Method and system for performing predictive analysis of database clusters

Abstract

The present disclosure relates to a method and a system for performing predictive analysis of database cluster within a plurality of database clusters The method comprises collecting, by a collecting unit [302], data associated with a set of historical performance metrics from a plurality of database clusters. The method comprises detecting, by a detecting unit [304] using a trained model, one or more anomalies in the set of historical performance metrics data. Furthermore, the method comprises performing, by a processing unit [306], a root cause analysis for the detected one or more anomalies. The method comprises generating, by a generating unit [308], a report based on the root cause analysis. The method comprises rendering, by a display unit [310], the generated report.

Description

METHOD AND SYSTEM FOR PERFORMING PREDICTIVE ANALYSIS OF DATABASE CLUSTERSTECHNICAL FIELD

[0001] Embodiments of the present disclosure generally relate to network performance management systems. More particularly, embodiments of the present disclosure relate to performing predictive analysis of database cluster within a database clusters.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] Wireless communication technology has rapidly evolved over the past few decades, with each generation bringing significant improvements and advancements. The first generation of wireless communication technology was based on analog technology and offered only voice services. However, with the advent of the second generation (2G) technology, digital communication and data services became possible, and text messaging was introduced. The third generation (3G) technology marked the introduction of high-speed internet access, mobile video calling, and location-based services. The fourth generation (4G) technology revolutionized wireless communication with faster data speeds, better network coverage, and improved security. Currently, the fifth generation (5G) technology is being deployed, promising even faster data speeds, low latency, and the ability to connect multiple devices simultaneously. With each generation, wireless communication technology has become more advanced, sophisticated, and capable of delivering more services to its users.

[0004] Tracing cluster health changes in a 5G network can be challenging primarily, due to high complexity of multitude of interconnected network elements. Real-time monitoring of cluster health may lead to delays in data collection and analysis. Also, 5G networks generate vast amounts of data relating to network performance and health, thereby, leading to huge overhead caused by even sophisticated analytics tools.

[0005] Thus, there exists an imperative need in the art to develop methods and systems for tracing cluster health changes and analyzing historical data.SUMMARY

[0006] This section is provided to introduce certain aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.

[0007] An aspect of the present disclosure may relate to a method for performing predictive analysis of database cluster within a plurality of database clusters. The method comprises collecting, by a collecting unit, data associated with a set of historical performance metrics from the plurality of database clusters. The method further comprises detecting, by a detecting unit using a trained model, one or more anomalies in the set of historical performance metrics data. The method further comprises performing, by a processing unit, a root cause analysis for the detected one or more anomalies. Furthermore, the method comprises generating, by a generating unit, a report based on the root cause analysis. The method further comprises rendering, by a display unit, the generated report.

[0008] In an exemplary aspect of the present disclosure, the set of historical performance metrics data comprises at least one of central processing unit (CPU) utilisation, memory utilisation, network traffic, storage usage, and application log.

[0009] In an exemplary aspect of the present disclosure, the method further comprises preprocessing, by the processing unit, the collected set of historical performance metrics data, wherein the preprocessing comprises performing at least one of data cleaning, transformation, and filtering.

[0010] In an exemplary aspect of the present disclosure, the root cause analysis comprises at least one of examining logs, reviewing configuration changes, and investigating impact of external factors.

[0011] In an exemplary aspect of the present disclosure, the one or more anomalies corresponds to unusual pattern that indicate at least one of a potential issue or an irregular behaviour within theplurality of database clusters, wherein the irregular behaviour comprises at least: increase query latency, unbalanced load distribution, high resource utilization.

[0012] In an exemplary aspect of the present disclosure, the trained model is trained based on the set of historical performance metrics data.

[0013] In an exemplary aspect of the present disclosure, the set of historical performance metrics data is collected at a preconfigured time period periodically.

[0014] In an exemplary aspect of the present disclosure, the method further comprises selecting, by a selecting unit, a set of key performance metrics from the set of historical performance metrics, wherein the selection of set of key performance metrics is based on a database of the plurality of database clusters being analysed for detecting the one or more anomalies.

[0015] In an exemplary aspect of the present disclosure, the set of key performance metrics comprises at least one of response time, throughput, error rate, and resource utilization.

[0016] In an exemplary aspect of the present disclosure, the generated report comprises a set of actionable recommendations for optimizing performance of plurality of database clusters. The set of actionable recommendations comprises at least recommendation for scaling size of the plurality of database clusters based on the detected one or more anomalies.

[0017] In an exemplary aspect of the present disclosure, the scaling comprises at least one of scale up and scale down.

[0018] Another aspect of the present disclosure may relate to a system for performing predictive analysis of database cluster within a plurality of database clusters. The system comprises a collecting unit. The collecting unit is configured to collect data associated with a set of historical performance metrics from the plurality of database clusters. The system further comprises a detecting unit. The detecting unit may be using a trained model to detect one or more anomalies in the set of historical performance metrics data. The system further comprises a processing unit. The processing unit is configured to perform a root cause analysis for the detected one or more anomalies. The root cause analysis comprises at least one of examining logs, reviewing configuration changes, and investigating impact of external factors. The system further comprises a generating unit. The generating unit is configured to generate a report based on the root causeanalysis. Furthermore, the system comprises a display unit. The display unit is configured to render the generated report.

