Description:DESCRIPTION
Technical Field
[001] This disclosure relates generally to backup software, and more particularly to method and system for managing and forecasting backup data.
Background
[002] Currently, the backup administrator is required to manually read documents of various backup vendors to select the most suitable backup solution for an organization that suits their cost and infrastructure needs. However, the manual process has several challenges, such as time consuming, prone to errors, lack of advanced tools which accurately forecast future storage needs and performance requirements, higher costs, difficulty in handling rapid data source which required frequent reassessment of backup solutions, and difficulty in consistently collecting and comparing performance metrices across different backup solutions.
[003] The techniques in the present state of art fail to facilitate efficient backup solutions to the organization based on current and future requirements. There is, therefore, a need for method and system for managing, evaluating, and forecasting backup data.
SUMMARY
[004] In one embodiment, a method for managing and forecasting backup data is disclosed. In one example, the method may include receiving, via a user interface, input data from a user device. It should be noted that the input data may include a plurality of backup software, a set of technical documents corresponding to each of the plurality of backup software, organization data, a set of current backup requirements, and an expected expansion of the organization data. The method may further include determining, via an Artificial Intelligence (AI) model, a set of expected backup requirements based on the expected expansion of the organization data. The method may further include configuring each of the plurality of backup software based on the corresponding set of technical documents and one of the set of current backup requirements or the set of expected backup requirements. For each configured backup software of the plurality of backup software, the method may further include performing, via the AI model, a simulated backup of the organization data using the configured backup software based on the set of current backup requirements and the set of expected backup requirements to obtain a set of simulation results. The method may further include rendering, via the user interface, the set of simulation results for each of the plurality of backup software on the user device.
[005] In another embodiment, a system for managing and forecasting backup data is disclosed. In one example, the system may include a processor and a computer-readable medium communicatively coupled to the processor. The computer-readable medium may store processor-executable instructions, which, on execution, may cause the processor to receive, via a user interface, input data from a user device. It should be noted that the input data may include a plurality of backup software, a set of technical documents corresponding to each of the plurality of backup software, organization data, a set of current backup requirements, and an expected expansion of the organization data. The processor-executable instructions, on execution, may further cause the processor to determine, via an AI model, a set of expected backup requirements based on the expected expansion of the organization data. The processor-executable instructions, on execution, may further cause the processor to configure each of the plurality of backup software based on the corresponding set of technical documents and one of the set of current backup requirements or the set of expected backup requirements. For each configured backup software of the plurality of backup software, the processor-executable instructions, on execution, may further cause the processor to perform, via the AI model, a simulated backup of the organization data using the configured backup software based on the set of current backup requirements and the set of expected backup requirements to obtain a set of simulation results. The processor-executable instructions, on execution, may further cause the processor to render, via the user interface, the set of simulation results for each of the plurality of backup software on the user device.
[006] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[007] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles.
[008] FIG. 1 is a block diagram of an exemplary system for managing and forecasting backup data, in accordance with some embodiments of the present disclosure.
[009] FIG. 2 illustrates a functional block diagram of a system for managing and forecasting backup data, in accordance with some embodiments of the present disclosure.
[010] FIGS. 3A and 3B illustrate a flow diagram of an exemplary process for managing and forecasting backup data, in accordance with some embodiments of the present disclosure.
[011] FIG. 4 illustrates a flow diagram of an exemplary process for identifying one or more relevant backup software, in accordance with some embodiments of the present disclosure.
[012] FIG. 5 illustrates a flow diagram of an exemplary process for performing automated backup of organization data, in accordance with some embodiments of the present disclosure.
[013] FIG. 6 is a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.
DETAILED DESCRIPTION
[014] Exemplary embodiments are described with reference to the accompanying drawings. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims.
[015] Referring now to FIG. 1, an exemplary system 100 for managing and forecasting backup data is illustrated, in accordance with some embodiments of the present disclosure. The system 100 may include a computing device 102. The computing device 102 may be, for example, but may not be limited to, server, desktop, laptop, notebook, netbook, tablet, smartphone, mobile phone, or any other computing device, in accordance with some embodiments of the present disclosure. For each of a plurality of backup software provided to Artificail Intelligence (AI) model, the computing device 102 may run backup simulations of organization data based on a set of current backup requirements and a set of expected backup requirements using the AI model to obtain a set of simulation results. Further, the computing device 102 may recommend one most appropriate backup software for the organization data based on the set of simulation results.
