FORM 2
THE PATENTS ACT, 1970
(39 OF 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
“SYSTEM AND METHOD FOR CENTRALIZED MONITORING OF DATABASE ECOSYSTEMS IN A
NETWORK”
We, Jio Platforms Limited, an Indian National, of Office - 101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India.
The following specification particularly describes the invention and the manner in which it is to be performed.

SYSTEM AND METHOD FOR CENTRALIZED MONITORING OF DATABASE ECOSYSTEMS IN A NETWORK
FIELD OF INVENTION
[0001] Embodiments of the present disclosure generally relate to the field
of wireless communication systems. More particularly, embodiments of the present disclosure relates to a system and method for centralized monitoring of database ecosystems in a network.
BACKGROUND
[0002] The following description of related art is intended to provide
background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as an admissions of 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. 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] Existing solutions often focus on monitoring individual databases,
requiring administrators to use separate tools for each database. This results in an inefficient, siloed monitoring approach that hampers administrators' ability to gain a holistic view of the database landscape. Another drawback of the prior art is its resource intensity. Traditional monitoring tools often need to be installed directly on the database servers they are monitoring. This approach consumes valuable system resources, potentially leading to suboptimal database performance. It's like robbing Peter to pay Paul; in trying to monitor the health of a system, you might inadvertently compromise it. Additionally, the complexity introduced by using multiple tools is a significant challenge. Each tool comes with its own user interface, set of features, and configuration settings, leading to a steep learning curve for database administrators. This complexity not only takes up valuable time but also introduces a high probability of human error, particularly when configuring alerts, thresholds, and reporting settings across multiple platforms. The disparate nature of existing solutions also gives rise to monitoring gaps and incomplete coverage. Some database metrics or health indicators may be overlooked because administrators are juggling multiple tools that may not cover all the necessary parameters. This fragmented approach can delay the identification and resolution of critical issues, thus increasing the risk of downtime and other operational inefficiencies. Lastly, prior systems generally lack adaptability and consistency. They are not designed to dynamically adapt to changes in the database landscape, which is crucial in rapidly evolving technological environments. Furthermore, data collected by different tools is often formatted differently, complicating any efforts to aggregate this information into a coherent, comprehensive overview.
[0005] Thus, there exists an imperative need in the art for a system and
method for centralized monitoring of database ecosystems in a network, that aims to a more centralized, streamlined, and adaptable solution.
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
centralized monitoring of database ecosystems in a network. The method includes establishing, by a processing unit, secure connections of a centralized manager service with each of a plurality of database clusters across the network. Next, the method includes retrieving, by a retrieving unit, data associated with at least one of a set of performance metrics, a status update, and health data from each of the plurality of database clusters in real-time, the retrieved data is heterogeneously formatted. Next, the method includes preprocessing, by the processing unit, the retrieved heterogeneously formatted data to normalise the retrieved heterogeneously formatted data into a standardised format. Next, the method includes presenting, by a display unit, a unified dashboard interface that displays the at least one of the set of performance metrics, the status update, and the health data for each of the plurality of database clusters. Thereafter, the method includes generating, by a generating unit, an alert when the set of performance metrics breaches a predefined threshold.
[0008] In an exemplary aspect of the present disclosure, the method further
comprises retrieving data associated with the set of performance metrics comprises at least one of CPU usage, memory utilization, disk input/ Output (I/O) operations, and network latency.
[0009] In an exemplary aspect of the present disclosure, the unified
dashboard interface is configurable to display data visualizations, comprising at least one of graph, chart, and heatmap.
[00010] In an exemplary aspect of the present disclosure, the method further
comprises storing, by the processing unit, the normalised data in a central repository.

[00011] In an exemplary aspect of the present disclosure, the method further
comprises storing the normalized data in the central repository involves timestamping and indexing each data entry.
[00012] In an exemplary aspect of the present disclosure, the generated alert
is disseminated through one or more, and wherein the generated alert are prioritised according to severity.
[00013] In an exemplary aspect of the present disclosure, the method further
comprises identifying, by an identifying unit, a network topology of each of the plurality of database clusters.
[00014] In an exemplary aspect of the present disclosure, the method further
comprises adjusting, by the processing unit, the predefined thresholds based on a trained model that analyses the performance metrics over time, wherein the trained model is trained on a historical data associated with at least one of the set of performance metrics, the status update, and the health data of the plurality of database clusters.
[00015] In an exemplary aspect, the network is a 5G network, and wherein
the plurality of database clusters are associated with the 5G database ecosystem.
[00016] Another aspect of the present disclosure may relate to a system for
centralized monitoring of database ecosystems in a network. The system comprising a processing unit configured to establish secure connections of a centralized manager service with each of a plurality of database clusters across the network. The system further comprises a retrieving unit configured to retrieve data associated with at least one of a set of performance metrics, a status update, and health data from each of the plurality of database clusters in real¬time, the retrieved data is heterogeneously formatted. The system further comprises the processing unit configured to preprocess the retrieved heterogeneously formatted data to normalise the retrieved heterogeneously formatted data into a standardised format. The system further comprises a display unit configured to present a unified dashboard interface that displays the at least one of the set of performance metrics, the status update, and the health