[0019] Yet another aspect of the present disclosure may relate to a non-transitory computer readable storage medium, storing one or more instructions for performing predictive analysis of database cluster within a plurality of database clusters, the instructions include executable code which, when executed by one or more units of a system cause a collecting unit to collect data associated with a set of historical performance metrics from the plurality of database clusters. The instructions when executed by the system further cause a detecting unit, using a trained model, to detect one or more anomalies in the set of historical performance metrics data. The instructions when executed by the system further cause a processing unit to perform a root cause analysis for the detected one or more anomalies. The root cause analysis comprises at least one of examining logs, reviewing configuration changes, and investigating impact of external factors. The instructions when executed by the system further cause a generating unit to generate a report based on the root cause analysis. The instructions when executed by the system further cause a display unit configured to render the generated report.OBJECTS OF THE INVENTION

[0020] Some of the objects of the present disclosure, which at least one embodiment disclosed herein satisfies are listed herein below.

[0021] It is an object of the present disclosure to provide a system and a method for gathering historical data from different regions of 5G network within the cluster, such as monitoring tools, logs, performance metrics, and resource usage statistics.

[0022] It is another object of the present disclosure provides numerous functionality such as metric selection, reporting and recommendations, continuous learning, health and performance of database, to ensure the overall stability, availability, and performance of database.

[0023] It is yet another object of the present disclosure to perform anomaly detection, root cause analysis and predictive analysis to remove the anomalies.DESCRIPTION OF THE DRAWINGS

[0024] 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.

[0025] FIG. 1 illustrates an exemplary block diagram representation of 5th generation core (5GC) network architecture, in accordance with exemplary implementation of the present disclosure.

[0026] FIG. 2 illustrates an exemplary block diagram of a computing device upon which the features of the present disclosure may be implemented in accordance with exemplary implementation of the present disclosure.

[0027] FIG. 3 illustrates an exemplary block diagram of a system for performing predictive analysis of database cluster within a database clusters, in accordance with exemplary implementations of the present disclosure.

[0028] FIG. 4 illustrates a method flow diagram for performing predictive analysis of database cluster within a database clusters, in accordance with exemplary implementations of the present disclosure.

[0029] FIG. 5 illustrates an implementation of the system for performing predictive analysis of database cluster within a database clusters, in accordance with exemplary implementations of the present disclosure.

[0030] FIG. 6 illustrates an implementation of the method of collecting historic performance data for performing predictive analysis of database cluster within a database clusters, in accordance with exemplary implementations of the present disclosure.

[0031] FIG. 7 illustrates a second implementation of a method for generation of a report for predictive analysis of database cluster within a database clusters, in accordance with exemplary implementations of the present disclosure.

[0032] The foregoing shall be more apparent from the following more detailed description of the disclosure.DETAILED DESCRIPTION

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] As used herein, a “processing unit” or “processor” or “operating processor” includes one or more processors, wherein processor refers to any logic circuitry for processing instructions. A processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a (Digital Signal Processing) DSP core, a controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Array circuits, any other type of integrated circuits, etc. The processor may perform signal coding data processing, input / output processing, and / or any other functionality that enables the working of the system according to the present disclosure. More specifically, the processor or processing unit is a hardware processor.

[0039] As used herein, “a user equipment”, “a user device”, “a smart-user-device”, “a smartdevice”, “an electronic device”, “a mobile device”, “a handheld device”, “a wireless communication device”, “a mobile communication device”, “a communication device” may be any electrical, electronic and / or computing device or equipment, capable of implementing the features of the present disclosure. The user equipment / device may include, but is not limited to, a mobile phone, smart phone, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, wearable device or any other computing device which is capable of implementing the features of the present disclosure. Also, the user device may contain at least one input means configured to receive an input from at least one of a transceiver unit, a processing unit, a storage unit, a detection unit and any other such unit(s) which are required to implement the features of the present disclosure.

[0040] As used herein, “storage unit” or “memory unit” refers to a machine or computer-readable medium including any mechanism for storing information in a form readable by a computer orsimilar machine. For example, a computer-readable medium includes read-only memory (“ROM”), random access memory (“RAM”), magnetic disk storage media, optical storage media, flash memory devices or other types of machine-accessible storage media. The storage unit stores at least the data that may be required by one or more units of the system to perform their respective functions.

[0041] 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.

[0042] All modules, units, components used herein, unless explicitly excluded herein, may be software modules or hardware processors, the processors being a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASIC), Field Programmable Gate Array circuits (FPGA), any other type of integrated circuits, etc.

[0043] 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.

[0044] As discussed in the background section, the current known solutions have several shortcomings. The present disclosure aims to overcome the problems mentioned in the background and other existing problems in this field of technology by providing method and system of performing predictive analysis of database cluster within a plurality of database clusters. The present disclosure provides functionality to address Anomaly Detection, Root Cause Analysis, Performance Measurement, Predictive Analysis and Root Cause Identification.