[016] As will be described in greater detail in conjunction with FIGS. 2 – 6, the computing device 102 may receive, via a user interface, input data from a user device. The input data may include a plurality of backup software, a set of technical documents corresponding to each of the plurality of backup software, organization data, a set of current backup requirements, and an expected expansion of the organization data. The computing device 102 may further determine, via an AI model, a set of expected backup requirements based on the expected expansion of the organization data. The computing device 102 may further configure each of the plurality of backup software based on the corresponding set of technical documents and one of the set of current backup requirements or the set of expected backup requirements. For each configured backup software of the plurality of backup software, the computing device 102 may further perform, via the AI model, a simulated backup of the organization data using the configured backup software based on the set of current backup requirements and the set of expected backup requirements to obtain a set of simulation results. The computing device 102 may further render, via the user interface, the set of simulation results for each of the plurality of backup software on the user device.
[017] In some embodiments, the computing device 102 may include one or more processors 104 and a memory 106. Further, the memory 106 may store instructions that, when executed by the one or more processors 104, may cause the one or more processors 104 to manage and forecast the backup data, in accordance with aspects of the present disclosure. The memory 106 may also store various data (for example, input data, a set of expected backup requirements, simulated results, one or more relevant backup software, a set of backup patterns, and the like) that may be captured, processed, and/or required by the system 100.
[018] The system 100 may further include a display 108. The system 100 may interact with a user interface 110 accessible via the display 108. The system 100 may also include one or more external devices 112. In some embodiments, the computing device 102 may interact with the one or more external devices 112 over a communication network 114 for sending or receiving various data. The communication network 114 may include, for example, but may not be limited to, a wireless fidelity (Wi-Fi) network, a light fidelity (Li-Fi) network, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a satellite network, the internet, a fiber optic network, a coaxial cable network, an infrared (IR) network, a radio frequency (RF) network, and a combination thereof. The one or more external devices 112 may include, but may not be limited to, a remote server, a laptop, a netbook, a notebook, a smartphone, a mobile phone, a tablet, or any other computing device.
[019] Referring now to FIG. 2, a functional block diagram of a system 200 for managing and forecasting backup data is illustrated, in accordance with some embodiments of the present disclosure. FIG. 2 is explained in conjunction with FIG. 1. The system 200 may be analogous to the system 100. The system 200 may implement the computing device 102. The system 200 may include, within a memory (such as the memory 106), a receiving module 202, a backup requirement estimating module 204, a simulation module 206, a backup software selecting module 208, a threat detecting module 210, a backup module 212, a rendering module 214, an Artificial Intelligence (AI) module 216. The AI module 216 may include AI model 218.
[020] Initially, the receiving module 202 may receive, via a user interface, input data 220 from a user device. The input data 220 may include a plurality of backup software, a set of technical documents corresponding to each of the plurality of backup software, organization data, a set of current backup requirements, and an expected expansion of the organization data. The set of current backup requirements may include a current backup data size corresponding to the organization data. For example, the current backup data size may be, but may not be limited to, 1000 Kilo Byte (KB), 1000 Mega Byte (MB), 1000 Giga Byte (GB), 500 Tera Byte (TB), 100 Peta Byte (PB), or the like.
[021] The plurality of backup software may be received from different vendors. For example, the plurality of backup software, may be, but may not be limited to, IDrive, Backblaze, Druva, Rubrik, Google® Drive, Microsoft® Azure, Acronis, or the like. In some embodiments a user (e.g., a backup administrator, or the like) may provide trial versions of the backup software to the receiving module 202. The technical documents may be any documents which include architecture, usage, configuration, integration, and maintenance of the backup software. The set of technical documents may be received in a format of, for example, Portable Document Format (PDF), word document (DOC or DOCX), HTML, spreadsheet (e.g., excel files, Comma Separated Values (CSV) files, etc.), or the like. For example, the user device may include, but may not be limited to, a remote server, a laptop, a netbook, a notebook, a smartphone, a mobile phone, a tablet, or any other computing device.
[022] In some embodiments, if the user may not provide any expected expansion of the organization data, in such cases, the backup requirement estimating module 204 may monitor the organization data to identify a set of backup patterns using a clustering algorithm. The backup pattern may be a predefined strategy used to create and manage backups of data. For example, the set of backup patterns may be, but may not be limited to, a full backup, an incremental backup, a differential backup, a mirror backup, or the like. By way of an example, the clustering algorithm may be, but may not be limited to, a K-means clustering, a Density-Based Spatial Clustering of Applications with Noise (DBSCAN), a Principal Component Analysis (PCA), or the like. Further, the backup requirement estimating module 204 may determine, via the AI model 218, the expected expansion of the organization data based on the set of backup patterns.
[023] Further, the backup requirement estimating module 204 may determine, via the AI model 218, a set of expected backup requirements based on the organization data. The set of expected backup requirements may include an expected backup data size corresponding to the organization data. For example, the expected backup data size may be, but may not be limited to, 1000 GB, 1000 TB, 1000 PB, or the like. To determine the set of expected backup requirements, the backup requirement estimating module 204 may predict the expected backup data size for a predefined time period (e.g., daily, weekly, monthly, quarterly, etc.) based on the set of backup patterns using a Machine Learning (ML) model. For example, the ML model may be, but may not be limited to, a Linear Regression, a Decision Tree, a Support Vector Machine (SVM), or the like.
[024] Further, the backup requirement estimating module 204 may predict the set of expected backup requirements for the predefined time period based on the expected backup data size using a statistical algorithm. For example, the statistical algorithm may be, but may not be limited to, a Time Series Analysis, a Logistic Regression, an Auto Regressive Integrated Moving Average (ARIMA), or the like.
