Method and system for dynamically assigning network counters

Abstract

The present disclosure relates to a method [300] and a system [200] for dynamically assigning network counters. The method [300] comprises: receiving [304], by a transceiver unit [202], a counter assign request associated with one or more user groups. The method [300] further comprises identifying [306], by a processing unit [204] at a Network performance management system, one or more network counters based on the counter assign request. Thereafter, the method [300] comprises dynamically assigning [308], by the processing unit [204] from the network performance management system, the one or more network counters to the one or more user groups associated with the counter assign request.

Description

METHOD AND SYSTEM FOR DYNAMICALLY ASSIGNING NETWORK COUNTERSFIELD OF INVENTION

[0001] Embodiments of the present disclosure relate to a method and a system for dynamic counter assignment i.e., for dynamically assigning network counters.BACKGROUND

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

[0003] Network performance management systems typically track network elements and data from network monitoring tools and then combine and process such data to determine key performance indicators (KPI) of the network.

[0004] In many organizations, dashboards are used to monitor and analyze the performance of systems, networks, or other operations. These dashboards rely on specific metrics, called counters, to generate insights and measure performance. However, in many cases, users are overwhelmed with too many counters that may not be relevant to their needs. This makes it difficult to focus on the most important data and can lead to less accurate or meaningful analysis.

[0005] Thus, there exists an imperative need in the art for dynamic counter assignment i.e., dynamically assigning network counters, which the present disclosure aims to address.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 relates to a method for dynamically assigning network counters. The method comprises receiving, by a transceiver unit, a counter assign request associated with one or more user groups. The method further comprises identifying, by a processing unit at a Network performance management system, one or more network counters based on the counter assign request. The method furthermore comprises dynamically assigning, by the processing unit from the network performance management system, the one or more network counters to the one or more user groups associated with the counter assign request.

[0008] In an exemplary aspect of the present disclosure, the method further comprises transmitting, by the transceiver unit from the network performance management system, a request completion status associated with the counter assign request based on dynamically assigning at least the one or more network counters to the one or more user groups.

[0009] In an exemplary aspect of the present disclosure, the one or more network counters are identified from a list of counters based on a network node and a category of the one or more user groups associated with the counter assign request.

[0010] In an exemplary aspect of the present disclosure, the method further comprises recommending, by the processing unit using a trained model, at least one counter based on a user profile data, a user preference data, and a historical usage data of the one or more user groups.

[0011] Another aspect of the present disclosure relates to a system for dynamically assigning network counters. The system comprises a transceiver unit, wherein the transceiver unit is configured to receive a counter assign request associated with one or more user groups. The system further comprises a processing unit connected to at least the transceiver unit, wherein the processing unit is configured to identify, at a Network performance management system, one or more network counters based on the counter assign request. The processing unit is further configured to dynamically assign, from the network performance management system, the one or more network counters to the one or more user groups associated with the counter assign request.

[0012] Yet another aspect of the present disclosure relates to a non-transitory computer readable storage medium storing instructions for dynamically assigning network counters, the instructions include executable code which, when executed by one or more units of a system, causes a transceiver unit of the system to receive a counter assign request associated with one or more user groups. Further, the instructions include executable code which, when executed causes aprocessing unit at a Network performance management system to identify one or more network counters based on the counter assign request. Further, the instructions include executable code which, when executed causes the processing unit from the Network performance management system to dynamically assign the one or more network counters to the one or more user groups associated with the counter assign request.OBJECTS OF THE DISCLOSURE

[0013] Some of the objects of the present disclosure, which at least one embodiment disclosed herein satisfies are listed herein below.

[0014] It is an object of the present disclosure to provide a system and a method for dynamically assigning network counters.

[0015] It is another object of the present disclosure to provide a solution that receives a target category from a set of categories associated with an assign counter request.

[0016] It is yet another object of the present disclosure to provide a solution that retrieves a list of counters based on at least one of the targets categories from the set of categories and a node.

[0017] It is yet another object of the present disclosure to provide a solution to assign one or more target counters to at least one of the user’s groups and the target user group received in the assign counter request.BRIEF DESCRIPTION OF THE DRAWINGS

[0018] 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.

[0019] FIG. 1 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.

[0020] FIG. 2 illustrates an exemplary block diagram of a system for dynamically assigning network counters, in accordance with exemplary implementations of the present disclosure.

[0021] FIG. 3 illustrates a method flow diagram for dynamically assigning network counters, in accordance with exemplary implementations of the present disclosure.

[0022] FIG. 4 illustrates an exemplary block diagram of a network performance management system, in accordance with the exemplary embodiments of the present disclosure.