[0045] FIG. 1 illustrates an exemplary block diagram representation of 5th generation core (5GC) network architecture

[0100] , in accordance with exemplary implementation of the present disclosure. As shown in FIG. 1, the 5GC network architecture

[0100] includes a user equipment (UE)

[0102] , a radio access network (RAN)

[0104] , an access and mobility management function (AMF)

[0106] , a Session Management Function (SMF)

[0108] , a Service Communication Proxy(SCP)

[0110] , an Authentication Server Function (AUSF)

[0112] , a Network Slice Specific Authentication and Authorization Function (NSSAAF)

[0114] , a Network Slice Selection Function (NSSF)

[0116] , a Network Exposure Function (NEF)

[0118] , a Network Repository Function (NRF)

[0120] , a Policy Control Function (PCF)

[0122] , a Unified Data Management (UDM)

[0124] , an application function (AF)

[0126] , a User Plane Function (UPF)

[0128] , a data network (DN)

[0130] , wherein all the components are assumed to be connected to each other in a manner as obvious to the person skilled in the art for implementing features of the present disclosure.

[0046] The Radio Access Network (RAN)

[0104] is the part of a mobile telecommunications system that connects user equipment (UE)

[0102] to the core network (CN) and provides access to different types of networks (e.g., 5G network). It consists of radio base stations and the radio access technologies that enable wireless communication.

[0047] The Access and Mobility Management Function (AMF)

[0106] is a 5G core network function responsible for managing access and mobility aspects, such as UE registration, connection, and reachability. It also handles mobility management procedures like handovers and paging.

[0048] The Session Management Function (SMF)

[0108] is a 5G core network function responsible for managing session-related aspects, such as establishing, modifying, and releasing sessions. It coordinates with the User Plane Function (UPF) for data forwarding and handles IP address allocation and QoS enforcement.

[0049] The Service Communication Proxy (SCP)

[0110] is a network function in the 5G core network that facilitates communication between other network functions by providing a secure and efficient messaging service. It acts as a mediator for service-based interfaces.

[0050] The Authentication Server Function (AUSF)

[0112] is a network function in the 5G core responsible for authenticating UEs during registration and providing security services. It generates and verifies authentication vectors and tokens.

[0051] The Network Slice Specific Authentication and Authorization Function (NSSAAF)

[0114] is a network function that provides authentication and authorization services specific to network slices. It ensures that UEs can access only the slices for which they are authorized.

[0052] The Network Slice Selection Function (NSSF)

[0116] is a network function responsible for selecting the appropriate network slice for a UE based on factors such as subscription, requested services, and network policies.

[0053] The Network Exposure Function (NEF)

[0118] is a network function that exposes capabilities and services of the 5G network to external applications, enabling integration with third-party services and applications.

[0054] The Network Repository Function (NRF)

[0120] is a network function that acts as a central repository for information about available network functions and services. It facilitates the discovery and dynamic registration of network functions.

[0055] The Policy Control Function (PCF)

[0122] is a network function responsible for policy control decisions, such as QoS, charging, and access control, based on subscriber information and network policies.

[0056] The Unified Data Management (UDM)

[0124] is a network function that centralizes the management of subscriber data, including authentication, authorization, and subscription information.

[0057] The Application Function (AF)

[0126] is a network function that represents external applications interfacing with the 5G core network to access network capabilities and services.

[0058] The User Plane Function (UPF)

[0128] is a network function responsible for handling user data traffic, including packet routing, forwarding, and QoS enforcement.

[0059] The Data Network (DN)

[0130] refers to a network that provides data services to user equipment (UE) in a telecommunications system. The data services may include but are not limited to Internet services, private data network related services.

[0060] The 5GC network architecture

[0100] also comprises a plurality of interfaces for connecting the network functions with a network entity for performing the network functions. The NSSF

[0116] is connected with the network entity via the interface denoted as (Nnssf) interface in the figure. The NEF

[0118] is connected with the network entity via the interface denoted as (Nnef) interface in the figure. The NRF

[0120] is connected with the network entity via the interface denoted as(Nnrf) interface in the figure. The PCF

[0122] is connected with the network entity via the interface denoted as (Npcf) interface in the figure. The UDM

[0124] is connected with the network entity via the interface denoted as (Nudm) interface in the figure. The AF

[0126] is connected with the network entity via the interface denoted as (Naf) interface in the figure. The NSSAAF

[0114] is connected with the network entity via the interface denoted as (Nnssaaf) interface in the figure. The AUSF

[0112] is connected with the network entity via the interface denoted as (Nausf) interface in the figure. The AMF

[0106] is connected with the network entity via the interface denoted as (Namf) interface in the figure. The SMF

[0108] is connected with the network entity via the interface denoted as (Nsmf) interface in the figure. The SMF

[0108] is connected with the UPF

[0128] via the interface denoted as (N4) interface in the figure. The UPF

[0128] is connected with the RAN

[0104] via the interface denoted as (N3) interface in the figure. The UPF

[0128] is connected with the DN

[0130] via the interface denoted as (N6) interface in the figure. The RAN

[0104] is connected with the AMF

[0106] via the interface denoted as (N2). The AMF

[0106] is connected with the RAN

[0104] via the interface denoted as (Nl). The UPF

[0128] is connected with other UPF

[0128] via the interface denoted as (N9). The interfaces such as Nnssf, Nnef, Nnrf, Npcf, Nudm, Naf, Nnssaaf, Nausf, Namf, Nsmf, N9, N6, N4, N3, N2, and Nl can be referred to as a communication channel between one or more functions or modules for enabling exchange of data or information between such functions or modules, and network entities.