[025] Further, the simulation module 206 may configure each of the plurality of backup software based on the corresponding set of technical documents and one or more of the set of current backup requirements or the set of expected backup requirements. Further, for each configured backup software of the plurality of backup software, the simulation module 206 may perform, via the AI model 218, a simulated backup of the organization data using the configured backup software based on the set of current backup requirements and the set of expected backup requirements to obtain a set of simulation results 222. The set of simulation results 222 may include one or more performance metrics (e.g., Backup Success Rate, Recovery Time Objective (RTO), backup speed, storage efficiency, Recovery Point Objective (RPO), etc.), and one or more cost metrics.
[026] To obtain the set of simulation results 222, the simulation module 206 may perform, via the AI model 218, a first simulated backup of the organization data using the configured backup software based on the set of current backup requirements to obtain a set of first simulation results. Further, the simulation module 206 may perform, via the AI model 218, a second simulated backup of the organization data using the configured backup software based on the set of expected backup requirements to obtain a set of second simulation results. In some embodiments, the first simulation backup and the second simulation backup may be performed simultaneously by the simulation module 206 to obtain the set of first simulation results and the set of second simulation results respectively. The set of simulation results 222 may include the set of first simulated results and the set of second simulated results. Further, the simulation module 206 may send the set of simulation results 222 to the rendering module 214.
[027] Further, the rendering module 214 may render, via the user interface, the set of simulation results 222 for each of the plurality of backup software on the user device for the user review. The set of simulation results 222 may be rendered in one of a format, for example, a dashboard, a report, or the like.
[028] In some embodiments, upon obtaining the set of simulation results, the backup software selecting module 208 may compare, via the AI model 218, the plurality of backup software based on the set of simulation results, the set of current backup requirements, and the set of expected backup requirements to obtain a comparison matrix. Further, the backup software selecting module 208 may identify, via the AI model 218, one or more relevant backup software from the plurality of backup software based on the comparison matrix.
[029] Further, the backup software selecting module 208 may send the one or more relevant backup software and the corresponding set of simulation results to the rendering module 214. Further, the rendering module 214 may render, via the user interface, the one or more relevant backup software and the corresponding simulation results on the user device.
[030] Further, the receiving module 202 may receive, via the user interface, a user-selected backup software from the plurality of backup software. Further, the backup module 212 may automatically create, via the AI model 218, a backup schedule (e.g., daily, weekly, monthly, or the like) for backup of the organization data using the user-selected backup software. Upon creating the backup schedule, the backup module 212 may validate, via the AI model 218, the organization data prior to backup using a set of data validation techniques. To validate the organization data, the threat detecting module 210 may detect, via the AI model 218, one or more anomalies (or issues) in the organization data based on the set of backup patterns using an anomaly detection algorithm. For example, the anomaly detection algorithm may be, but may not be limited to, an Isolation Forest, a Local Outlier Factor (LOF), or the like. It should be noted that each of the one or more anomalies may correspond to a potential threat.
[031] If the one or more anomalies are detected, the threat detecting module 210 may notify, via the user interface, the user about the one or more anomalies. Further, the backup module 212 may perform an automated backup of the validated organization data based on the backup schedule.
[032] By way of an example, a user may want to implement an AI-based comparison tool in the user device (e.g., laptop). Initially, the user may ensure to have the necessary infrastructure (i.e., hardware and software) which support the AI tools. The infrastructure may include servers, storage devices, network infrastructure. Further, the user may integrate the AI tools with the existing data sources by setting up Application Programming Interface (APIs), data pipelines, and Extract, Transform, Load (ETL) processes. For example, the user may integrate an IBM Watson Studio and Tableau (i.e., the AI tools) with the existing data sources.
[033] Further, the user may configure the AI tools to meet specific requirements. Configuring the AI tools, may include setting up data collection, performance metrices, and comparison matrices. Further, the user may develop an AI-based backup comparison tool using the selected AI tools. Developing the AI-based backup comparison tool may include coding, setting up workflows, and creating dashboards. In continuation with the above example, the user may create workflows in the IBM Watson Studio to analyze the backup performance and visualize results in the Tableau. Upon developing the AI-based comparison tool, the user may deploy the AI-based comparison tool in the production environment. It should be noted that the production environment may be accessible to the user and other stakeholders.
[034] In continuation with the above example, the user may deploy the AI-based compassion tool on Amazon Web Services (AWS) and make the AI-based comparison tool accessible to a backup administrator (i.e., the user). Upon deployment, the user may continuously monitor the AI-based comparison tool to ensure correct functioning. Continuous monitoring of the AI-based comparison tool may include tracking performance, detecting anomalies, and making necessary adjustments as required. In continuation with the above example, the user may continuously monitor the performance of the deployed tool and make adjustments as needed.
