (468). Further, the normalization layer engine (434) may produce data for the message broker platform (464). The normalization layer engine (434) may also be responsible for providing the normalized data to another sub-system. These sub-systems may include, but not be limited to, an analysis engine (452), a correlation engine (444), a service quality management (480), and a streaming engine (456).

[0089] Further, a 5G probe(s) (436) plays a crucial role. The 5G probe(s) (436) comprises of 5G machine learning (ML) probe, a 5G real-time conductors and 5G fulcrums. The 5G probe(s) (436) is designed as a software-based solution, meaning the probing logic (network taps and probes) may be embedded within the NF business logic, eliminating the need for physical probes. The probing solution requires only summarized data records from NFs to generate analytics. The vProbe solution incorporates a probing agent that collects probing data, specifically Streaming Data Records (SDR), from a 4G / 5G combo-core network nodes. These records are generated by NFs in case of any network failure conditions. The recordsare streamed in real-time into the 5G probe(s) (436) aggregation layer and then reach the analysis engine (452). The analysis engine (452) normalizes and enriches the data, creating reports using reporting tools, which further aids in overall network troubleshooting and root cause analysis.

[0002] In an embodiment, a computation layer (438) acts as a security checkpoint for requests originating from external systems. The computation layer (438) receives and manages requests submitted by external systems. The computation layer (438) strictly controls access to internal resources or data by verifying that each request is authorized. This ensures only authorized external systems can interact with the system (108).

[0003] In an embodiment, the message broker platform (464) is a publish- subscribe messaging system that manages and maintains the real-time stream of data from different applications. The message broker platform (464) may act as a central hub for message exchange between different components within the system (108). The message broker platform (464) may enable communication between producers and the users (102) using message-based topics. The message broker platform (464) is designed for high-end new-generation distributed applications and permits a large number of permanent or ad-hoc users. Further, the message broker platform (464) may rely on the file system for storage and caching purposes. Thus, the message broker platform (464) is fast, prevents data loss, and is fault-tolerant.

[0004] Further, the graph layer module (468) may include a relationship modeler, which may be capable of modelling the alarm, counter, configuration, data records, infra-metric data, 5G probe data, and inventory data as captured by the ingestion layer engine (432). The graph layer module (468) may build the relationship among the various type of data provision. For example, the modeler may model the alarm and the counter data or Vprobe and alarm data and their relationship with each other. Further, the modeler may process the steps provisioned in the model and provide the outcome to a requested system(s), where the system(s) may be a parallel computing system, a workflow engine, a queryengine, a correlation system, a 5G performance management engine, and a 5G Key Performance Indicator (KPI) engine.

[0005] In an embodiment, the scheduling layer module (478) may be configured to a task(s) at a pre-defined intervals of time configured as per the choice of the user (102). A task may be an activity, that performs a service call, an Application Programming Interface (API) call to another microservice, an execution of an elastic search query and storing its output in the DDL (462) or a Distributed File System (476) (DFS) or sending it to another micro-service. The scheduling layer module (478) may also facilitate graph traversals via a mapping layer engine (440) to execute tasks.

[0006] Further, the objective for designing the analysis engine (452) may be to define the environment where the workflow, i.e. a set of tasks for any usecases may be configured and executed to debug or for a better understanding of the call flow. A user (102) may also query the data coming from different sub-systems or external gateway for a better overview of the data or to identify the actual issue present in the data. The user (102) may also configure the set of policies through which the user (102) may identify the anomaly present on the data and receive a notification once a policy is breached or some abnormal behaviour occurs.

[0007] In an embodiment, a parallel computing framework (474) may provide a simple but sophisticated interface to execute computing tasks in parallel. The user (102) may either provide the DFS (476) locations or the DDL (462) indices for input data. The parallel computing framework (474) may support creating chains of tasks by connecting to a Sub-parallel computing framework System (SCM). Each of the tasks in a workflow may be executed sequentially whereas multiple chains may be executed in parallel. The parallel computing framework (474) may support allocating specific lists of hosts for different computing tasks.