[0023] FIG. 5 illustrates a system architecture diagram for dynamically assigning network counters, in accordance with exemplary implementations of the present disclosure.

[0024] FIG. 6 illustrates a signalling flow diagram for dynamically assigning network counters, in accordance with exemplary implementations of the present disclosure.

[0025] The foregoing shall be more apparent from the following more detailed description of the disclosure.DETAILED DESCRIPTION

[0026] 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.

[0027] 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 forimplementing 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.

[0028] 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.

[0029] It should be noted that the terms "first", "second", "primary", "secondary", "target" and the like, herein do not denote any order, ranking, quantity, or importance, but rather are used to distinguish one element from another.

[0030] 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.

[0031] 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.

[0032] 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 microprocessorsin 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.

[0033] As used herein, “a user equipment”, “a user device”, “a smart-user-device”, “a smartdevice”, “an electronic device”, “a mobile device”, “a handheld device”, “a wireless communication device”, “a mobile communication device”, “a communication device” may be any electrical, electronic and / or computing device or equipment, capable of implementing the features of the present disclosure. The user equipment / device may include, but is not limited to, a mobile phone, smart phone, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, wearable device or any other computing device which is capable of implementing the features of the present disclosure. Also, the user device may contain at least one input means configured to receive an input from unit(s) which are required to implement the features of the present disclosure.

[0034] 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.

[0035] 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.

[0036] 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, amicrocontroller, Application Specific Integrated Circuits (ASIC), Field Programmable Gate Array circuits (FPGA), any other type of integrated circuits, etc.

[0037] As used herein the transceiver unit include at least one receiver and at least one transmitter configured respectively for receiving and transmitting data, signals, information or a combination thereof between units / components within the system and / or connected with the system.

[0038] As used herein “User Interface” (UI) refers to the user interface is the point of interaction between a user and a computer system. It allows users to communicate with and control the system, typically through graphical elements such as windows, buttons, and menus.

[0039] As used herein “network management performance system refers to describe a system or component that manages and coordinates various processes or tasks within an environment.

[0040] As discussed in the background section, the current known solutions have several shortcomings. As discussed in the background section, the current known solutions for dynamic counter assignment have several shortcomings such as lack of a method that incorporates a dynamic counter assignment feature, which is essential for efficient network performance monitoring and management. The existing prior art fails to provide a solution that allows for the dynamic allocation of counters, thereby hindering the ability to analyse and troubleshoot network issues effectively. Additionally, the prior art does not offer the flexibility to configure counters into the application for monitoring specific network nodes as desired by the user. This limitation restricts the customization and granularity of network monitoring, impeding the ability to focus on specific areas of interest. Another technical challenge is the absence of adaptability to changing network requirements. As networks constantly evolve and expand, it is crucial to have a monitoring application that can readily adjust to new demands. The previous solution lacks configurability and adaptability, which hampers effective network management and monitoring in dynamic environments.

[0041] The present disclosure aims to overcome the above-mentioned and other existing problems in this field of technology by disclosing a novel solution centred around the dynamic counter assignment feature, which tackles the challenge of analysing dashboards based on specific counters configured for each user group. This solution stands out due to its unique approach and methodology employed to address this problem effectively. It also addresses the dependency of Key Performance Indicators (KPIs) on the counters by dynamically assigning counters to users.As a result, KPIs are composed only of relevant counters assigned to the user, eliminating any irrelevant information. This leads to better and optimized metrics, enhancing the efficiency of monitoring network issues for different user groups. Another distinctive aspect of this feature is its ability to recommend counters to users based on their profile and history, further improving the efficiency of monitoring processes. Overall, this innovative solution offers a comprehensive and efficient approach to analysing dashboards and monitoring network issues.

[0042] The KPI (Key Performance Indicator): Indicators that reflect the network performance. KPIs are collected from the network or are calculated from the network measurements. The KPI such as Accessibility KPI, Integrity KPI, Utilization KPI, Retainability KPI, Mobility KPI, and Energy Efficiency (EE) KPI.

[0043] As would be further noted, Dynamic Counter Assignment allows users to select only the counters that are relevant to their specific group or task. As a result, the dashboards are customized to show only the most relevant information, making it easier to analyze data and create more accurate KPIs. This feature also ensures that KPI calculations are based only on the selected counters, leading to more precise and useful performance measurements. Additionally, the system may suggest counters based on the user’s preferences and past behavior, saving time and improving the user experience.

[0044] This invention simplifies the management of large sets of counters, allowing users to upload counter details via an excel file for quick updates and adjustments, making the system both scalable and flexible.

[0045] Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the accompanying drawings.