[035] It should be noted that all such aforementioned modules 202 – 218 may be represented as a single module or a combination of different modules. Further, as will be appreciated by those skilled in the art, each of the modules 202 – 218 may reside, in whole or in parts, on one device or multiple devices in communication with each other. In some embodiments, each of the modules 202 – 218 may be implemented as dedicated hardware circuit comprising custom application-specific integrated circuit (ASIC) or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. Each of the modules 202 – 218 may also be implemented in a programmable hardware device such as a field programmable gate array (FPGA), programmable array logic, programmable logic device, and so forth. Alternatively, each of the modules 202 – 218 may be implemented in software for execution by various types of processors (e.g., processor 104). An identified module of executable code may, for instance, include one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, function, or other construct. Nevertheless, the executables of an identified module or component need not be physically located together but may include disparate instructions stored in different locations which, when joined logically together, include the module and achieve the stated purpose of the module. Indeed, a module of executable code could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different applications, and across several memory devices.
[036] As will be appreciated by one skilled in the art, a variety of processes may be employed for managing and forecasting backup data. For example, the exemplary system 100 and the associated computing device 102, may manage and forecast backup data, by the processes discussed herein. In particular, as will be appreciated by those of ordinary skill in the art, control logic and/or automated routines for performing the techniques and steps described herein may be implemented by the system 100 and the associated computing device 102 either by hardware, software, or combinations of hardware and software. For example, suitable code may be accessed and executed by the one or more processors on the system 100 to perform some or all of the techniques described herein. Similarly, application specific integrated circuits (ASICs) configured to perform some or all of the processes described herein may be included in the one or more processors on the system 100.
[037] Referring now to FIGS. 3A and 3B, an exemplary process 300 for managing and forecasting backup data is illustrated via a flow chart, in accordance with some embodiments of the present disclosure. FIGS. 3A and 3B are explained in conjunction with FIGS. 1 and 2. The process 300 may be implemented by the computing device 102 of the system 100. In some embodiments, the process 300 may include receiving, by a receiving module (such as the receiving module 202) via a user interface, input data (such as the input data 220) from a user device, at step 302. The input data may include a plurality of backup software, a set of technical documents corresponding to each of the plurality of backup software, organization data, a set of current backup requirements, and an expected expansion of the organization data.
[038] In some embodiments, if a user may not provide any expected expansion of the organization data to the receiving module, the process 300 may include monitoring, by a backup requirement estimating module (such as the backup requirement estimating module 204), the organization data to identify a set of backup patterns using a clustering algorithm, at step 304. Further, the process 300 may include determining, by the backup requirement estimating module via an AI model (such as the AI model 218), the expected expansion of the organization data based on the set of backup patterns, at step 306.
[039] Upon receiving the input data, the process 300 may include determining, by the backup requirement estimating module via the AI model, a set of expected backup requirements based on the expected expansion of the organization data, at step 308. The set of expected backup requirements may include an expected backup data size corresponding to the organization data. The step 308 may include steps 310, and 312.
[040] To determine the set of expected backup requirements, the process 300 may include predicting, by the backup requirement estimating module, the expected backup data size for a predefined time period based on the set of backup patterns using a ML model, at step 310. Upon predicting the expected backup data size, the process 300 may include predicting, by the backup requirement estimating module, the set of expected backup requirements for the predefined time period based on the expected backup data size using a statistical algorithm, at step 312.
[041] Once the set of expected backup requirements are determined, the process 300 may include configuring, by a simulation module (such as the simulation module 206), each of the plurality of backup software based on the corresponding set of technical documents and one of the set of current backup requirements or the set of expected backup requirements, at step 314. Further, for each configured backup software of the plurality of backup software, the process 300 may include performing, by the simulation module via the AI model, a simulated backup of the organization data using the configured backup software based on the set of current backup requirements and the set of expected backup requirements to obtain a set of simulation results, at step 316. The set of simulation results may include one or more performance metrics and one or more cost metrics. The step 316 may include steps 318 and 320.
[042] To obtain the set of simulation results, the process 300 may include performing, by the simulation module via the AI model, a first simulated backup of the organization data using the configured backup software based on the set of current backup requirements to obtain the set of first simulation results, at step 318. Additionally, the process 300 may include performing, by the simulation module via the AI model, a second simulated backup of the organization data using the configured backup software based on the set of expected backup requirements to obtain the set of second simulation results, at step 320. The set of simulation results may include the set of first simulation results and the set of second simulation results.
[043] Further, the process 300 may include rendering, by a rendering module (such as the rendering module 214), via the user interface, the set of simulation results for each of the plurality of backup software on the user device, at step 322.
[044] Referring now to FIG. 4, an exemplary process 400 for identifying one or more relevant backup software is illustrated via a flow chart, in accordance with some embodiments of the present disclosure. FIG. 4 is explained in conjunction with FIGS. 1, 2, and 3. In some embodiments, upon obtaining the simulated results, at step 316, the process 400 may include comparing, by a backup software selecting module (such as the backup software selecting module 208) via the AI model, the plurality of backup software based on the set of simulation results, the set of current backup requirements, and the set of expected backup requirements to obtain a comparison matrix, at step 402. The set of simulation results may include one or more performance metrics and one or more cost metrics.