[0008] Further, the distributed file system (476) may be a file system that allows the user (102) to have access to data and supports different operations. Each data file may be partitioned into several parts called chunks.

[0009] Furthermore, load balancing may refer to efficiently distributing incoming network traffic across a group of backend servers, also known as a server farm or server pool. A load balancer (448) may route all the traffic on the servers and send the client requests across the microservices capable of fulfilling those requests. In some embodiments, the load balancer (448) may route the request based on round robin scheduling, header-based request dispatch, and context-based request dispatch, but not limited to the like. In header-based request dispatch, the load balancer (448) may handle event and event acknowledgment and forward the request / response to the specific microservice which has requested for the event.

[0010] In an aspect, the objective of the streaming engine (456) may be the creation of fast-paced streaming pipelining to the GUI (314). The streaming engine (456) may receive the data from connected sub-systems and stream the received data to GUI (314) in support of the DDL (462), the message broker platform (464), and the caching layer engine (446). The stream analytics engine or the streaming engine (456) may receive data from the sub-systems and perform the required computation on data in real-time, followed by sending it to the GUI (314).

[0011] Further, the objective for designing an integrated performance management engine (450) that may be configured to provide the platform that may use all the requirements related to performance counters. The requirement may be related to visualizing the performance counters of a particular node, creating and analyzing the KPIs, creating counter / KPI reports consisting of single or multiple nodes with multiple levels of aggregation, and the like. In some embodiments, the integrated performance management engine (450) may maintain the performance counters and KPIs of the network elements. The 5G performance management engine may gather and process performance counter data from different data sources, and based on the aggregation required, store the network performance data and the KPI engine responsible for managing all the KPI of all the network elements.

[0012] Furthermore, the objective for designing the reporting engine (460) may be configured to create the report view layout of API according to requirement of the user (102) and objective of a notification engine may be to send the report to the user (102) by email according to client requirement. In some embodiments, the reporting engine (460) may create the report according to a dashboard that the user (102) may create from the GUI (314) according to the user (102) requirement(s). The reporting engine (460) may process the data from different interfaces and create report in, for example, excel format.

[0013] In an aspect, an anomaly detection module (466) may be configured to notify the analysis engine (452) to create a policy for a selected algorithm or model to determine anomalies in the KPIs. Once this provision is set, the user (102) start receiving machine learning reports on a scheduled basis. Additionally, the anomaly detection module (466) utilizes data normalized by the normalization layer engine (434) received from the DDL (462) for model creation and prediction purposes. In some embodiments, the anomaly detection module (466) supports model chaining, allowing the user (102) to link similar models to identify anomalies within the same data set. The models with the same time unit can be chained for comparative analysis of anomaly detection tasks. Moreover, the anomaly detection module (466) includes a model comparison feature, enabling the user (102) to compare outputs from different algorithms. This helps the user (102) to select the best-performing algorithm based on accuracy and performance. Furthermore, the anomaly detection module (466) may provide report management capabilities, allowing the users (102) to export data from selected models using various filters and granularity levels for further optimization. The anomaly detection module (466) also integrates a streaming engine, enabling users (102) to identify anomalies in real-time data streams. In some embodiments, the anomaly detection module (466) includes a statistics management function, which allows the user (102) to view model statistics categorized by algorithms. The anomaly detection module (466) may have a wide range of prediction algorithms and can automatically select the most suitable algorithm for the given data set. Additionally, the anomalydetection module (466) allows the user (102) to set thresholds for the predicted values, providing further customization and control over the anomaly detection process.

[0014] In an aspect, a forecasting engine (470) may provide a simple and sophisticated interface to generate forecasts with high accuracy and precision. The architecture may be scalable and fault-tolerant. The user (102) may either upload data directly in a CSV format or upload it on a server and provide its path to the service. The forecasting engine (470) may support multiple data visualization techniques. The user (102) may analyse and clean the dataset. If the dataset has an inherent hierarchy, the user (102) may divide the dataset and create sub-models. The user (102) may select their preferred algorithm that suits their needs and tweak its parameters. In some embodiments, the user (102) may also add external variables to improve the accuracy of the model. The execution of the model may be done in the background, and the user (102) may be notified when it is ready. The user (102) may view the predictions and export them for offline use.