[0061] FIG. 2 illustrates an exemplary block diagram of a computing device

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

[0200] may also implement a method for performing predictive analysis of database cluster within a database clusters, utilising the system. In another implementation, the computing device

[0200] itself implements the method performing predictive analysis of database cluster within a database clusters, using one or more units configured within the computing device

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

[0062] The computing device

[0200] may include a bus

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

[0204] coupled with bus

[0202] for processing information. The hardware processor

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

[0200] may also include a main memory

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

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

[0204] , The main memory

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

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

[0204] , render the computing device

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

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

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

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

[0204] ,

[0063] A storage device

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

[0202] for storing information and instructions. The computing device

[0200] may be coupled via the bus

[0202] to a display

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

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

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

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

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

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

[0212] , 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.

[0064] The computing device

[0200] may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and / or program logic which in combination with the computing device

[0200] causes or programs the computing device

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

[0200] in response to the processor

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

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

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

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

[0206] causes the processor

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

[0065] The computing device

[0200] also may include a communication interface

[0218] coupled to the bus

[0202] , The communication interface

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

[0220] that is connected to a local network

[0222] , For example, thecommunication interface

[0218] may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface

[0218] may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, the communication interface

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

[0066] The computing device

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

[0220] and the communication interface

[0218] , In the Internet example, a server

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

[0228] , the ISP

[0226] , the local network

[0222] , a host

[0224] and the communication interface

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

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

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

[0067] The present disclosure is implemented by a system

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

[0300] may include the computing device

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

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

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

[0068] Referring to FIG. 3, an exemplary block diagram of a system

[0300] for performing predictive analysis of database cluster within a database clusters is shown, in accordance with the exemplary implementations of the present disclosure. The system

[0300] comprises at least one collecting unit

[0302] , at least one detecting unit

[0304] , at least one processing unit

[0306] , at least one generating unit

[0308] and at least one display unit

[0310] and at least one selecting unit

[0312] , Also, all of the components / units of the system

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

[0300] may comprise multiple such units or the system

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

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

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

[0300] may reside in a server or a network entity. In yet anotherimplementation, the system

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

[0069] The system

[0300] is configured for performing predictive analysis of database cluster within a plurality of database clusters, with the help of the interconnection between the components / units of the system

[0300] , In an implementation of the present disclosure, the term refers herewith ‘database clusters’ is the groups of databases that are managed and monitored collectively.

[0070] Examples of such database clusters include but may not be limited to Nosql databases such as “mongodb” cluster, “redis”, “kafka”, “cassandra”, or “Oracle”. It may be noted that such database clusters are only exemplary, and in no manner construed to limit the scope of the present subject matter in any manner. As would be explained in the foregoing description, the health status and topology of these clusters are tracked to confirm their proper functioning.

[0071] The predictive analysis of database cluster in the database cluster refers to a process of using historical performance metrics data to predict one or more anomalies of the database cluster. For instance, the historical performance metrics data shows that during peak hours, a database of the system starts to slow down and sends corrupt data to a user. Based on the historical performance metrics data, the system

[0300] during peak hours, may activate a second database to avoid the anomaly. The one or more anomalies may include an unexpected surge in a query or abnormal patterns in data. In an implementation, the system

[0300] may perform the functionalities in the 5th generation core network. In another implementation, the system

[0300] may perform the functionalities in a 4th generation network, 6th generation network, or any other future generations of network.

[0072] The collecting unit

[0302] is configured to collect data associated with a set of historical performance metrics. The data may be collected from the plurality of database clusters. In one example, the set of historical performance metrics data comprises at least one of central processing unit (CPU) utilisation, memory utilisation, network traffic, storage usage, and application log. The CPU utilization refers to the percentage of CPU’s capacity being used by the database. A high CPU utilization may indicate that the database is under heavy load, while a low CPU utilization indicates that the CPU is idle or underutilized. The memory utilization refers to amount of RAM (Random Access Memory) being used by the database. The network traffic refers to amount of data being transmitted and received over a network at the database. The storage usage refers to anactual amount of storage of the database utilized. The application log refers to a record of events including information about errors, warnings, and the like.

[0073] In one example, the set of historical performance metrics data is collected at a preconfigured time period periodically. The preconfigured time period may be defined by a user. The user may be one of a system operator, a network operator, and the like. To collect the set of historical performance metrics data, the collecting unit

[0302] may query the plurality of database clusters.

[0074] The processing unit

[0306] is further configured to preprocess the collected set of historical performance metrics data. In one example, the preprocessing of the collected set of historical performance metrics data includes but may not be limited to performing at least one of data cleaning, transformation, and filtering.

[0075] The selecting unit

[0312] is configured to select a set of key performance metrics from the set of historical performance metrics. In one example, the key performance metrics may be selected based on selection of a database from the plurality of database clusters that may be analysed. The key performance metrics refer to a measurement to evaluate the success of the plurality of database. The set of key performance metrics comprises at least one of response time, throughput, error rate, and resource utilization.