[0046] FIG. 1 illustrates an exemplary block diagram of a computing device

[0100] 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

[0100] may also implement a method for managing performance data of a node in a network utilising the system. In another implementation, the computing device

[0100] itself implements the method for managing performance data of a node in a network using one or more units configured within the computing device

[0100] , wherein said one or more units are capable of implementing the features as disclosed in the present disclosure.

[0047] The computing device

[0100] may include a bus

[0102] or other communication mechanism for communicating information, and a hardware processor

[0104] coupled with bus

[0102] for processing information. The hardware processor

[0104] may be, for example, a general-purpose microprocessor. The computing device

[0100] may also include a main memory

[0106] , such as a random-access memory (RAM), or other dynamic storage device, coupled to the bus

[0102] for storing information and instructions to be executed by the processor

[0104] , The main memory

[0106] also may be used for storing temporary variables or other intermediate information during execution of the instructions to be executed by the processor

[0104] , Such instructions, when stored in non-transitory storage media accessible to the processor

[0104] , render the computing device

[0100] into a special-purpose machine that is customized to perform the operations specified in the instructions. The computing device

[0100] further includes a read only memory (ROM)

[0108] or other static storage device coupled to the bus

[0102] for storing static information and instructions for the processor

[0104] ,

[0048] A storage device

[0110] , such as a magnetic disk, optical disk, or solid-state drive is provided and coupled to the bus

[0102] for storing information and instructions. The computing device

[0100] may be coupled via the bus

[0102] to a display

[0112] , 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

[0114] , including alphanumeric and other keys, touch screen input means, etc. may be coupled to the bus

[0102] for communicating information and command selections to the processor

[0104] , Another type of user input device may be a cursor controller

[0116] , such as a mouse, a trackball, or cursor direction keys, for communicating direction information and command selections to the processor

[0104] , and for controlling cursor movement on the display

[0112] , This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allow the device to specify positions in a plane.

[0049] The computing device

[0100] 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

[0100] causes or programs the computing device

[0100] to be a special-purpose machine. According to one implementation, the techniques herein are performed by the computing device

[0100] in response to the processor

[0104] executing one or more sequences of one or more instructions contained in the main memory

[0106] , Such instructions may be read into the main memory

[0106] from another storage medium, such as the storage device

[0110] , Execution of the sequences of instructions contained in the main memory

[0106] causes the processor

[0104] 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.

[0050] The computing device

[0100] also may include a communication interface

[0118] coupled to the bus

[0102] , The communication interface

[0118] provides a two-way data communication coupling to a network link

[0120] that is connected to a local network

[0122] , For example, the communication interface

[0118] 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

[0118] may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, the communication interface

[0118] sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

[0051] The computing device

[0100] can send messages and receive data, including program code, through the network(s), the network link

[0120] and the communication interface

[0118] , In the Internet example, a server

[0130] might transmit a requested code for an application program through the Internet

[0128] , the ISP

[0126] , the local network

[0122] , the host

[0124] and the communication interface

[0118] , The received code may be executed by the processor

[0104] as it is received, and / or stored in the storage device

[0110] , or other non-volatile storage for later execution.

[0052] Referring to FIG. 2, an exemplary block diagram of a system

[0200] for dynamically assigning network counters, is shown, in accordance with the exemplary implementations of the present disclosure. As depicted in FIG. 2, the system

[0200] may include at least one transceiver unit

[0202] and at least one processing unit

[0204] , Also, all of the components / units of the system

[0200] are assumed to be connected to each other unless otherwise indicated below. As shown in the figures all units shown within the system

[0200] should also be assumed to be connected to each other. Also, in FIG. 2, only a few units are shown, however, the system

[0200] may include multiple such units or the system

[0200] may include any such numbers of said units, as required to implement the features of the present disclosure. Further, in an implementation, the system

[0200] may be present in a user device / user equipment

[0102] to implement the features of the present disclosure.

[0053] In one example, the system

[0200] may be implemented as or within a network performance management system (not depicted in FIG. 2). In such cases, the units of the system

[0200] , as depicted in FIG. 2, may be in communication with other entities and / or functions of the network performance management system. Such entities and / or functions have not been depicted and explained here for the sake of brevity, and would be well understood by a person skilled in the art.

[0054] The system

[0200] is configured for dynamically assigning network counters, with the help of the interconnection between the components / units of the system

[0200] ,

[0055] In one example, the transceiver unit

[0202] is configured to receive a counter assign request associated with one or more user groups.

[0056] In an implementation of the present disclosure, dynamically means that the assignment of network counters is done in real-time or near real-time based on current conditions or requirements. The network counters are metrics or identifiers used to track various parameters or performance indicators in a network. Further, the transceiver unit

[0202] is designed to accept requests for the assignment of network counters.