[0015] Further, the integrated performance management engine (450), the analysis engine (452), and the correlation engine (444) and their sub-systems may have geographic location-based data that need to be presented over a map on the GUI (314). The mapping layer engine (440) may provide map data to the integrated performance engine, analysis engine (452), and the correlation engine (444) subsystems to show respective data over the map on the GUI (314). In some embodiments, the integrated performance engine may use map data to show performance counter, KPI information, etc., on the map. The analysis engine (452) may use the map data to show alarms specific to a location on the map. Further, the correlation engine (444) may use the map data to show location-wise alarms, counters, and metrics on the map. Furthermore, the map data may be stored in the distributed file system (476) to be used by the mapping layer engine (440).

[0016] In an aspect, the script engine (472) may provide a simple interface to manage and execute python scripts. The script engine (472) may be scalable andfault-tolerant. The script engine (472) instances may fetch the input in CSV format from a common location on the distributed file system (476). The script engine (472) may support multiple data pre-processing and cleaning techniques. The user (102) may analyse and manipulate the dataset.

[0017] In some embodiments, an API Gateway (454) may take all API calls from clients, and route them to the appropriate microservice with request routing, composition, and protocol translation. Typically, the API Gateway (454) may handle a request by invoking multiple microservices and aggregating the results to determine the best path. The API Gateway (454) may translate between web protocols and web-unfriendly protocols that may be used internally.

[0018] Further, for most microservices-based applications, the API Gateway (454) may be implemented because the API Gateway (454) acts as a single-entry point into the system (108). The API Gateway (454) may be responsible for request routing, composition, and protocol translation, and may streamline the system. The API Gateway (454) may handle some requests by simply routing them to the appropriate backend service, and handle others by invoking multiple backend services and aggregating the results. If there are failures in the backend services, the API Gateway (454) may mask them by returning cached or default data.

[0019] In an aspect, the DDL (462) may make organizational data from different sources, accessible to a variety of end users like business analysts, data engineers, data scientists, product managers, executives, etc., in order to enable these personas to leverage insights in a cost-effective manner, for improved business performance. It may be noted that the DDL (462) is important in the context of many kinds of applications. Whenever an application needs to store data persistently and access this data regularly, the DDL (462) is required. The DDL (462) is all about storing large amounts of data, which may be structured, semistructured, or unstructured, e.g. web server logs, NoSQL data, sensors, Internet ofThings (loT) data, and third-party data. The DDL (462) can either store the data in the same format as its source systems or transform it before storing.

[0020] In an aspect, the objective for designing the service quality manager (480) may be to define the environment where workflows may be configured for services provided to the user (102) such as Voice, Messaging, Wi-Fi, FTTx, etc. and also execute these workflows whenever the user (102) complaints for service outage. In some embodiments, the service quality manager (480) may maintain the status of customer services. The monitoring tool in the service quality manager (480) may gather and process data from different data sources and based on the fine-grained analysis of the network data collected and KPIs, the service quality manager (480) may provide the service status per the user (102) for a desired service.

[0021] Further, an operation and management engine (430) may manage all Fault, Configuration, Alarm, Performance, and Security (FCAPS) of all the system nodes and service engines. Through FCAPS, microservices may monitor its faults, configurations, performance counters, etc. Alarms may be the events that arise on certain conditions, for example, when database server initialization fails, etc., the operation and management engine (430) may signify the specific application parameters used by the system (108). Performance counters may be the values that get incremented when a particular event occurs in the system (108), representing the success / failure of that event.