[045] Further, the process 400 may include identifying, by the backup software selecting module via the AI model, one or more relevant backup software from the plurality of backup software based on the comparison matrix, at step 404. Further, the process 400 may include rendering, by the rendering module via the user interface, the one or more relevant backup software on the user device, at step 406.
[046] Referring now to FIG. 5, an exemplary process for performing automated backup of organization data is illustrated, via a flow chart, in accordance with some embodiments of the present disclosure. FIG. 5 is explained in conjunction with FIGS. 1, 2, 3, and 4. In some embodiments, upon rendering the simulated results on the user device, at step 322, the process 500 may include automatically creating, by a backup module (such as the backup module 212) via the AI model, a backup schedule for backup of the organization data using a user-selected backup software from the plurality of backup software, at step 502.
[047] Further, the process 500 may include validating, by the backup module via the AI model, the organization data prior to backup using a set of data validation techniques, at step 504. To validate the organization data, the process 500 may include detecting, by a threat detecting module (such as the threat detecting module 210) via the AI model, one or more anomalies in the organization data based on the set of backup patterns, at step 506. Each of the one or more anomalies may correspond to a potential threat. If the one or more anomalies are detected, the process 500 may include notifying, by the threat detecting module via the user interface, a user about the one or more anomalies, at step 508.
[048] Upon successful validation, the process 500 may include performing, by the backup module, an automated backup of the validated organization data based on the backup schedule, at step 510.
[049] As will be also appreciated, the above-described techniques may take the form of computer or controller implemented processes and apparatuses for practicing those processes. The disclosure can also be embodied in the form of computer program code containing instructions embodied in tangible media, such as floppy diskettes, solid state drives, CD-ROMs, hard drives, or any other computer-readable storage medium, wherein, when the computer program code is loaded into and executed by a computer or controller, the computer becomes an apparatus for practicing the invention. The disclosure may also be embodied in the form of computer program code or signal, for example, whether stored in a storage medium, loaded into and/or executed by a computer or controller, or transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the invention. When implemented on a general-purpose microprocessor, the computer program code segments configure the microprocessor to create specific logic circuits.
[050] The disclosed methods and systems may be implemented on a conventional or a general-purpose computer system, such as a personal computer (PC) or server computer. Referring now to FIG. 6, an exemplary computing system 600 that may be employed to implement processing functionality for various embodiments (e.g., as a SIMD device, client device, server device, one or more processors, or the like) is illustrated. Those skilled in the relevant art will also recognize how to implement the invention using other computer systems or architectures. The computing system 600 may represent, for example, a user device such as a desktop, a laptop, a mobile phone, personal entertainment device, DVR, and so on, or any other type of special or general-purpose computing device as may be desirable or appropriate for a given application or environment. The computing system 600 may include one or more processors, such as a processor 602 that may be implemented using a general or special purpose processing engine such as, for example, a microprocessor, microcontroller or other control logic. In this example, the processor 602 is connected to a bus 604 or other communication medium. In some embodiments, the processor 602 may be an Artificial Intelligence (AI) processor, which may be implemented as a Tensor Processing Unit (TPU), or a graphical processor unit, or a custom programmable solution Field-Programmable Gate Array (FPGA).
[051] The computing system 600 may also include a memory 606 (main memory), for example, Random Access Memory (RAM) or other dynamic memory, for storing information and instructions to be executed by the processor 602. The memory 606 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by the processor 602. The computing system 600 may likewise include a read only memory (“ROM”) or other static storage device coupled to bus 604 for storing static information and instructions for the processor 602.
[052] The computing system 600 may also include a storage devices 608, which may include, for example, a media drive 610 and a removable storage interface. The media drive 610 may include a drive or other mechanism to support fixed or removable storage media, such as a hard disk drive, a floppy disk drive, a magnetic tape drive, an SD card port, a USB port, a micro USB, an optical disk drive, a CD or DVD drive (R or RW), or other removable or fixed media drive. A storage media 612 may include, for example, a hard disk, magnetic tape, flash drive, or other fixed or removable medium that is read by and written to by the media drive 610. As these examples illustrate, the storage media 612 may include a computer-readable storage medium having stored therein particular computer software or data.
[053] In alternative embodiments, the storage devices 608 may include other similar instrumentalities for allowing computer programs or other instructions or data to be loaded into the computing system 600. Such instrumentalities may include, for example, a removable storage unit 614 and a storage unit interface 616, such as a program cartridge and cartridge interface, a removable memory (for example, a flash memory or other removable memory module) and memory slot, and other removable storage units and interfaces that allow software and data to be transferred from the removable storage unit 614 to the computing system 600.
[054] The computing system 600 may also include a communications interface 618. The communications interface 618 may be used to allow software and data to be transferred between the computing system 600 and external devices. Examples of the communications interface 618 may include a network interface (such as an Ethernet or other NIC card), a communications port (such as for example, a USB port, a micro USB port), Near field Communication (NFC), etc. Software and data transferred via the communications interface 618 are in the form of signals which may be electronic, electromagnetic, optical, or other signals capable of being received by the communications interface 618. These signals are provided to the communications interface 618 via a channel 620. The channel 620 may carry signals and may be implemented using a wireless medium, wire or cable, fiber optics, or other communications medium. Some examples of the channel 620 may include a phone line, a cellular phone link, an RF link, a Bluetooth link, a network interface, a local or wide area network, and other communications channels.