data for each of the plurality of database clusters. The system further comprises a generating unit configured to generate an alert when the set of performance metrics breaches a predefined threshold.
5 [00017] Yet another aspect of the present disclosure may relate to a non-
transitory computer readable storage medium storing instructions for centralized monitoring of database ecosystems in a network, the instructions include executable code which, when executed by one or more units of a system, causes: a processing unit of the system to establish secure connections of a centralized
10 manager service with each of a plurality of database clusters across the network;
a retrieving unit of the system to retrieve data associated with at least one of a set of performance metrics, a status update, and health data from each of the plurality of database clusters in real-time, the retrieved data is heterogeneously formatted; the processing unit of the system to preprocess the retrieved
15 heterogeneously formatted data to normalise the retrieved heterogeneously
formatted data into a standardised format; a display unit of the system to present a unified dashboard interface that displays the at least one of the set of performance metrics, the status update, and the health data for each of the plurality of database clusters; and a generating unit of the system to generate an
20 alert when the set of performance metrics breaches a predefined threshold.
OBJECTS OF THE INVENTION
[00018] Some of the objects of the present disclosure, which at least one
25 embodiment disclosed herein satisfies are listed herein below.
[00019] It is an object of the present disclosure to provide a system and
method that provides a system and method for centralized monitoring of 5G database ecosystems. 30
[00020] It is another object of the present disclosure to provide a system and
method for centralized monitoring of 5G database ecosystems that centralizes
6

the monitoring of multiple databases, particularly within the 5G Core and 5G Cloud ecosystems.
[00021] It is yet another object of the present disclosure to provide a system
5 and method for centralized monitoring of 5G database ecosystems that optimizes
resource utilization by running a centralized monitoring service remotely, thereby reducing the computational load on individual database servers.
[00022] It is yet another object of the present disclosure to provide a system
10 and method for centralized monitoring of 5G database ecosystems that simplifies
the user experience by consolidating multiple monitoring tools into a single, user-friendly interface.
[00023] It is yet another object of the present disclosure to provide a system
15 and method for centralized monitoring of 5G database ecosystems that closes
monitoring gaps by offering comprehensive coverage of database performance metrics and health indicators.
[00024] It is yet another object of the present disclosure to provide a system
20 and method for centralized monitoring of 5G database ecosystems that expedites
issue identification and resolution through real-time alerting and data aggregation.
[00025] It is yet another object of the present disclosure to provide a system
25 and method for centralized monitoring of 5G database ecosystems that
minimizes configuration overhead by enabling administrators to set alerts, define thresholds, and configure reporting settings through a unified dashboard interface.
30 [00026] It is yet another object of the present disclosure to provide a system
and method that adapt dynamically to changes in the database landscape, thus ensuring continued efficiency and relevance in monitoring practices.
7

[00027] It is yet another object of the present disclosure to provide a system
and method that standardizes the data collected from various databases into a unified format for easier aggregation and analysis.
5 [00028] It is yet another object of the present disclosure to provide a system
and method for centralized monitoring of 5G database ecosystems that streamlines the management of alerts and reports by consolidating them into a centralized dashboard.
10 [00029] It is yet another object of the present disclosure to provide a system
and method for centralized monitoring of 5G database ecosystems that enhances the administrators' ability to make informed decisions by offering insightful visualizations of database performance metrics.
15 DESCRIPTION OF THE DRAWINGS
[00030] 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
20 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
25 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.
[00031] FIG. 1 illustrates an exemplary block diagram of a computing device
30 upon which the features of the present disclosure may be implemented in
accordance with exemplary implementation of the present disclosure.
8

[00032] FIG. 2 illustrates an exemplary block diagram of a system for
centralized monitoring of database ecosystems in a network, in accordance with exemplary implementations of the present disclosure.
5 [00033] FIG. 3 illustrates a method flow diagram for centralized monitoring
of database ecosystems in a network, in accordance with exemplary implementations of the present disclosure.
[00034] FIG. 4 illustrates an exemplary block diagram of a system for
10 centralized monitoring of multiple databases in 5G ecosystems, in accordance
with exemplary implementations of the present disclosure.
[00035] FIG. 5 an exemplary method flow diagram for centralized
monitoring of multiple databases in 5G ecosystems, in accordance with
15 exemplary implementations of the present disclosure.
[00036] The foregoing shall be more apparent from the following more
detailed description of the disclosure.
20 DETAILED DESCRIPTION
[00037] 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
25 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.
30
[00038] 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
9

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.
5 [00039] 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 skills 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
10 obscure the embodiments in unnecessary detail.
[00040] 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
15 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.
20 [00041] 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
25 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
30 word—without precluding any additional or other elements.
[00042] As used herein, a “processing unit” or “processor” or “operating
processor” includes one or more processors, wherein processor refers to any
10

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
5 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
10 processor.
[00043] As used herein, “a user equipment”, “a user device”, “a smart-user-
device”, “a smart-device”, “an electronic device”, “a mobile device”, “a handheld device”, “a wireless communication device”, “a mobile
15 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
20 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.
25
[00044] 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
30 (“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.
11