[0022] In an aspect, a correlation engine (444) may be used in systems management tools to aggregate, normalize, and analyse event log data, using predictive analytics and fuzzy logic to alert the systems administrator when there is a problem. The correlation engine (444) may perform correlation and crosscorrelation based on rules, policies, and machine learning such that rules may be pre-defined to detect patterns. The correlation engine (444) may be continuously enhanced and customized to the needs of the user (102). Policies may be used to verify if certain actions happen at the right time and place. Machine learning mayinclude the abilities of the correlation engine (444) to learn and differentiate between normal and abnormal states as well as to detect changes in the behaviour of applications, servers, and other areas of a network.

[0023] Although FIG. 4 shows an exemplary block diagram (400) of the system (108), in other embodiments, the system (108) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 4. Additionally, or alternatively, one or more components of the system (108) may perform functions described as being performed by one or more other components of the system (108).

[0024] FIG. 5 illustrates a method (500) for performing dynamic service and resource management in a network.

[0025] At step 502, the method (500) is configured to receive, by a receiving unit (208), at least one request from a user (102), wherein the request is for a data records network function. This request pertains to a network function that involves processing or accessing network data records.

[0026] At step 504, the method (500) is configured to determine, by a processing engine (210), a type of request received. The processing engine (210) analyzes the received request (502) to identify the specific type of network data function requested by the user. This involves tasks like searching for specific data, analyzing network performance metrics, or generating report.

[0027] At step 506, the method (500) is configured to perform at least one action based on the determined type of request. The at least one action may include retrieving the requested data from the database(s) (220), processing or transforming the retrieved data for user consumption (e.g., generating reports, filtering data based on specific criteria), or delivering the processed data to the interface (206).

[0028] At step 508, the method (500) is configured to generate a processed data on completion of the at least one action.

[0029] At step 510, the method (500) is configured to store, by a plurality of databases (220), the processed data resulting from the actions performed in step 506 is stored within the plurality of databases (220) within the system (108). This allows for future access and analysis of the data.

[0030] In an aspect, the method includes ingesting, by an ingestion layer engine (432), at least one type of the data record received from the network function and generating an ingested data. The method includes normalizing, by a normalization layer engine (434) the ingested data. The method includes receiving, by a message broker platform (464), the normalized data and managing real-time data streams catering to a plurality of users (102). The method includes executing, by scheduling layer engine (478), a plurality of tasks based on user-configured intervals corresponding to the real-time data streams managed by the message broker platform (464).

[0031] In an aspect, the network function includes a Virtual Network Function (VNF), a Physical Network Function (PNF), or a combination thereof.

[0032] In an aspect, the at least one action is selected from search, view, analyze, generate reports, monitor specific error codes, service allocation, and resource management.

[0033] In an aspect, the method includes employing a configuration management, an alarm management, and a counter management.

[0034] In an aspect, the method includes a microservice-based architecture configured to employ machine learning as a Service (MLaaS) to enable a plurality of operations within a distributed data lake (462).

[0035] In an aspect, the method includes providing, by the normalization layer engine (434), the normalized data to an analysis engine (452), a correlation engine (444), a service quality manager (480), and a streaming engine (456).

[0036] In an aspect, the method includes generating, by a forecasting engine (470), forecasts depicting future trends and outcomes by analysing the processed data.

[0037] In an exemplary aspect, the present disclosure discloses a user equipment communicatively coupled with the network (106). The coupling comprises of receiving, by a receiving unit, at least one request from a user, determining by a processing engine, a type of the at least one received request that is indicative of a type of action that is to be performed on a data record corresponding to / associated with a network function in responding to the request, performing, by the processing engine, at least one action based on the determined type of request, generating, by the processing engine, a processed data on completion of the at least one action, and storing, in a plurality of database(s), the processed data.

[0038] FIG. 6 is an illustration (600) of a non-limiting example of details of computing hardware used in the system (108), in accordance with an embodiment of the present disclosure. As shown in FIG. 2, the system (108) may include an external storage device (610), a bus (620), a main memory (630), a read-only memory (640), a mass storage device (650), a communication port (660), and a processor (670). A person skilled in the art will appreciate that the system (108) may include more than one processor (670) and communication ports (660). Processor (670) may include various modules associated with embodiments of the present disclosure.