[0057] In an example, the system's transceiver unit

[0202] receives the request, which tells it to assign certain counters to one or more specific user groups. This allows the system to customize which counters are used for monitoring and analysis based on the needs of different user group.

[0058] Thereafter, a processing unit

[0204] , connected to at least the transceiver unit

[0202] , may identify, at a Network performance management system, one or more network counters based on the counter assign request.

[0059] In an implementation of the present disclosure, the processing unit

[0204] identifies the appropriate network counters using a Network performance management system system. This network performance management system acts as a centralized platform that manages and coordinates the various processes involved in network counter assignment. The identification process involves selecting one or more network counters specified in the counter assign request. Once identified, these network counters are dynamically assigned to the user groups associated with the request.

[0060] In an example, the one or more network counters are identified from a list of counters based on a network node and a category of the one or more user groups associated with the counter assign request. The one or more network counters are chosen from a pre-existing list based on the network node and the category of user group. A list of counters refers to a predefined collection of network counters, each representing a specific metric, parameter, or identifier used to monitor and measure various aspects of network performance. These counters can include data points such as bandwidth usage, latency, packet loss, error rates, throughput, connection durations, and other relevant indicators. The selection criteria include the specific network node (which can be a device or location within the network).

[0061] In an example, the processing unit

[0204] is further configured to recommend, using a trained model, at least one counter based on a user profile data, a user preference data, and a historical usage data of the one or more user groups. The processing unit

[0204] utilizes a trained model (likely a machine learning or Al model) to make recommendations based on the user profile data, the user preference data, and the historical usage data.

[0062] In another example, the recommendation of counters is informed by three types of data.

[0063] The user profile data includes information about the user or the user group, such as their role, responsibilities, and the types of tasks they typically perform.

[0064] The user preference data captures the specific preferences or choices of the user or user group. It may include preferences for certain types of metrics, the format in which data is displayed, or even past selections of counters.

[0065] The historical usage data is based on the user's or user group's past interactions with the system. It includes the counters they have used or selected in the past, the frequency of use, and the contexts in which those counters were applied. By analysing this historical data, the system can identify patterns and make more informed recommendations.

[0066] After that, the processing unit

[0204] is configured to dynamically assign, from the network performance management system, the one or more network counters to the one or more user groups associated with the counter assign request.

[0067] In an implementation of the present disclosure, the system dynamically assigns the identified network counters to the user groups associated with the counter assign request, leveraging the Network performance management system system. This dynamic assignment process means that the allocation of network counters happens in real-time or near real-time, allowing the system to respond quickly to current network conditions and requirements.

[0068] In an example, the transceiver unit

[0202] is further configured to transmit, from the network performance management system, a request completion status associated with the counter assign request based on dynamically assigning at least the one or more network counters to the one or more user groups.

[0069] After dynamically assigning the network counters, the transceiver unit

[0202] sends a status message indicating the completion of the request. This is done from the network performance management system and is based on the assignment of the network counters to the user groups.

[0070] Referring to FIG. 3, an exemplary method flow diagram

[0300] for dynamically assigning network counters, in accordance with exemplary implementations of the present disclosure is shown. In an implementation the method

[0300] is performed by the system

[0200] , Further, in an implementation, the system

[0200] may be present in a server device to implement the features of the present disclosure. Also, as shown in FIG. 3, the method

[0300] starts at step

[0302] ,

[0071] At step 304, the method comprises, receiving, by a transceiver unit

[0202] , a counter assign request associated with one or more user groups.

[0072] In an implementation of the present disclosure, dynamically means that the assignment of network counters is done in real-time or near real-time based on current conditions or requirements. The network counters are metrics or identifiers used to track various parameters or performance indicators in a network. Further, the transceiver unit

[0202] is designed to accept requests for the assignment of network counters.

[0073] In an example, the system's transceiver unit

[0202] receives the request, which tells it to assign certain counters to one or more specific user groups. This allows the system to customize which counters are used for monitoring and analysis based on the needs of different user group.

[0074] At step

[0306] , the method comprises, identifying, by a processing unit

[0204] at a Network performance management system, one or more network counters based on the counter assign request.

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

[0204] identifies the appropriate network counters using a Network performance management system system. This network performance management system acts as a centralized platform that manages and coordinates the various processes involved in network counter assignment. The identification process involves selecting one or more network counters specified in the counter assign request. Once identified, these network counters are dynamically assigned to the user groups associated with the request.