[00045] 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
5 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.
[00046] All modules, units, components used herein, unless explicitly
10 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),
15 Field Programmable Gate Array circuits (FPGA), any other type of integrated
circuits, etc.
[00047] As used herein the transceiver unit include at least one receiver and
at least one transmitter configured respectively for receiving and transmitting
20 data, signals, information or a combination thereof between units/components
within the system and/or connected with the system.
[00048] As discussed in the background section, existing solutions often
focus on monitoring individual databases, requiring administrators to use
25 separate tools for each database. This results in an inefficient, siloed monitoring
approach that hampers administrators' ability to gain a holistic view of the database landscape. Another drawback of the prior art is its resource intensity. Traditional monitoring tools often need to be installed directly on the database servers they are monitoring. This approach consumes valuable system resources,
30 potentially leading to suboptimal database performance. It's like robbing Peter
to pay Paul; in trying to monitor the health of a system, you might inadvertently compromise it. Additionally, the complexity introduced by using multiple tools is a significant challenge. Each tool comes with its own user interface, set of
12

features, and configuration settings, leading to a steep learning curve for
database administrators. This complexity not only takes up valuable time but
also introduces a high probability of human error, particularly when configuring
alerts, thresholds, and reporting settings across multiple platforms. The disparate
5 nature of existing solutions also gives rise to monitoring gaps and incomplete
coverage. Some database metrics or health indicators may be overlooked because administrators are juggling multiple tools that may not cover all the necessary parameters. This fragmented approach can delay the identification and resolution of critical issues, thus increasing the risk of downtime and other
10 operational inefficiencies. Lastly, prior systems generally lack adaptability and
consistency. They are not designed to dynamically adapt to changes in the database landscape, which is crucial in rapidly evolving technological environments. Furthermore, data collected by different tools is often formatted differently, complicating any efforts to aggregate this information into a
15 coherent, comprehensive overview.
[00049] The present disclosure aims to overcome the above-mentioned and
other existing problems in this field of technology by providing a method and system for centralized monitoring of database ecosystems in a network.
20
[00050] FIG. 1 illustrates an exemplary block diagram of a computing device
[100] (also referred herein as a computer system [100]) 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
25 device [100] may also implement a method for centralized monitoring of
database ecosystems in a network, utilising the system. In another implementation, the computing device [100] itself implements the method for centralized monitoring of database ecosystems in a network, using one or more units configured within the computing device [100], wherein said one or more
30 units are capable of implementing the features as disclosed in the present
disclosure.
[00051] The computing device [100] may include a bus [102] or other
communication mechanism for communicating information, and a processor
13

[104] coupled with bus [102] for processing information. The processor [104]
may be, for example, a general purpose microprocessor. The computing device
[100] may also include a main memory [106], such as a random access memory
(RAM), or other dynamic storage device, coupled to the bus [102] for storing
5 information and instructions to be executed by the processor [104]. The main
memory [106] also may be used for storing temporary variables or other
intermediate information during execution of the instructions to be executed by
the processor [104]. Such instructions, when stored in non-transitory storage
media accessible to the processor [104], render the computing device [100] into
10 a special-purpose machine that is customized to perform the operations specified
in the instructions. The computing device [100] further includes a read only memory (ROM) [108] or other static storage device coupled to the bus [102] for storing static information and instructions for the processor [104].
15 [00052] A storage device [110], such as a magnetic disk, optical disk, or
solid-state drive is provided and coupled to the bus [102] for storing information and instructions. The computing device [100] may be coupled via the bus [102] to a display [112], such as a cathode ray tube (CRT), Liquid crystal Display (LCD), Light Emitting Diode (LED) display, Organic LED (OLED) display, etc.
20 for displaying information to a computer user. An input device [114], including
alphanumeric and other keys, touch screen input means, etc. may be coupled to the bus [102] for communicating information and command selections to the processor [104]. Another type of user input device may be a cursor controller [116], such as a mouse, a trackball, or cursor direction keys, for communicating
25 direction information and command selections to the processor [104], and for
controlling cursor movement on the display [112]. 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.
30 [00053] The computing device [100] 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 [100] causes or programs the computing device [100] to be a
14

special-purpose machine. According to one implementation, the techniques
herein are performed by the computing device [100] in response to the processor
[104] executing one or more sequences of one or more instructions contained in
the main memory [106]. Such instructions may be read into the main memory
5 [106] from another storage medium, such as the storage device [110]. Execution
of the sequences of instructions contained in the main memory [106] causes the processor [104] 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.
10
[00054] The computing device [100] also may include a communication
interface [118] coupled to the bus [102]. The communication interface [118] provides a two-way data communication coupling to a network link [120] that is connected to a local network [122]. For example, the communication interface
15 [118] 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 [118] may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be
20 implemented. In any such implementation, the communication interface [118]
sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
[00055] The computing device [100] can send messages and receive data,
25 including program code, through the network(s), the network link [120] and the
communication interface [118]. In the Internet example, a server [130] might
transmit a requested code for an application program through the Internet [128],
the ISP [126], the local network [122], host [124] and the communication
interface [118]. The received code may be executed by the processor [104] as it
30 is received, and/or stored in the storage device [110], or other non-volatile
storage for later execution.
15

[00056] The computing device [100] encompasses a wide range of electronic
devices capable of processing data and performing computations. Examples of
computing devices [100] include, but are not limited only to, personal
computers, laptops, tablets, smartphones, servers, and embedded systems. The
5 devices may operate independently or as part of a network and can perform a
variety of tasks such as data storage, retrieval, and analysis. Additionally,
computing device [100] may include peripheral devices, such as monitors,
keyboards, and printers, as well as integrated components within larger
electronic systems, showcasing their versatility in various technological
10 applications.
[00057] Referring to FIG. 2, an exemplary block diagram of a system [200]
for centralized monitoring of database ecosystems in a network, is shown, in accordance with the exemplary implementations of the present disclosure. The
15 system [200] comprises at least one processing unit [202], at least one retrieving
unit [204], at least one display unit [206], at least one generation unit [208] and at least one identifying unit [210]. Also, all of the components/ units of the system [200] are assumed to be connected to each other unless otherwise indicated below. As shown in the figures all units shown within the system
20 should also be assumed to be connected to each other. Also, in FIG. 2 only a few
units are shown, however, the system [200] may comprise multiple such units or the system [200] may comprise any such numbers of said units, as required to implement the features of the present disclosure. Further, in an implementation, the system [200] may be present in a user device to implement the features of
25 the present disclosure. The system [200] 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 [200] may reside in a server or a network entity. In yet another implementation, the system [200] may reside partly in the server/ network entity and partly in the user device.
30
[00058] The system [200] is configured for centralized monitoring of
database ecosystems in a network, with the help of the interconnection between the components/units of the system [200].
16