[055] The computing system 600 may further include Input/Output (I/O) devices 622. Examples may include, but are not limited to a display, keypad, microphone, audio speakers, vibrating motor, LED lights, etc. The I/O devices 622 may receive input from a user and also display an output of the computation performed by the processor 602. In this document, the terms “computer program product” and “computer-readable medium” may be used generally to refer to media such as, for example, the memory 606, the storage devices 608, the removable storage unit 614, or signal(s) on the channel 620. These and other forms of computer-readable media may be involved in providing one or more sequences of one or more instructions to the processor 602 for execution. Such instructions, generally referred to as “computer program code” (which may be grouped in the form of computer programs or other groupings), when executed, enable the computing system 600 to perform features or functions of embodiments of the present invention.
[056] In an embodiment where the elements are implemented using software, the software may be stored in a computer-readable medium and loaded into the computing system 600 using, for example, the removable storage unit 614, the media drive 610 or the communications interface 618. The control logic (in this example, software instructions or computer program code), when executed by the processor 602, causes the processor 602 to perform the functions of the invention as described herein.
[057] Various embodiments provide method and system for managing and forecasting backup data. The disclosed method and system may receive, via a user interface, input data from a user device. The input data may include a plurality of backup software, a set of technical documents corresponding to each of the plurality of backup software, organization data, a set of current backup requirements, and an expected expansion of the organization data. Further, the disclosed method and system may determine, via an AI model, a set of expected backup requirements based on the expected expansion of the organization data. Further, the disclosed method and system may configure each of the plurality of backup software based on the corresponding set of technical documents and one of the set of current backup requirements or the set of expected backup requirements. Moreover, for each configured backup software of the plurality of backup software, the disclosed method and system may perform, via the AI model, a simulated backup of the organization data using the configured backup software based on the set of current backup requirements and the set of expected backup requirements to obtain a set of simulation results. Thereafter, the disclosed method and system may render, via the user interface, the set of simulation results for each of the plurality of backup software on the user device.
[058] Thus, the disclosed method and system try to overcome the technical problem of managing and forecasting backup data. The disclosed method and system may automate an evaluation process of backup software using an AI model. Further, the disclosed method and system may automatically collect data about the backup software. Further, the disclosed method and system may generate and record performance metrics and cost metrics using the AI model while performing backups. Further, the disclosed method and system may populate a comparison matrix. The comparison matrix may display which backup software is more suitable for organization as per their requirements (i.e., current requirement and expected requirements). Further, the disclosed method and system may provide recommendations on the scope for expansion. Additionally, the disclosed method and system may provide right (or best) backup software with an improved forecast for future workloads. Further, the disclosed method and system may provide services to other organizations. Further, the disclosed method and system may detect potential threats before they cause damage (or performing backup of data). Further, the disclosed method and system may automate routine tasks (such as data validation, backup scheduling, anomaly detection) using the AI model. This may lead to reduce the risk of human error and may ensure data backups are performed consistently and accurately.
[059] In light of the above mentioned advantages and the technical advancements provided by the disclosed method and system, the claimed steps as discussed above are not routine, conventional, or well understood in the art, as the claimed steps enable the following solutions to the existing problems in conventional technologies. Further, the claimed steps clearly bring an improvement in the functioning of the device itself as the claimed steps provide a technical solution to a technical problem.
[060] The specification has described method and system for managing and forecasting backup data. The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[061] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
[062] It is intended that the disclosure and examples be considered as exemplary only, with a true scope and spirit of disclosed embodiments being indicated by the following claims. , Claims:CLAIMS
I/We Claim:
1. A method (300) for managing and forecasting backup data, the method (300) comprising:
receiving (302), by a computing device (102) via a user interface, input data from a user device, wherein the input data comprises a plurality of backup software, a set of technical documents corresponding to each of the plurality of backup software, organization data, a set of current backup requirements, and an expected expansion of the organization data;
determining (308), by the computing device (102) via an Artificial Intelligence (AI) model (218), a set of expected backup requirements based on the expected expansion of the organization data;
configuring (314), by the computing device (102), each of the plurality of backup software based on the corresponding set of technical documents and one of the set of current backup requirements or the set of expected backup requirements;
for each configured backup software of the plurality of backup software, performing (316), by the computing device (102) via the AI model (218), a simulated backup of the organization data using the configured backup software based on the set of current backup requirements and the set of expected backup requirements to obtain a set of simulation results; and
rendering (322), by the computing device (102) via the user interface, the set of simulation results for each of the plurality of backup software on the user device.