[0076] In an example, the one or more network counters are identified from a list of counters based on a network node and a category of the one or more user groups associated with the counter assign request. The one or more network counters are chosen from a pre-existing list based on the network node and the category of user group. A list of counters refers to a predefined collection of network counters, each representing a specific metric, parameter, or identifier used to monitor and measure various aspects of network performance. These counters can include data points such as bandwidth usage, latency, packet loss, error rates, throughput, connection durations, and other relevant indicators. The selection criteria include the specific network node (which can be a device or location within the network).

[0077] In an example, the processing unit

[0204] , using a trained model, may recommend at least one counter based on a user profile data, a user preference data, and a historical usage data of the one or more user groups.

[0078] The processing unit

[0204] utilizes a trained model (likely a machine learning or Al model) to make recommendations based on User Profile Data, User Preference Data, and Historical Usage Data:

[0079] The recommendation of counters is informed by three types of data.

[0080] The user profile data includes information about the user or the user group, such as their role, responsibilities, and the types of tasks they typically perform.

[0081] The user preference data captures the specific preferences or choices of the user or user group. It might include preferences for certain types of metrics, the format in which data is displayed, or even past selections of counters.

[0082] The historical usage data is based on the user's or user group's past interactions with the system. It includes the counters they have used or selected in the past, the frequency of use, and the contexts in which those counters were applied. By analysing this historical data, the system can identify patterns and make more informed recommendations.

[0083] At step

[0308] , the method comprises, dynamically assigning, by the processing unit

[0204] from the network performance management system, the one or more network counters to the one or more user groups associated with the counter assign request.

[0084] In an implementation of the present disclosure, the system dynamically assigns the identified network counters to the user groups associated with the counter assign request, leveraging the Network performance management system system. This dynamic assignment process means that the allocation of network counters happens in real-time or near real-time, allowing the system to respond quickly to current network conditions and requirements.

[0085] The method further comprises transmitting, by the transceiver unit

[0202] from the network performance management system, a request completion status associated with the counter assign request based on dynamically assigning at least the one or more network counters to the one or more user groups.

[0086] After dynamically assigning the network counters, the transceiver unit

[0202] sends a status message indicating the completion of the request. This is done from the network performance management system and is based on the assignment of the network counters to the user groups.

[0087] Thereafter, at step

[0310] , the method

[0300] is terminated.

[0088] Referring to FIG. 4, an exemplary block diagram of a network performance management system

[0400] , in accordance with the exemplary embodiments of the present disclosure is illustrated. In one example, the network performance management system

[0400] may be implemented as system

[0200] , as explained in conjunction with FIGs. 2-3.

[0089] As depicted in FIG. 4, the network performance management system

[0400] may include various sub-systems such as: performance management system [400a], normalization layer [400b], computation layer [400d], anomaly detection layer [400o], streaming engine

[4001] , load balancer [400k], operations and management system [400p], API gateway system [400r], analysis engine [400h], parallel computing framework [400i], forecasting engine [400t], distributed file system, mapping layer [400s], distributed data lake [400w], scheduling layer [400g], reporting engine [400m], message broker [400e], graph layer [400f], caching layer [400c], service quality manager [400q] and correlation engine[400n]. Exemplary connections between these subsystems are also as shown in FIG. 4. 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.

[0090] Following are the various components of the system

[0400] , the various components may include:

[0091] Performance management system [400a] comprises a performance engine [400v] and a Key Performance Indicator (KPI) Engine [400u],

[0092] Performance Management Engine [400v]: The Performance Management engine [400v] is a crucial component of the system, responsible for collecting, processing, and managing performance counter data from various data sources within the network. The gathered data includes metrics such as connection speed, latency, data transfer rates, and many others. This raw data is then processed and aggregated as required, forming a comprehensive overview of network performance. The processed information is then stored in a Distributed Data Lake [400w], a centralized, scalable, and flexible storage solution, allowing for easy access and further analysis. The Performance Management engine [400v] also enables the reporting and visualization of this performance counter data, thus providing network administrators with a real-time, insightful view of the network's operation. Through these visualizations, operators can monitor the network's performance, identify potential issues, and make informed decisions to enhance network efficiency and reliability.

[0093] Key Performance Indicator (KPI) Engine [400u]: The Key Performance Indicator (KPI) Engine is a dedicated component tasked with managing the KPIs of all the network elements. It uses the performance counters, which are collected and processed by the PerformanceManagement engine from various data sources. These counters, encapsulating crucial performance data, are harnessed by the KPI engine [400u] to calculate essential KPIs. These KPIs might include data throughput, latency, packet loss rate, and more. Once the KPIs are computed, they are segregated based on the aggregation requirements, offering a multi-layered and detailed understanding of network performance. The processed KPI data is then stored in the Distributed Data Lake [400w], ensuring a highly accessible, centralized, and scalable data repository for further analysis and utilization. Similar to the Performance Management engine, the KPI engine [400u] 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.