[00059] The system [200] comprises a processing unit [202]. The processing
unit [202] is configured to establish secure connections of a centralized manager
service with each of a plurality of database clusters across the network. In an
5 implementation, the system [200] performs centralized monitoring of plurality
of database clusters across the network, such as, but not limited to 5G network and 6G network. The centralized manager service facilitates a single interface for centralized monitoring of the plurality of database clusters across the network. The processing unit [202] of the system [200] is configured to establish
10 secure connections of the centralized manager service with each of the plurality
of database clusters across the network. In an implementation, the secure connections may be established using such as, but not limited to, Secure Sockets Layer (SSL) based connections, encrypted connections and password protected connections. The database clusters may have the plurality of databases
15 associated with such as, but not limited to, 5G core network and/or 5G cloud
network. The database may be such as, but not limited to, redis. In an implementation, the centralized manager service may monitor the plurality of database clusters, server clusters, or network components remotely. In an implementation, the centralized manager service may monitor different types
20 (e.g., data types, structures and format) of database clusters and server clusters.
[00060] The system [200] comprises a retrieving unit [204]. The retrieving
unit [204] is configured to retrieve data associated with at least one of a set of performance metrics, a status update, and health data from each of the plurality
25 of database clusters in real-time, the retrieved data is heterogeneously formatted.
The processing unit [202] is communicatively coupled with the retrieving unit [204]. After establishing the secure connections of the centralized manager service with each of the plurality of database clusters via the processing unit [202], the retrieving unit [204] is configured to retrieve data associated with the
30 set of performance metrics, such as, but not limited to, at least one of CPU usage,
memory utilization, disk input/ Output (I/O) operations, and network latency in real-time. In an exemplary aspect, the retrieving unit [204] is configured to retrieve data associated with each of the plurality of database clusters, such as, failure, inactive databases, availability of the databases. The retrieved data is
17

heterogeneously formatted. The heterogeneous format of data may be associated
with such as, but not limited to, dataset composed of different data types,
structured/unstructured, formats, sources, network model or vendor’s specific
format.
5 [00061] In an exemplary aspect, health data defines the efficiency of the
database cluster. In an example, if the efficiency of the database cluster is greater
than predetermined threshold then it may suggest that the health of said database
cluster is positive. In another example, if the efficiency of the database cluster is
below the predetermined level then it may suggest that the health of said database
10 is poor.
[00062] The system [200] comprises the processing unit [202]. The
processing unit [202] is configured to preprocess the retrieved heterogeneously formatted data to normalise the retrieved heterogeneously formatted data into a
15 standardised format. After retrieving data associated with the plurality of
database clusters, the processing unit [202] is configured to preprocess the retrieved heterogeneously formatted data to normalise the retrieved heterogeneously formatted data into a standardised format. The processing unit [202] is configured to store the normalised data in a central repository. The
20 processing unit [202] is configured to store the normalized data in the central
repository with timestamping and indexing each data entry. The central repository acts as a centralized storage for all the performance metrics and information gathered from the plurality of database clusters. In an exemplary aspect, the processing unit [202] may normalize data to database intelligence
25 quotient (DBIQ’)s standard format to ensure consistency and compatibility
across the plurality of database clusters. The processing unit [202] may facilitate the data conversion into the standardized format, regardless of the specific database technology being used.
30 [00063] In an implementation, the system [200] comprises an identifying
unit [210]. The identifying unit [210] is configured to identify a network topology of each of the plurality of database clusters. The topology may refer to connections and relationships between the databases in a cluster or between different clusters in the network. The identifying unit [210] may share
18

information of network topology of database clusters with the processing unit [202] for further processing.
[00064] The system [200] comprises a display unit [206]. The display unit
5 [206] is configured to present a unified dashboard interface that displays the at
least one of the set of performance metrics, the status update, and the health data for each of the plurality of database clusters. The display unit [206] of the system [200] is configured to display the unified dashboard interface. The unified dashboard interface is configurable to display data visualizations, such as, but
10 not limited to, at least one of graph, chart, and heatmap for each of the plurality
of database clusters. The display unit [206] may be associated with a mobile device, user device, computing device or human machine interface (HMI). The unified dashboard interface provides real-time metrics, alerts, and health statuses, offering a comprehensive and centralized overview for the monitored
15 database clusters. Through this, the network administrator of the network may
gain a better understanding of database clusters performance and take decisions
for scaling the clusters or any modifications. In an implementation, the
identifying unit [210] may communicate with the display unit [206] to show the
connections and relationships between databases and associated clusters.
20
[00065] The system [200] comprises a generating unit [208]. The generating
unit [208] is configured to generate an alert when the set of performance metrics
breaches a predefined threshold. The processing unit [202] is communicatively
coupled with the generating unit [208]. The processing unit [202] may trigger
25 the generating unit [208] for generating the alert when the set of performance
metrics breaches a predefined threshold. The network administrator may predefine threshold such as, but not limited to, CPU capacity for serving the network traffic. The predefined threshold are customizable and flexible as per network operational needs. Further, as per network traffic patterns or trends, the
30 processing unit [202] is configured to adjust the customizable thresholds based
on a trained model that analyses the performance metrics over time. In an implementation, the network administrator may also customize the predefined threshold. In an implementation, the trained model is trained on a historical data associated with at least one of the set of performance metrics, the status update,
19