[0039] In an embodiment, the communication port (660) is any of an RS- 232 port for use with a modem-based dialup connection, a 10 / 100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port (660) is chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the system (108) connects.

[0040] In an embodiment, the memory (630) is Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Readonly memory (640) is any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or Basic Input / Output System (BIOS) instructions for the processor (670).

[0041] In an embodiment, the mass storage (650) is any current or future mass storage solution, which is used to store information and / or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and / or Firewire interfaces), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g., an array of disks (e.g., SATA arrays).

[0042] In an embodiment, the bus (620) communicatively couples the processor(s) (270) with the other memory, storage, and communication blocks. The bus (620) is, e.g., a Peripheral Component Interconnect (PCI) / PCI Extended (PCI- X) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB) or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (670) to the system (108).

[0043] Optionally, operator and administrative interfaces, e.g., a display, keyboard, joystick, and a cursor control device, may also be coupled to the bus (620) to support direct operator interaction with the system (108). Other operators and administrative interfaces are provided through network connections connected through the communication port (660). The components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary illustration (600) limit the scope of the present disclosure.

[0044] The method and system of the present disclosure may be implemented in a number of ways. For example, the methods and systems of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

[0045] While the foregoing describes various embodiments of the present disclosure, other and further embodiments of the present disclosure may be devised without departing from the basic scope thereof. The scope of the present disclosure is determined by the claims that follow. The present disclosure is not limited to the described embodiments, versions, or examples, which are included to enable a person having ordinary skill in the art to make and use the present disclosure when combined with information and knowledge available to the person having ordinary skill in the art.

[0046] The present disclosure discloses a system and method for performing dynamic service and resource management in a network. This advancement addresses the limitations of existing static network management solutions by enabling real-time allocation of resources based on current demands. The disclosure involves a system with a processing engine that analyzes user requests and network data. This offers significant improvements in efficiency and adaptability for network operations. By implementing features like machine learning and parallel processing, the disclosed invention enhances network troubleshooting, resource allocation, and service delivery, resulting in improved network performance and reduced costs.ADVANTAGES OF THE PRESENT DISCLOSURE

[0047] The present disclosure leverages machine learning to find anomalous network patterns and creates reports and alerts based on the events.

[0048] The present disclosure helps in proactive issue root cause analysis and resolution before a network symptom affects operations.

[0049] The present disclosure helps in operational insights, data binding, as well as correlation without writing a single line of code.

[0050] The present disclosure provides auto-triggering of workflows and organizational assignment of tasks, bringing transparency and resolution.

[0051] The present disclosure helps in automating workflow steps through artificial intelligence and machine learning.

Claims

We Claim:

1. A system (108) for performing dynamic service and resource management in a network, the system (108) comprising: a receiving unit (208) configured to receive at least one request from a user (102); a processing engine (210) configured to: determine a type of the at least one received request that is indicative of a type of action that is to be performed on a data record corresponding to / associated with a network function in responding to the at least one request; perform at least one action based on the determined type of request; generate a processed data on completion of the at least one action; and a plurality of database(s) (220) configured to store the processed data.

2. The system (108) as claimed in claim 1, wherein the processing engine (210) comprises: an ingestion layer engine (432) configured to ingest at least one type of the data record received from the network function and generate an ingested data; a normalization layer engine (434) configured for normalizing the ingested data; a message broker platform (464) configured to receive the normalized data and manage real-time data streams catering to a plurality of users (102); and a scheduling layer engine (478) configured to enable the plurality of users (102) to execute a plurality of tasks based on user-configured intervals corresponding to the real-time data streams managed by the message broker platform (464).

3. The system (108) as claimed in claim 1, wherein the network function includes a Virtual Network Function (VNF), a Physical Network Function (PNF), or a combination thereof.

4. The system (108) as claimed in claim 1, wherein the at least one action is selected from search, view, analyze, generate reports, monitor specific error codes, service allocation, and resource management.

5. The system (108) as claimed in claim 1, is further configured to employ a configuration management, an alarm management, and a counter management.