[0076] The detecting unit

[0304] is configured to detect one or more anomalies in the set of historical performance metrics data. In one example, the one or more anomalies may be one of an unusual pattern. The unusual pattern refers to a deviation from normal behaviour within the plurality of dataset clusters. The unusual pattern may indicate at least one of a potential issue or an irregular behaviour in the plurality of database clusters. An example of the unusual pattern may be an unexpected increase or decrease in the CPU utilization or the network traffic. The potential issue refers to a problem that may affect the performance of the database.

[0077] The irregular behaviour includes but may not be limited to at least increase query latency, unbalanced load distribution, high resource utilization. The increased query latency refers to an increase in delay in the time taken by the database to process a received query and the delay in time to return results for the query. The unbalanced load distribution refers to an uneven load distribution of workload across nodes in the database cluster. The unbalanced load distribution may affect performance of the database. The high resource utilization refers to uneven resourceutilization across the system where some resources are being highly consumed and other resources are not utilized.

[0078] In one example, the detecting unit

[0304] may be using a trained model. The trained model refers to a model that is exposed to the historical performance metrics data. Based on the historical performance metrics data, the trained model stores a pattern, relationship, and other insights. The trained model is trained based on the set of historical performance metrics data.

[0079] The processing unit

[0306] is configured to perform a root cause analysis. The root cause analysis refers to a process to detect underlying issues in the one or more anomalies. In one example, the root cause analysis includes but may not be limited to at least one of examining logs, reviewing configuration changes, and investigating impact of external factors. The examining of logs refers to reviewing and other recorded data to identify patterns, errors, to indicate the root cause of the one or more anomalies. The logs refer to a sequence of events in a process to identify the sequence where the problem may be occurring. The reviewing of the configuration changes refers to checking any recent modifications made to the configuration of the database as the changes in configuration may sometimes lead to the one or more anomalies.

[0080] The generating unit

[0308] is configured to generate a report based on the root cause analysis. In one example, the generated report includes but may not be limited to a set of actionable recommendations for optimizing performance of plurality of database clusters. The processing unit

[0308] may send the root cause analysis to the generating unit

[0308] to add the set of actionable recommendations. The set of actionable recommendations may include sending alerts via the generated report. The alerts may be sent via a configured notification channel based on irregular behaviours in the plurality of database metrics. In an example, the generating unit

[0308] may compile results of the root cause analysis such as compiling a summary of the detected anomalies, identified root causes, and any other relevant data to generate the report.

[0081] The set of actionable recommendations includes but may not be limited to a recommendation for scaling a size of the plurality of database clusters based on the detected one or more anomalies. The scaling comprises at least one of scale up and scale down. The scale up refers to adding to more resources to existing resources. For instance, increasing the amount of memory for more memory usage, if the root cause analysis shows the causes of the one or more anomalies to be due to high memory utilization. The scale down refers to removing resources from existing resources.

[0082] The display unit

[0310] is configured to render the generated report. The display unit

[0310] may be a user interface (UI). The UI may be a graphical user interface (GUI). The GUI refers to an interface to interact with the system

[0300] by visual or graphical representation of icons, menu, etc. The GUI may be a smartphone, laptop, computer, etc.

[0083] Referring to FIG. 4, an exemplary method flow diagram

[0400] for performing predictive analysis of database cluster within a database clusters, in accordance with exemplary implementations of the present disclosure is shown. In an implementation the method

[0400] is performed by the system

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

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

[0400] starts at step

[0402] ,

[0084] At step

[0404] , the method

[0400] comprises collecting, by a collecting unit

[0302] , data associated with a set of historical performance metrics. In one example, the data may be collected from a plurality of database clusters. The set of historical performance metrics data comprises at least one of central processing unit (CPU) utilisation, memory utilisation, network traffic, storage usage, and application log.

[0085] The set of historical performance metrics data is collected at a preconfigured time period periodically. The preconfigured time period may be defined by a user. The user may be one of a system operator, a network operator, and the like. To collect the set of historical performance metrics data, the collecting unit

[0302] may query the plurality of database clusters.

[0086] The method

[0400] comprises further comprises preprocessing, by the processing unit

[0306] , the collected set of historical performance metrics data. In one example, the preprocessing includes but may not be limited to performing at least one of data cleaning, transformation, and filtering.

[0087] The method

[0400] further comprises selecting, by a selecting unit

[0312] , a set of key performance metrics from the set of historical performance metrics. In one example, the key performance metrics may be selected based on selection of a database from the plurality of database clusters that may be analysed. The set of key performance metrics comprises at least one of response time, throughput, error rate, and resource utilization.

[0088] Next at step

[0406] , the method comprises detecting, by a detecting unit

[0304] , one or more anomalies in the set of historical performance metrics data. In one example, the one or more anomalies may be one of an unusual pattern. The unusual pattern refers to a deviation from normal behaviour within the plurality of dataset. The unusual pattern may indicate at least one of a potential issue or an irregular behaviour in the plurality of database clusters. An example of the unusual pattern may be an unexpected increase or decrease in the CPU utilization or the network traffic.