[0094] Ingestion layer: The Ingestion layer forms a key part of the Performance Management system. Its primary function is 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 it by validating its integrity and correctness to ensure it is fit for further use. Following validation, the data is routed to various components of the system, 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.

[0095] Normalization layer [400b]: The Normalization Layer [400b] serves to standardize, enrich, and store data into the appropriate databases. It takes in data that's 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 [400w], 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 [400b] 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 [400b] supplies the standardized data to several other subsystems. These include the Analysis Engine for detailed data examination, the Correlation Engine [400n] fordetecting 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 [400b] critical role in the entire system.

[0096] Caching layer [400c]: The Caching Layer [400c] in the Performance Management system plays a significant role in data management and optimization. During the initial phase, the Normalization Layer [400b] 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 [400c], The Caching Layer [400c] 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 [400c], the system significantly reduces the time taken to access this data, improving overall system efficiency and performance. Further, the Caching Layer [400c] serves as an intermediate layer between the data sources and the sub-systems, such as the Analysis Engine, Correlation Engine [400n], Service Quality Manager, and Streaming Engine. The Normalization Layer [400b] is responsible for providing these sub-systems with the necessary data from the Caching Layer [400c],

[0097] Computation layer [400d]: The Computation Layer [400d] in the 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 [400b], The Normalizer Layer then inserts this standardized data into multiple databases including the Distributed Data Lake [400w], Caching Layer [400c], and Graph Layer, and also feeds it to the Message Broker. Within the Computation Layer [400d], several powerful sub-systems such as the Analysis Engine, Correlation Engine [400n], 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 [400n] 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 [400d] is where all major computation and data processing tasks occur. It uses the normalized data provided by the Normalization Layer [400b], processing it to generate useful insights, ensure service quality, understand data patterns, and facilitate real-time data analytics.

[0098] Message broker [400e] : The Message Broker [400e], an integral part of the Performance Management system, 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 [400e] facilitates communication between data producers and consumers through messagebased topics. This creates an advanced platform for contemporary distributed applications. With the ability to accommodate a large number of permanent or ad-hoc consumers, the Message Broker [400e] demonstrates immense flexibility in managing data streams. Moreover, it leverages the filesystem for storage and caching, boosting its speed and efficiency. The design of the Message Broker [400e] 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 [400e] forms a critical component in managing and delivering real-time data in the system.

[0099] Graph layer [400fJ: The Graph Layer [400f , serving as the Relationship Modeler, plays a pivotal role in the Performance Management system. 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, Vprobe 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 [400n], 5G Performance Management Engine, or 5G KPI Engine [400u], With its powerful modeling and processing capabilities, the Graph Layer [400f] forms an essential part of the system, enabling the processing and analysis of complex relationships between various types of network data.

[0100] Scheduling layer [400g]: The Scheduling Layer [400g] serves as a key element of the Performance Management System, 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 [400w] or Distributed File System or sending it to another microservice. The versatility of the Scheduling Layer [400g] 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, theScheduling Layer [400g] orchestrates the systematic and periodic execution of tasks, making it an integral part of the efficient functioning of the entire system.

[0101] Analysis Engine [400h] : The Analysis Engine [400h] forms a crucial part of the Performance Management System, designed to provide an environment where users can configure and execute workflows for a wide array of use-cases. This facility aids in the debugging process and facilitates a better understanding of call flows. With the Analysis Engine [400h], 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 [400h] 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.

[0102] Parallel Computing Framework [400i] : The Parallel Computing Framework [400i] is a key aspect of the Performance Management System, providing a user-friendly yet advanced platform for executing computing tasks in parallel. This framework showcases both scalability and fault tolerance, crucial for managing vast amounts of data. Users can input data via Distributed File System (DFS) [400j] 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 [400i] is an essential tool for enhancing processing speeds and efficiently managing computing resources, significantly improving the system's performance management capabilities.

[0103] Distributed File System [400j]: The Distributed File System (DFS) [400j] is a critical component of the Performance Management System, enabling multiple clients to access and interact with data seamlessly. This file system 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 [400j] effectively allows for the distribution of data across multiple nodes. This architecture enhances both the scalability and redundancy of the system, ensuring optimal performance evenwith large data sets. DFS [400j] 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.

[0104] Load Balancer [400k]: The Load Balancer (LB) [400k] is a vital component of the Performance Management System, 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 [400k] implements various routing strategies to manage traffic. These include round-robin scheduling, header-based request dispatch, and contextbased 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. Context-based dispatching routes traffic based on the contextual information about the incoming requests. For example, in an event-driven architecture, the LB [400k] 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.