and the health data of the plurality of database clusters. In an implementation,
the trained model may be such as, but not limited only to artificial intelligence /
machine learning based models. The trained model is trained on historical data
using AI/ML based models in order to check the recurring trends or patterns such
5 that to analyse the performance metrics over a period of time. In an
implementation, the generating unit [208] may generate alert based on priority and critical issues. The generated alert is disseminated through one or more channels comprising at least one of an email, and an SMS to the network administrator or authorised user. 10
[00066] In an exemplary aspect, one or more AI/ML based models may be
used which includes such as but not limited to neural network based model, decision tree based model etc.
15 [00067] 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.
20 The functionality of specific units as disclosed in the disclosure should not be
construed as limiting the scope of the present disclosure. Consequently, alternative arrangements and substitutions of units, provided they achieve the intended functionality described herein, are considered to be encompassed within the scope of the present disclosure.
25
[00068] Referring to FIG. 3, an exemplary method flow diagram [300] for
centralized monitoring of database ecosystems in a network, in accordance with exemplary implementations of the present disclosure is shown. In an implementation the method [300] is performed by the system [200]. Further, in
30 an implementation, the system [200] may be present in a server device to
implement the features of the present disclosure. Also, as shown in FIG. 3 the method [300] starts at step [302].
20

[00069] At step 304, the method [300] of the present disclosure comprises
establishing, by a processing unit [202], secure connections of a centralized
manager service with each of a plurality of database clusters across the network.
The method [300] implemented by the system [200] performs the centralized
5 monitoring of plurality of database clusters across the network, such as, but not
limited to 5G network and 6G network. The centralized manager service facilitates a single interface for centralized monitoring of the plurality of database clusters across the network. The processing unit [202] of the system [200] may establish secure connections of the centralized manager service with
10 each of the plurality of database clusters across the network. In an
implementation, the secure connections may be established using such as, but not limited to, Secure Sockets Layer (SSL) based connections, encrypted connections and password protected connections. The database clusters may have the plurality of databases associated with such as, but not limited to, 5G
15 core network and/or 5G cloud network. The database may be such as, but not
limited to, Redis database. In an implementation, the centralized manager service may monitor the plurality of database clusters, server clusters, or network components remotely. In an implementation, the centralized manager service may monitor different types (e.g., data types, structures and format) of database
20 clusters and server clusters.
[00070] Next, at step 306, the method [300] of the present disclosure
comprises retrieving, by a retrieving unit [204], data associated with at least one of a set of performance metrics, a status update, and health data from each of the
25 plurality of database clusters in real-time, the retrieved data is heterogeneously
formatted. After establishing the secure connections of the centralized manager service with each of the plurality of database clusters via the processing unit [202], the retrieving unit [204] may retrieve data associated with the set of performance metrics, such as, but not limited to, at least one of CPU usage,
30 memory utilization, disk input/ Output (I/O) operations, and network latency in
real-time. In an exemplary aspect, the retrieving unit [204] may retrieve data associated with each of the plurality of database clusters, such as, failure, inactive databases, availability of the databases. The retrieved data is
21

heterogeneously formatted. The heterogeneous format of data may be associated with such as, but not limited to, dataset composed of different data types, structured/unstructured, formats, sources, network model or vendor’s specific format. 5
[00071] Next, at step 308, the method [300] of the present disclosure
comprises preprocessing, by the processing unit [202], the retrieved heterogeneously formatted data to normalise the retrieved heterogeneously formatted data into a standardised format. After retrieving data associated with
10 the plurality of database clusters, the processing unit [202] may preprocess the
retrieved heterogeneously formatted data to normalise the retrieved heterogeneously formatted data into a standardised format. In an exemplary aspect, the heterogenous format data includes data received from one or more vendors having different formats which makes it difficult for the network
15 administrator to monitor the health of the database cluster as he/she has to use
different tools for different formats. By aggregating the heterogenous data into a standard format, the network operator may easily monitor the health of the database cluster. The processing unit [202] may store the normalised data in a central repository. The processing unit [202] may store the normalized data in
20 the central repository with timestamping and indexing each data entry. The
central repository acts as a centralized storage for all the performance metrics and information gathered from the plurality of database clusters. In an exemplary aspect, the processing unit [202] may normalize data to database intelligence quotient (DBIQ’s) standard format to ensure consistency and compatibility
25 across the plurality of database clusters. The processing unit [202] may facilitate
the data conversion into the standardized format, regardless of the specific database technology being used.
[00072] In an implementation, the system [200] comprises an identifying
30 unit [210]. The identifying unit [210] may identify a network topology of each
of the plurality of database clusters. The topology may refer to connections and relationships between the databases in a cluster or between different clusters in the network. The identifying unit [210] may share information of network
22