[0089] The irregular behaviour includes but may not be limited to at least increase query latency, unbalanced load distribution, high resource utilization. The increased query latency refers to an increase in delay in the time taken by the database to process a received query and the delay in time to return results for the query. The unbalanced load distribution refers to an uneven load distribution of workload across nodes in the database cluster. The unbalanced load distribution may affect performance of the database. The high resource utilization refers to uneven resource utilization across the system where some resources are being highly consumed and other resources are not utilized.

[0090] In one example, the trained model may be used to detect the one or more anomalies. Based on the historical performance metrics data, the trained model stores a pattern, relationship, and other insights. The trained model is trained based on the set of historical performance metrics data.

[0091] Next at step

[0408] , the method comprises performing, by a processing unit

[0306] , a root cause analysis. The root cause analysis refers to a process to detect underlying issues in the one or more anomalies. In one example, the root cause analysis includes but may not be limited to at least one of examining logs, reviewing configuration changes, and investigating impact of external factors.

[0092] Next at step

[0410] , the method comprises generating, by a generating unit

[0308] , a report based on the root cause analysis. The generated report includes but may not be limited to a set of actionable recommendations for optimizing performance of plurality of database clusters. The set of actionable recommendations comprises at least recommendation for scaling size of the plurality of database clusters based on the detected one or more anomalies. The scaling comprises at least one of scale up and scale down. The scaling comprises at least one of scale up and scale down. The scale up refers to adding to more resources to existing resources. For instance, increasing the amount of memory for more memory usage, if the root cause analysis shows the causes of the oneor more anomalies to be due to high memory utilization. The scale down refers to removing resources from existing resources.

[0093] Next at step

[0412] , the method includes rendering, by a display unit

[0310] , the generated report. The display unit

[0310] may be a user interface (UI). The UI may be a graphical user interface (GUI). The GUI refers to an interface to interact with the system

[0300] by visual or graphical representation of icons, menu, etc. The GUI may be a smartphone, laptop, computer, etc.

[0094] The method terminates at step

[0014] ,

[0095] Referring to FIG. 5, an implementation of the system

[0500] for performing predictive analysis of a database cluster within a plurality of database clusters, in accordance with exemplary implementations of the present disclosure is shown. The implementation system

[0500] comprises a user interface (UI)

[0502] , a manager service

[0504] , a centralized data repository

[0506] , a database A service

[0508] , a database B service

[0510] , a database C service

[0512] , a database A cluster

[0514] , a database B cluster

[0516] and a database C cluster

[0518] ,

[0096] One or more users (user A, user B, user C) may use the user interface (UI)

[0502] to send the request for generating the report to obtain the set of actionable recommendations for optimizing performance of plurality of database cluster within one or more database clusters. In an example the one or more database clusters refers to the database A cluster

[0514] , the database B cluster

[0516] and the database C cluster

[0518] ,

[0097] The one or more users may configure the system to collect the set of historical performance metrics in the centralized data repository

[0506] at the preconfigured time period. The one or more users may send the set of historical performance metrics through the UI

[0502] , The one or more database clusters may be monitored by the one or more database services to collect the historical performance metrics.

[0098] The set of historical performance metrics may be normalized into a unified format by the system

[0300] , Further, the normalized data may be stored at the centralized data repository

[0506] by the system

[0300] ,

[0099] The one or more users may send the request for generating the report to obtain the set of actionable recommendations for optimizing performance of the plurality of database clusters via the UI

[0502] ,

[0100] Based on the request, the system

[0200] may retrieve the normalized historical performance metrics. The retrieved data may be analysed to detect one or more anomalies. The one or more anomalies may be detected based on the root cause analysis. The one or more anomalies corresponds to unusual pattern that indicate at least one of a potential issue or an irregular behaviour within the plurality of database clusters. The irregular behaviour comprises at least an increase query latency, unbalanced load distribution, high resource utilization.

[0101] The root cause analysis comprises at least one of examining logs, reviewing configuration changes, and investigating impact of external factors. Based on the root cause analysis, the report may be generated by the system

[0300] , The report may send the recommendation for action to be taken on the one or more anomalies. The recommendation may be one of a scale up or scale down of the plurality of database clusters.

[0102] Referring to FIG. 6, an implementation of the method

[0600] of collecting historic performance data for performing predictive analysis of database cluster within a database clusters, in accordance with exemplary implementations of the present disclosure is shown. In an implementation of the present disclosure, the implementation method

[0600] may be performed by the system

[0300] as shown in FIG. 3.

[0103] The implementation method

[0600] starts at step

[0602] , The user may configure the system

[0300] as shown in FIG. 3 to monitor the database.

[0104] At step

[0604] , the system

[0300] may try to create a connection with the database cluster.

[0105] In one example, if the connection is not established, the implementation method

[0600] may proceed to step

[0608] , where the system

[0300] may check configuration and retry to create the connection again.

[0106] At step

[0610] , the system

[0300] may further check if the retried connection is a success or a failure. If the connection is not established after retrying, the implementation method

[0600] mayfurther proceed to step

[0612] , where the system

[0300] may send an error to the system operator or the network operator.