2. The method (300) as claimed in claim 1, comprising:
comparing (402), by the computing device (102) via the AI model (218), the plurality of backup software based on the set of simulation results, the set of current backup requirements, and the set of expected backup requirements to obtain a comparison matrix, wherein the set of simulation results comprises one or more performance metrics and one or more cost metrics;
identifying (404), by the computing device (102) via the AI model (218), one or more relevant backup software from the plurality of backup software based on the comparison matrix; and
rendering (406), by the computing device (102) via the user interface, the one or more relevant backup software on the user device.

3. The method (300) as claimed in claim 1, comprising:
monitoring (304) the organization data to identify a set of backup patterns using a clustering algorithm; and
determining (306), via the AI model (218), the expected expansion of the organization data based on the set of backup patterns.

4. The method (300) as claimed in claim 3, wherein the set of current backup requirements comprises a current backup data size corresponding to the organization data, and wherein the set of expected backup requirements comprises an expected backup data size corresponding to the organization data.

5. The method (300) as claimed in claim 4, wherein determining, via the AI model (218), the set of expected backup requirements comprises:
predicting (310) the expected backup data size for a predefined time period based on the set of backup patterns using a Machine Learning (ML) model; and
predicting (312) the set of expected backup requirements for the predefined time period based on the expected backup data size using a statistical algorithm.