topology of database clusters with the processing unit [202] for further
processing. In an exemplary aspect, the identified network topology facilitates
in providing a comprehensive map of how various database clusters are
interconnected within the 5G Core and 5G Cloud ecosystem. The topology helps
5 the centralized monitoring tool to identify the structure and relationships
between the clusters, ensuring efficient data collection and communication.
Understanding the topology enables the system to adjust monitoring strategies
dynamically, optimize data flow, and avoid bottlenecks, ensuring a seamless
collection of performance metrics, status updates, and health data from all
10 databases. Additionally, it facilitates intelligent routing of alerts and optimized
resource usage, which enhances the overall efficiency of the monitoring process and improves issue detection across the entire network.
[00073] Next, at step 310, the method [300] of the present disclosure
15 comprises presenting, by a display unit [206], a unified dashboard interface that
displays the at least one of the set of performance metrics, the status update, and the health data for each of the plurality of database clusters. The display unit [206] of the system [200] may display the unified dashboard interface. The unified dashboard interface may be configurable to display data visualizations,
20 such as, but not limited to, at least one of graph, chart, and heatmap for each of
the plurality of database clusters. The display unit [206] may be associated with a mobile device, user device, computing device or human machine interface (HMI). The unified dashboard interface provides real-time metrics, alerts, and health statuses, offering a comprehensive and centralized overview for the
25 monitored database clusters. Through this, the network administrator of the
network may gain a better understanding of database clusters performance and take decision for scaling the clusters or any modifications. In an implementation, the identifying unit [210] may communicate with the display unit [206] to show the connections and relationships between databases and associated clusters.
30
[00074] Next, at step 312, the method [300] of the present disclosure
comprises generating, by a generating unit [208], an alert when the set of performance metrics breaches a predefined threshold. The processing unit [202]
23

is communicatively coupled with the generating unit [208]. The processing unit
[202] may trigger the generating unit [208] for generating the alert when the set
of performance metrics breaches a predefined threshold. The network
administrator may predefine threshold such as, but not limited to, CPU capacity
5 for serving the network traffic. The predefined threshold are customizable and
flexible as per network operational needs. Further, as per network traffic patterns or trends, the processing unit [202] may adjust the customizable thresholds based on a trained model that analyses the performance metrics over time. In an implementation, the network administrator may also customize the predefined
10 threshold. In an implementation, the trained model is trained on a historical data
associated with at least one of the set of performance metrics, the status update, and the health data of the plurality of database clusters. In an implementation, the trained model may be such as, but not limited only to artificial intelligence / machine learning based models. The trained model is trained on historical data
15 using AI/ML based models in order to check the recurring trends or patterns such
that to analyse the performance metrics over a period of time. In an implementation, the generating unit [208] may generate alert based on priority and critical issues. The generated alert is disseminated through one or more channels comprising at least one of an email, and an SMS to the network
20 administrator or authorised user.
[00075] In an exemplary aspect, one or more AI/ML based models may be
used which includes such as but not limited to neural network based model, decision tree based model etc. 25
[00076] Thereafter, the method [300] terminates at step [314].
[00077] Referring to FIG. 4, an exemplary block diagram of a system
architecture [400] for centralized database monitoring is shown, in accordance
30 with the exemplary implementations of the present disclosure. The system
architecture [400] illustrates at least one user [402], at least one user interface [404], at least one manager service [406], at least one central data repository [408], at least one database A service [410A], at least one database B service
24

[410B], at least one database C service [410C], at least one database A clusters [412A], at least one database B clusters [412B], and at least one database C clusters [412C].
5 [00078] The user [402] interacts with the system through the user interface
[404], which provides a unified dashboard to monitor the health, performance
metrics, and status updates of multiple databases. The manager service [406]
serves as a centralized controller, establishing secure connections with the
database A clusters [412A], the database B clusters [412B], and the database C
10 clusters [412C], corresponding to different types of databases within the 5G Core
and Cloud ecosystem.
[00079] Each database service, including database A service [410A],
database B service [410B], and database C service [410C], retrieves data from
15 their respective database A clusters [412A], database B clusters [412B], and
database C clusters [412C]. The data collected, which includes performance metrics and health statuses, is sent to the central data repository [408], where it is normalized into a standard format. This repository acts as the centralized storage hub for all data retrieved from the different databases, allowing for
20 consistent analysis and reporting.
[00080] The user interface [404] then presents the collected data in a
visualized form, including graphs and alerts, enabling administrators to monitor
trends and anomalies across the entire database ecosystem. In this architecture,
25 the system ensures streamlined monitoring across heterogeneous database
clusters through the centralization of data collection, analysis, and alert generation.
[00081] For example, in a telecommunications company operating a 5G
30 network, monitoring multiple databases becomes critical for ensuring optimal
performance and reliability. In such a scenario, a centralized monitoring service can be employed to manage diverse databases. This service performs several key functions. First, it continuously collects real-time metrics such as CPU usage and
25

memory utilization from each database. Second, it standardizes these metrics
into a common format like JSON for uniformity. This standardized data is then
stored in a high-availability cloud repository for easy access and analysis.
Administrators use a unified dashboard to view these real-time metrics from all
5 databases, presented graphically for quick and easy interpretation. Customizable
alert thresholds for each database type can be set directly from this unified
dashboard. The service constantly compares the real-time data against these
thresholds, generating alerts when any metric exceeds its limit. These alerts are
displayed on the dashboard and can also be sent via email or SMS, allowing for
10 quick and decisive action. Overall, this centralized approach streamlines the
monitoring process, making it more efficient and effective for managing multiple databases in a complex 5G network environment.
[00082] Referring to FIG. 5, an exemplary process [500] for centralized
15 monitoring of database ecosystems in accordance with exemplary
implementations of the present disclosure is shown. The process [500] is performed by a system architecture configured to monitor and manage a plurality of database clusters.
20 [00083] At step 502, the process [500] comprises configuring the service to
monitor a database by setting up the necessary parameters, thresholds, and settings for the centralized monitoring tool. This step includes defining which database clusters to monitor and configuring the rules for alert generation and data collection.
25
[00084] At step 504, the process [500] involves creating secure connections
with the database clusters across the network. The processing unit establishes these connections to allow real-time data retrieval from the databases. This ensures that the centralized service can access the necessary performance
30 metrics, health data, and status updates from each database.
[00085] At step 506, the process [500] starts collecting data from the
databases. The retrieving unit fetches various performance metrics such as CPU
26