[0107] If the connection is established at step

[0604] , the implementation method

[0600] may proceed to step

[0606] , At step

[0606] , the implementation method

[0600] includes collecting data associated with a set of historical performance metrics from a plurality of database clusters. The set of historical performance metrics data comprises at least one of the CPU utilisation, memory utilisation, network traffic, storage usage, and application log. The set of historical performance metrics data is collected at a preconfigured time period periodically.

[0108] Further, at step

[0614] , the implementation method

[0600] includes checking, if the collection of data is a success or a failure. In an event the collection of data is a failure, the system

[0300] may display an error to the system operator or the network operator at step

[0612] ,

[0109] In an event the collection of data is a success, the system

[0300] may normalize the collected data into a unified format at step

[0616] , The normalization may be performed using an algorithm.

[0110] Further at step

[0618] , the normalized data may be stores in the centralized data repository

[0506] ,[OHl] Further at step

[0620] , based on the request from the network operator or the system operator to perform predictive analysis of the data in the database, the normalized data may be sent from the centralized data repository

[0506] ,

[0112] Referring to FIG. 7, a second implementation of a method

[0700] for generation of a report for predictive analysis of database cluster within a database clusters, in accordance with exemplary implementations of the present disclosure is shown.

[0113] At step

[0702] , the user may send the request to the system

[0300] for generating the report to obtain the set of actionable recommendations for optimizing performance of plurality of database cluster. The user may send the request from the UI

[0502] ,

[0114] Further at step

[0704] , based on the request, the system

[0300] may query the centralized data repository

[0506] to check if the centralized data repository

[0506] is available.

[0115] If the centralized data repository

[0506] is not available, a service unavailable response may be sent by the system

[0300] at step

[0708] , Further, the system

[0300] may log an error for the centralized data repository

[0506] at step

[0710] ,

[0116] If the centralized data repository

[0506] is available, the implementation method

[0700] may proceed to step

[0706] , The data may be retrieved from the database service based on the request.

[0117] Further at step

[0712] , the system

[0300] may check if the data is retrieved successfully. In an event the data is not retrieved successfully, the implementation method

[0700] may proceed to step

[0710] where the system

[0300] may log an error.

[0118] In an event the data is retrieved successfully, the retrieved data may be analysed to detect one or more anomalies. The one or more anomalies may be detected based on the root cause analysis. The one or more anomalies corresponds to unusual pattern that indicate at least one of a potential issue or an irregular behaviour within the plurality of database clusters. The irregular behaviour comprises at least an increase query latency, unbalanced load distribution, high resource utilization.

[0119] The root cause analysis comprises at least one of examining logs, reviewing configuration changes, and investigating impact of external factors.

[0120] Based on the root cause analysis, the report may be generated by the system

[0300] and sent to the user via the UI

[0502] at step

[0714] , The report may send the recommendation for action to be taken on the one or more anomalies. The recommendation may be one of a scale up or scale down of the plurality of database clusters.

[0121] The present disclosure further discloses a non-transitory computer readable storage medium storing one or more instructions for performing predictive analysis of database cluster within a database clusters, the instructions include executable code which, when executed by one or more units of a system

[0300] , cause a collecting unit

[0302] to collect data associated with a set of historical performance metrics from a plurality of database clusters. The instructions when executed by the system

[0300] further cause a detecting unit

[0304] , using a trained model, to detect one or more anomalies in the set of historical performance metrics data. The instructions when executed by the system

[0300] further cause a processing unit

[0306] to perform a root cause analysis for the detected one or more anomalies. The root cause analysis comprises at least one ofexamining logs, reviewing configuration changes, and investigating impact of external factors. The instructions when executed by the system

[0300] further cause a generating unit

[0308] to generate a report based on the root cause analysis. The instructions when executed by the system

[0300] further cause a display unit

[0310] configured to render the generated report.

[0122] As is evident from the above, the present disclosure provides a technically advanced solution for performing predictive analysis of database cluster within a database cluster. The present solution provides a system and a method for gathering historical data from different regions of a network within the cluster, such as monitoring tools, logs, performance metrics, and resource usage statistics. The present disclosure is implemented in the 5G network, but may further be implemented in a 6thgeneration network or any other future generations of network. The present disclosure provides numerous functionality such as metric selection, reporting and recommendations, continuous learning, health and performance of database, to ensure the overall stability, availability, and performance of database. Further, the present disclosure performs anomaly detection, root cause analysis and predictive analysis to remove the anomalies.

[0123] 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.

[0124] Further, in accordance with the present disclosure, it is to be acknowledged that the functionality described for the various components / units can be implemented interchangeably. While specific embodiments may disclose a particular functionality of these units for clarity, it is recognized that various configurations and combinations thereof are within the scope of the disclosure. The functionality of specific units as disclosed in the disclosure should not be construed as limiting the scope of the present disclosure. Consequently, alternative arrangements and substitutions of units, provided they achieve the intended functionality described herein, are encompassed within the scope of the present disclosure.