6. The system (108) as claimed in claim 1, further comprises a microservicebased architecture configured to employ machine learning as a Service (MLaaS) to enable a plurality of operations within a distributed data lake (462).

7. The system (108) as claimed in claim 2, wherein the normalization layer engine (434) is configured to provide the normalized data to an analysis engine (452), a correlation engine (444), a service quality manager (480), and a streaming engine (456).

8. The system (108) as claimed in claim 7, wherein the data generated by the analysis engine (452), the correlation engine (444), the service quality manager (480), and the streaming engine (456) is a geographic locationbased data to be presented over a map on a Graphic User Interface (GUI) (314) by a mapping layer engine (440).

9. The system (108) as claimed in claim 1, includes a forecasting engine (470) configured to generate forecasts depicting future trends and outcomes by analysing the processed data.

10. The system (108) as claimed in claim 1, wherein the processing engine (210) is configured as a parallel computing framework (474) that provides parallel execution of computing tasks.

11. A method (500) for performing dynamic service and resource management in a network, the method (500) comprising: receiving (502), by a receiving unit (208), at least one request from a user (102); determining (504), by a processing engine (210), a type of the at least one received request that is indicative of a type of action that is to be performed on a data record corresponding to / associated with a network function in responding to the at least one request; performing (506), by the processing engine (210), at least one action based on the determined type of request; generating (508), by the processing engine (210), a processed data on completion of the at least one action; and storing (510), in a plurality of database(s) (220), the processed data.

12. The method (500) as claimed in claim 11, further comprising steps of: ingesting, by an ingestion layer engine (432), at least one type of the data record received from the network function and generating an ingested data; normalizing, by a normalization layer engine (434) the ingested data;receiving, by a message broker platform (464), the normalized data and managing real-time data streams catering to a plurality of users (102); and executing, by scheduling layer engine (478), a plurality of tasks based on user-configured intervals corresponding to the realtime data streams managed by the message broker platform (464).

13. The method (500) as claimed in claim 11, wherein the network function includes a Virtual Network Function (VNF), a Physical Network Function (PNF), or a combination thereof.

14. The method (500) as claimed in claim 11, wherein the at least one action is selected from search, view, analyze, generate reports, monitor specific error codes, service allocation, and resource management.

15. The method (500) as claimed in claim 11, further comprising employing a configuration management, an alarm management, and a counter management.

16. The method (500) as claimed in claim 11, further comprising employing a microservice-based architecture configured to employ machine learning as a Service (MLaaS) to enable a plurality of operations within a distributed data lake (462).

17. The method (500) as claimed in claim 11, further comprising providing, by the normalization layer engine (434), the normalized data to an analysis engine (452), a correlation engine (444), a service quality manager (480), and a streaming engine (456).

18. The method (500) as claimed in claim 11, further comprising generating, by a forecasting engine (470), forecasts depicting future trends and outcomes by analysing the processed data.

19. The method (500) as claimed in claim 11, further comprising configuring the processing engine (210) as a parallel computing framework (474) that provides parallel execution of computing tasks.

20. A user equipment (UE) (104) communicatively coupled with a network (106), the coupling comprises steps of: receiving, by a receiving unit, at least one request from a user (102); determining, by a processing engine (210), a type of the at least one received request that is indicative of a type of action that is to be performed on a data record corresponding to / associated with a network function in responding to the at least one request; performing, by the processing engine (210), at least one action based on the determined type of request; and generating, by the processing engine (210), a processed data on completion of the at least one action; and storing, in a plurality of database(s) (220), the processed data.

21. A computer program product comprising a non- transitory computer- readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to: receive, by a receiving unit, at least one request from a user (102); determine, by a processing engine (210), a type of the at least one received request that is indicative of a type of action that is tobe performed on a data record corresponding to / associated with a network function in responding to the at least one request; perform, by the processing engine (210), at least one action based on the determined type of request; and generate, by the processing engine (210), a processed data on completion of the at least one action; and store, in a plurality of database(s) (220), the processed data.

Images