[0105] Streaming Engine

[4001] : The Streaming Engine

[4001] , also referred to as Stream Analytics, is a critical subsystem in the Performance Management System. 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

[4001] , After processing, the data is streamed to the UI, fostering rapid decision-making and responses. The Streaming Engine

[4001] cooperates with the Distributed Data Lake [400w], Message Broker [400e], and Caching Layer [400c] 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 [400w], Message Broker [400e], and Caching Layer [400c] as per the requirement and deliver it to the UI in real-time. The streaming engine's

[4001] goal is to provide fast, reliable, and efficient data streaming, contributing to the overall performance of the management system.

[0106] Reporting Engine [400m]: The Reporting Engine [400m] is a key subsystem of the Performance Management System. The fundamental purpose of designing the Reporting Engine [400m] is to dynamically create report layouts of API data, catered to individual client 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 [400m] to process and compile data from various interfaces. The main output of the Reporting Engine [400m] is a detailed report generated in Excel format. The Reporting Engine’s [400m] 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 [400m] 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.

[0107] In the preferred embodiment as illustrated in FIG. 5, the connections between the various components of a system

[0500] are established using different protocols and mechanisms, as well known in the art. For example:

[0108] UI interface to network performance management system: The connection between the User Interface (UI)

[0502] and the network performance management system is established using an HTTP connection. HTTP (Hypertext Transfer Protocol) is a widely used protocol for communication between web browsers and servers. It allows the UI

[0502] to send requests and configurations to the network performance management system , and also receive responses or acknowl edgments .

[0109] PM to DDL: The connection between the network performance management system and the Distributed Data Lake (DDL)

[0506] is established using a TCP (Transmission Control Protocol) connection. TCP is a reliable and connection-oriented protocol that ensures the integrity and ordered delivery of data packets. By using TCP, the network performance management system can save and retrieve relevant data from the DDL

[0506] for computations, ensuring data consistency and reliability.

[0110] In some embodiments, the system

[0500] may include a load balancer

[0508] for managing connections. The load balancer

[0508] is adapted to distribute the incoming network traffic acrossmultiple servers or components to ensure optimal resource utilization and high availability. Particularly, the load balancer

[0508] is commonly employed to evenly distribute incoming requests across multiple instances of the network performance management system providing scalability and fault tolerance to the system

[0500] , Overall, these connections and the inclusion of the load balancer

[0500] help to facilitate effective communication, data transfer, and resource management within the system

[0500] , enhancing its performance and reliability.

[0111] Referring to FIG. 6, a signalling flow diagram

[0600] for dynamically assigning network counters, in accordance with exemplary implementations of the present disclosure, is illustrated.

[0112] The interactions between various components involved in processing a counter assignment request.

[0113] User (600): The initiator of the request to assign network counters to specific user groups.

[0114] UI Server: The interface that receives the request from the user and interacts with other components to process the request.

[0115] Load Balancer (508): Distributes the processing load across different components to ensure efficient handling of the request.

[0116] Network Performance Management System (504): A central system responsible for validating the request, managing network counters, and coordinating the assignment process.

[0117] Distributed Data Lake (506): A storage system that holds the data related to network counters, including performance metrics and user group associations.

[0118] At step SI : The user initiates the process by submitting a counter assignment request. The process begins with the User (600) submitting a request to assign a specific counter to a user group. This request is sent to the UI Server, which acts as the interface between the user and the rest of the system.

[0119] At step 2: In one example, the UI Server forwards the user's request to the Load Balancer (508). The role of the Load Balancer is to manage and distribute the request load across the system's components.

[0120] At step S3: This network performance management system

[0504] acts as a centralized platform that manages and coordinates the various processes involved in network counter assignment. The identification process involves selecting one or more network counters that specified in the counter assign request. Once identified, these network counters are dynamically assigned to the user groups associated with the request. After the network performance management system

[0504] updates the counter's data, this information is stored in the distributed data lake

[0506] , ensuring it is accessible for future reference and analysis.

[0121] Step S4: Once the counter's data is successfully updated, the Network Performance Management System (504) sends a confirmation message back to the UI Server, indicating that the request validation was successful. This validation may include verifying the validity of the user group making the request, ensuring that the requested network counters are available and not already in use, and confirming that the request data is consistent and complete.

[0122] At Step S6: If the request fails validation (e.g., due to incorrect data or unmet criteria), the Network Performance Management System (504) sends a failure message to the UI Server.

[0123] Step S7: The UI Server then informs the User (600) that the request has failed, providing an appropriate error message.