6. The method (300) as claimed in claim 3, comprising:
detecting (506), via the AI model (218), one or more anomalies in the organization data based on the set of backup patterns, wherein each of the one or more anomalies correspond to a potential threat; and
notifying (508), via the user interface, a user about the one or more anomalies.

7. The method (300) as claimed in claim 1, wherein performing, via the AI model (218), the simulated backup of the organization data using the configured backup software comprises:
performing (318), via the AI model (218), a first simulated backup of the organization data using the configured backup software based on the set of current backup requirements to obtain a set of first simulation results; and
performing (320), via the AI model (218), a second simulated backup of the organization data using the configured backup software based on the set of expected backup requirements to obtain a set of second simulation results, wherein the set of simulation results comprises the set of first simulation results and the set of second simulation results.

8. The method (300) as claimed in claim 1, comprising:
automatically creating (502), via the AI model (218), a backup schedule for backup of the organization data using a user-selected backup software from the plurality of backup software;
validating (504), via the AI model (218), the organization data prior to backup using a set of data validation techniques; and
performing (510) an automated backup of the validated organization data based on the backup schedule.

9. A system (100) for managing and forecasting backup data, the system (100) comprising:
a processor (104); and
a memory (106) communicatively coupled to the processor (104), wherein the memory (106) stores processor executable instructions, which, on execution, causes the processor (104) to:
receive (302), via a user interface, input data from a user device, wherein the input data comprises a plurality of backup software, a set of technical documents corresponding to each of the plurality of backup software, organization data, a set of current backup requirements, and an expected expansion of the organization data;
determine (308), via an Artificial Intelligence (AI) model (218), a set of expected backup requirements based on the expected expansion of the organization data;
configure (314) each of the plurality of backup software based on the corresponding set of technical documents and one of the set of current backup requirements or the set of expected backup requirements;
for each configured backup software of the plurality of backup software, perform (316), via the AI model (218), a simulated backup of the organization data using the configured backup software based on the set of current backup requirements and the set of expected backup requirements to obtain a set of simulation results; and
render (322), via the user interface, the set of simulation results for each of the plurality of backup software on the user device.

10. The system (100) as claimed in claim 9, wherein the processor executable instructions cause the processor (104) to:
compare (402), via the AI model (218), the plurality of backup software based on the set of simulation results, the set of current backup requirements, and the set of expected backup requirements to obtain a comparison matrix, wherein the set of simulation results comprises one or more performance metrics and one or more cost metrics;
identify (404), via the AI model (218), one or more relevant backup software from the plurality of backup software based on the comparison matrix; and
render (406), via the user interface, the one or more relevant backup software on the user device.

11. The system (100) as claimed in claim 9, wherein the processor executable instructions cause the processor (104) to:
monitor (304) the organization data to identify a set of backup patterns using a clustering algorithm; and
determine (306), via the AI model (218), the expected expansion of the organization data based on the set of backup patterns.

12. The system (100) as claimed in claim 11, wherein the set of current backup requirements comprises a current backup data size corresponding to the organization data, and wherein the set of expected backup requirements comprises an expected backup data size corresponding to the organization data.

13. The system (100) as claimed in claim 12, wherein determining, via the AI model (218), the set of expected backup requirements, the processor executable instructions cause the processor (104) to:
predict (310) the expected backup data size for a predefined time period based on the set of backup patterns using a Machine Learning (ML) model; and
predict (312) the set of expected backup requirements for the predefined time period based on the expected backup data size using a statistical algorithm.

14. The system (100) as claimed in claim 11, wherein the processor executable instructions cause the processor (104) to:
detect (506), via the AI model (218), one or more anomalies in the organization data based on the set of backup patterns, wherein each of the one or more anomalies correspond to a potential threat; and
notify (508), via the user interface, a user about the one or more anomalies.

15. The system (100) as claimed in claim 9, wherein preforming, via the AI model (218), the simulated backup of the organization data using the configured backup software, the processor executable instructions cause the processor (104) to:
perform (318), via the AI model (218), a first simulated backup of the organization data using the configured backup software based on the set of current backup requirements to obtain a set of first simulation results; and
perform (320), via the AI model (218), a second simulated backup of the organization data using the configured backup software based on the set of expected backup requirements to obtain a set of second simulation results, wherein the set of simulation results comprises the set of first simulation results and the set of second simulation results.

16. The system (100) as claimed in claim 9, wherein the processor executable instructions cause the processor (104) to:
automatically create (502), via the AI model (218), a backup schedule for backup of the organization data using a user-selected backup software from the plurality of backup software;
validate (504), via the AI model (218), the organization data prior to backup using a set of data validation techniques; and
perform (510) an automated backup of the validated organization data based on the backup schedule.