Claims

We Claim:

1. A method [400] for performing predictive analysis of database cluster within a plurality of database clusters, the method [400] comprising: collecting, by a collecting unit [302], data associated with a set of historical performance metrics from the plurality of database clusters; detecting, by a detecting unit [304] using a trained model, one or more anomalies in the set of historical performance metrics data; performing, by a processing unit [306], a root cause analysis for the detected one or more anomalies; generating, by a generating unit [308], a report based on the root cause analysis; and rendering, by a display unit [310], the generated report.

2. The method [400] as claimed in claim 1, wherein the set of historical performance metrics data comprises at least one of central processing unit (CPU) utilisation, memory utilisation, network traffic, storage usage, and application log.

3. The method [400] as claimed in claim 1, wherein the method [400] comprises preprocessing, by the processing unit [306], the collected set of historical performance metrics data, wherein the preprocessing comprises performing at least one of data cleaning, transformation, and filtering.

4. The method [400] as claimed in claim 1, wherein the root cause analysis comprises at least one of examining logs, reviewing configuration changes, and investigating impact of external factors.

5. The method [400] as claimed in claim 1, wherein the one or more anomalies corresponds to unusual pattern that indicate at least one of a potential issue or an irregular behaviour within the plurality of database clusters, wherein the irregular behaviour comprises at least: increase query latency, unbalanced load distribution, high resource utilization.

6. The method [400] as claimed in claim 1, wherein the trained model is trained based on the set of historical performance metrics data.

7. The method [400] as claimed in claim 1, wherein the set of historical performance metrics data is collected at a preconfigured time period periodically.

8. The method [400] as claimed in claim 1, wherein the method [400] further comprises selecting, by a selecting unit [312], a set of key performance metrics from the set of historical performance metrics, wherein the selection of set of key performance metrics is based on a database of the plurality of database clusters being analysed for detecting the one or more anomalies.

9. The method [400] as claimed in claim 6, wherein the set of key performance metrics comprises at least one of response time, throughput, error rate, and resource utilization.

10. The method [400] as claimed in claim 1, wherein the generated report comprises a set of actionable recommendations for optimizing performance of plurality of database clusters, wherein the set of actionable recommendations comprises at least recommendation for scaling size of the plurality of database clusters based on the detected one or more anomalies.

11. The method [400] as claimed as claimed in claim 8, wherein the scaling comprises at least one of scale up and scale down.

12. A system [300] for performing predictive analysis of database cluster within a plurality of database clusters, the system [300] comprising: a collecting unit [302] configured to collect data associated with a set of historical performance metrics from the plurality of database clusters; a detecting unit [304] using a trained model configured to detect one or more anomalies in the set of historical performance metrics data; a processing unit [306] configured to perform a root cause analysis for the detected one or more anomalies; a generating unit [308] configured to generate a report based on the root cause analysis; and a display unit [310] configured to render the generated report.

13. The system [300] as claimed in claim 12, wherein the set of historical performance metrics data comprises at least one of central processing unit (CPU) utilisation, memory utilisation, network traffic, storage usage, and application log.

14. The system [300] as claimed in claim 12, wherein the processing unit [306] is further configured to preprocess the collected set of historical performance metrics data, wherein the preprocessing comprises performing at least one of data cleaning, transformation, and filtering.

15. The system [300] as claimed in claim 12, wherein the root cause analysis comprises at least one of examining logs, reviewing configuration changes, and investigating impact of external factors.

16. The system [300] as claimed in claim 12, the one or more anomalies corresponds to unusual pattern that indicate at least one of a potential issue or an irregular behaviour within the plurality of database clusters, wherein the irregular behaviour comprises at least: increase query latency, unbalanced load distribution, high resource utilization.

17. The system [300] as claimed in claim 12, wherein the trained model is trained based on the set of historical performance metrics data.

18. The system [300] as claimed in claim 13, wherein the set of historical performance metrics data is collected at a preconfigured time period periodically.

19. The system [300] as claimed in claim 12, wherein the system [300] further comprises a selecting unit [312] configured to select a set of key performance metrics from the set of historical performance metrics, wherein the selection of set of key performance metrics is based on a database of the plurality of database clusters being analysed for detecting the one or more anomalies.

20. The system [300] as claimed in claim 19, wherein the set of key performance metrics comprises at least one of response time, throughput, error rate, and resource utilization.

21. The system [300] as claimed in claim 12, wherein the generated report comprises a set of actionable recommendations for optimizing performance of plurality of database clusters, wherein the set of actionable recommendations comprises at least recommendation for scaling size of the plurality of database clusters based on the detected one or more anomalies.

22. The system [300] as claimed as claimed in claim 21, wherein the scaling comprises at least one of scale up and scale down.

23. A non-transitory computer-readable storage medium storing instruction for performing predictive analysis of database cluster within a plurality of database clusters, the storage medium comprising executable code which, when executed by one or more units of a system [300], causes: a collecting unit [302] to collect data associated with a set of historical performance metrics from the plurality of database clusters; a detecting unit [304] using a trained model to detect one or more anomalies in the set of historical performance metrics data, the one or more anomalies corresponds to unusual pattern that indicate at least one of a potential issue or an irregular behaviour within the plurality of database clusters; a processing unit [306] to perform a root cause analysis for the detected one or more anomalies; a generating unit [308] to generate a report based on the root cause analysis; and a display unit [310] to render the generated report.

Images