usage, memory utilization, disk I/O operations, and network latency. The data is retrieved in heterogeneous formats, depending on the specific database technologies.
5 [00086] At step 508, the process [500] successfully receives the data from
the connected database clusters. The system ensures that the data has been correctly fetched and prepares it for further processing, confirming successful communication with the databases.
10 [00087] At step 510, the process [500] checks the configuration and retries
the connection if necessary. If there are any issues with the connection or data retrieval, the system automatically adjusts the settings or retries the connection to ensure consistent monitoring of the database clusters.
15 [00088] At step 512, the process [500] verifies whether the connection has
been successfully established with the database clusters. If the connection is stable, the system proceeds with the monitoring and data collection; otherwise, it attempts to resolve the issue.
20 [00089] At step 514, the process [500] logs an error if the connection to the
database clusters cannot be established. This step ensures that any issues with the connection are documented for troubleshooting and further investigation.
[00090] At step 516, the process [500] checks whether the database cluster
25 is connected. This verification is critical to ensure that the monitoring service
can continue to collect real-time data. If the cluster is disconnected, the system will trigger an alert and attempt to reconnect.
[00091] At step 518, the process [500] checks other configurations and
30 retries collecting data from the database clusters. This step ensures that if the
initial configuration or data retrieval fails, the system will reattempt the process with adjusted parameters.
27

[00092] At step 520, the process [500] normalizes the data into a unified
format using a set of predefined steps. This step facilitates that the data retrieved from different database clusters is standardized, making it compatible for centralized analysis and monitoring. 5
[00093] At step 522, the process [500] stores the normalized data into the
central repository. The processing unit timestamps and indexes the data to allow for easy retrieval and historical analysis.
10 [00094] At step 524, on user request, the system serves the user with the
requested data from the central repository. The display unit presents the
requested performance metrics, health data, and status updates through a unified
dashboard providing the user with comprehensive insights into the database
ecosystem.
15
[00095] Referring to FIG. 6, an exemplary process [600] for centralized
monitoring of database ecosystems in accordance with exemplary
implementations of the present disclosure is shown. The process [600] outlines
how user requests are processed and how data is retrieved from a centralized
20 repository to be displayed on the user interface.
[00096] At step [602] of the process [600], the user accesses the tool and
requests data. The user interacts with the user interface, requesting performance
metrics, health statuses, or other relevant data from the centralized system. The
25 tool allows users to configure customizable thresholds for alerts and specify the
database clusters to be monitored.
[00097] At step [604] of the process [600], the system checks if the database
service is available. The processing unit [202] verifies that the necessary
30 database clusters are online and ready to provide the requested data. This ensures
that the system is able to retrieve real-time performance metrics from the database clusters.
28

[00098] At step [606] of the process [600], if the database service is
unavailable, the system sends a "service unavailable" response and logs the error. The system records any issues related to database availability, ensuring that administrators can troubleshoot and resolve the problem quickly. 5
[00099] At step [608] of the process [600], the request is forwarded to the
respective database service to collect the required data. The retrieving unit [204]
collects data associated with performance metrics, status updates, and health data
from the relevant database clusters. This data is retrieved in heterogeneous
10 formats and is later normalized for centralized analysis.
[000100] At step [610] of the process [600], the system checks if the data has
been successfully retrieved from the repository. If the data has been collected
successfully, it is prepared for display on the unified dashboard. If not, the
15 system proceeds to log the error and notify the user.
[000101] At step [612] of the process [600], if data retrieval from the
repository fails, the system sends a "service unavailable" response and logs the
error. This ensures that any retrieval issues are documented for further
20 investigation.
[000102] At step [614], the system sends the retrieved data and displays it to
the user interface. The display unit [206] presents the requested data in various
visual formats, such as graphs, charts, and heatmaps, allowing the user to
25 monitor real-time performance metrics and health statuses for the database
clusters. The unified dashboard provides a comprehensive view of all monitored databases, enabling quick detection of issues.
[000103] As is evident from the above, the present disclosure provides a
30 technically advanced solution for centralized monitoring of database ecosystems
in a network. The present solution provides with a centralized uniform holistic view of the database clusters deployed within 5G ecosystem, enabling to monitor all databases from a single interface. This comprehensive visibility simplifies the
29