[0124] The present disclosure further discloses a non-transitory computer readable storage medium storing instructions for dynamically assigning network counters, the instructions include executable code which, when executed by one or more units of a system

[0200] , causes a transceiver unit

[0202] of the system

[0200] to receive a counter assign request associated with one or more user groups. Further, the instructions include executable code which, when executed causes a processing unit

[0204] at a Network performance management system to identify one or more network counters based on the counter assign request. Further, the instructions include executable code which, when executed causes the processing unit

[0204] from the Network performance management system to dynamically assign the one or more network counters to the one or more user groups associated with the counter assign request.

[0125] As is evident from the above, the present disclosure provides a technically advanced solution for dynamic network counters assignment. The novel solution as disclosed in the present disclosure provides several technical advantages that make it a valuable innovation in the field. Firstly, it enables users to analyse tailored dashboards that cater to their specific needs, resultingin more accurate and actionable insights. By performing computations based only on relevant counters, the metrics derived from these dashboards become more meaningful, allowing network issues to be promptly addressed. Secondly, the assignment of user groups to their respective counters holds significant importance in the creation of Key Performance Indicators (KPIs). This feature allows for the development of meaningful KPIs that accurately measure and track performance. Thirdly, the solution eliminates the inclusion of irrelevant or unnecessary counters in KPI calculations, leading to more reliable and meaningful KPI metrics. This ensures that the derived metrics reflect the true performance of the network without any misleading or extraneous data. Fourthly, leveraging Al capabilities, the feature goes beyond static assignment and can suggest relevant counters to users based on their profiles, preferences, and historical usage. This dynamic recommendation system enhances computation and analysis efficiency, resulting in a better user experience. Lastly, the feature offers scalability and flexibility by allowing users to upload counter details via an Excel file. This facilitates the efficient management of large sets of counter data, enabling quick updates and adjustments as needed. This scalability ensures that the solution can accommodate growing data volumes and evolving network requirements. In conclusion, the technical advantages provided by this novel solution for patent specification offer improved accuracy, meaningful insights, reliable metrics, efficient computation, and scalability. These advantages position the invention as a valuable contribution to the field, addressing the challenges associated with analysing dashboards and monitoring network issues in a more effective and user-friendly manner.

[0126] 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.

[0127] 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 andsubstitutions 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 [300] for dynamically assigning network counters, the method [300] comprising: receiving [304], by a transceiver unit [202], a counter assign request associated with one or more user groups; identifying [306], by a processing unit [204] at a Network performance management system, one or more network counters based on the counter assign request; and dynamically assigning [308], by the processing unit [204] from the network performance management system, the one or more network counters to the one or more user groups associated with the counter assign request.

2. The method [300] as claimed in claim 1, further comprising transmitting, by the transceiver unit [202] from the network performance management system, a request completion status associated with the counter assign request based on dynamically assigning at least the one or more network counters to the one or more user groups.

3. The method [300] as claimed in claim 1, wherein the one or more network counters are identified from a list of counters based on a network node and a category of the one or more user groups associated with the counter assign request.

4. The method [300] as claimed in claim 1, further comprising: recommending, by the processing unit [204] using a trained model, at least one counter based on a user profile data, a user preference data, and a historical usage data of the one or more user groups.

5. A system [200] for dynamically assigning network counters, the system [200] comprises: a transceiver unit [202], wherein the transceiver unit [202] is configured to:• receive a counter assign request associated with one or more user groups, a processing unit [204] connected to at least the transceiver unit [202], wherein the processing unit [204] is configured to:• identify, at a Network performance management system, one or more network counters based on the counter assign request,• dynamically assign, from the network performance management system, the one or more network counters to the one or more user groups associated with the counter assign request.

6. The system [200] as claimed in claim 5, wherein the transceiver unit [202] is further configured to transmit, from the network performance management system, a request completion status associated with the counter assign request based on dynamically assigning at least the one or more network counters to the one or more user groups.

7. The system [200] as claimed in claim 5, wherein the one or more network counters are identified from a list of counters based on a network node and a category of the one or more user groups associated with the counter assign request.

8. The system [200] as claimed in claim 5, wherein the processing unit [204] is further configured to recommend, using a trained model, at least one counter based on a user profile data, a user preference data, and a historical usage data of the one or more user groups.

9. A non-transitory computer-readable storage medium storing instructions for dynamically assigning network counters, the instructions comprising executable code which, when executed by one or more units of a system [200], causes: a transceiver unit [202] to receive a counter assign request associated with one or more user groups; a processing unit [204], at a Network performance management system, to identify one or more network counters based on the counter assign request; and the processing unit [204], from the network performance management system, to dynamically assign the one or more network counters to the one or more user groups associated with the counter assign request.

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