monitoring process and enhances the ability to identify trends, anomalies, and
performance issues across the infrastructure. The present solution simplifies the
task of users by allowing them to monitor the health, performance, and status of
all databases through a single tool. This unified view enables quick detection of
5 issues and proactive troubleshooting actions to ensure smooth database
operations while reducing monitoring gaps. The present solution enables a uniform centralized monitoring tool to effortlessly connect with a diverse array of database clusters within the 5G Core and 5G Cloud architecture, facilitating remote seamless monitoring of database clusters. Database administrators can
10 attain proficiency with this tool more rapidly as they only need to familiarize
themselves with a single interface. This diminishes the learning curve typically linked to using various monitoring tools, consequently increasing overall productivity. The present solution enables the consolidation of alerts and reports from numerous databases, streamlining the management of alerts and reports.
15 Administrators can prioritize alerts according to their severity and respond
promptly, ensuring that critical matters are promptly addressed.
[000104] According to an aspect, the present disclosure provides a non-transitory computer readable storage medium storing instructions for centralized
20 monitoring of database ecosystems in a network, the instructions include
executable code which, when executed by one or more units of a system, causes: a processing unit of the system to establish secure connections of a centralized manager service with each of a plurality of database clusters across the network; a retrieving unit of the system to retrieve data associated with at least one of a
25 set of performance metrics, a status update, and health data from each of the
plurality of database clusters in real-time, the retrieved data is heterogeneously formatted; the processing unit of the system to preprocess the retrieved heterogeneously formatted data to normalise the retrieved heterogeneously formatted data into a standardised format; a display unit of the system to present
30 a unified dashboard interface that displays the at least one of the set of
performance metrics, the status update, and the health data for each of the plurality of database clusters; and a generating unit of the system to generate an alert when the set of performance metrics breaches a predefined threshold.
30

[000105] While considerable emphasis has been placed herein on the disclosed embodiments, it will be appreciated that many embodiments can be made and that many changes can be made to the embodiments without departing from the principles of the present disclosure. These and other changes in the embodiments 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.

I/We Claim:
1. A method for centralized monitoring of database ecosystems in a network,
said method comprising:
- establishing, by a processing unit [202], secure connections of a centralized manager service with each of a plurality of database clusters across the network;
- retrieving, by a retrieving unit [204], data associated with at least one of a set of performance metrics, a status update, and health data from each of the plurality of database clusters in real-time, the retrieved data is heterogeneously formatted;
- preprocessing, by the processing unit [202], the retrieved heterogeneously formatted data to normalise the retrieved heterogeneously formatted data into a standardised format;
- presenting, by a display unit [206], a unified dashboard interface that displays the at least one of the set of performance metrics, the status update, and the health data for each of the plurality of database clusters; and
- generating, by a generating unit [208], an alert when the set of performance metrics breaches a predefined threshold.

2. The method as claimed in claim 1, wherein retrieving data associated with the set of performance metrics comprises at least one of CPU usage, memory utilization, disk input/ Output (I/O) operations, and network latency.
3. The method as claimed in claim 1, wherein the unified dashboard interface is configurable to display data visualizations, comprising at least one of graph, chart, and heatmap.
4. The method as claimed in claim 1, wherein the method comprises storing, by the processing unit [202], the normalised data in a central repository.
5. The method as claimed in claim 4, wherein storing the normalized data in the central repository comprises timestamping and indexing each data entry.

6. The method as claimed in claim 1, wherein the generated alert is disseminated through one or more channels, and wherein the generated alert are prioritised according to severity..
7. The method as claimed in claim 1, wherein the method further comprises identifying, by an identifying unit [210], a network topology of each of the plurality of database clusters.
8. The method as claimed in claim 1, the method further comprises adjusting, by the processing unit [202], the predefined threshold based on a trained model that analyses the performance metrics over time, wherein the trained model is trained on a historical data associated with at least one of the set of performance metrics, the status update, and the health data of the plurality of database clusters.
9. The method as claimed in claim 1, wherein the network is 5G network, and wherein the plurality of database clusters are associated with 5G database ecosystem.
10. A system for centralized monitoring of database ecosystems in a network, said system comprising:
- a processing unit, configured to:
o establish secure connections of a centralized manager service with each of a plurality of database clusters across the network;
- a retrieving unit [204] configured to:
o retrieve data associated with at least one of a set of performance metrics, a status update, and health data from each of the plurality of database clusters in real-time, the retrieved data is heterogeneously formatted;
- the processing unit [202] configured to:
o preprocess the retrieved heterogeneously formatted data to normalise the retrieved heterogeneously formatted data into a standardised format;
- a display unit [206] configured to:

o present a unified dashboard interface that displays the at least one of the set of performance metrics, the status update, and the health data for each of the plurality of database clusters; and
- a generating unit [208] configured to:
o generate an alert when the set of performance metrics breaches a predefined threshold.
11. The system as claimed in claim 10, wherein retrieve data associated with the set of performance metrics comprises at least one of CPU usage, memory utilization, disk input/ Output (I/O) operations, and network latency.
12. The system as claimed in claim 10, wherein the unified dashboard interface is configurable to display data visualizations, comprising at least one of graph, chart, and heatmap.
13. The system as claimed in claim 10, wherein the processing unit [202] is configured to store the normalised data in a central repository.
14. The system as claimed in claim 13, wherein storing the normalized data in the central repository comprises timestamping and indexing each data entry.
15. The system as claimed in claim 10, wherein the generated alert is disseminated through one or more channels, and wherein the generated alert are prioritised according to severity.
16. The system as claimed in claim 10, wherein an identifying unit [210] is configured to identify a network topology of each of the plurality of database clusters.
17. The system as claimed in claim 10, wherein the processing unit [202] is configured to adjust the predefined threshold based on a trained model that analyses the performance metrics over time, wherein the trained model is trained on a historical data associated with at least one of the set of performance metrics, the status update, and the health data of the plurality of database clusters.

18. The system as claimed in claim 10, wherein the network is a 5G network, and wherein the plurality of database clusters are associated with 5G database ecosystem.