Extensive data center architecture systems and methods

Abstract

Embodiments of a system for determining a data gravity index score and implementing a wide range of data center architectures are disclosed. In some embodiments, the system can calculate a data gravity index score based on the amount of data stored at a given location, the amount of data in motion at the given location, a bandwidth index associated with the given location, and a latency index associated with the given location. Based on the data gravity index score, in some embodiments, the system can localize traffic to improve network performance, improve security operations, and create software-defined network overlays.

Description

Technical Field

[0001] Cross - reference to Related Applications This application claims priority from U.S. Provisional Patent Application No. 63 / 083,763, filed on September 25, 2020, which is hereby incorporated by reference in its entirety.

Background Art

[0002] The present disclosure generally relates to systems and methods for managing a data store across platforms and data stores.

Summary of the Invention

[0003] In one embodiment, a system for evaluating heterogeneous storage of data across multiple storage devices is disclosed. The system comprises a processor, memory, a data gravity analysis configuration module, a knowledge database stored in memory, and computer code stored in memory which, when retrieved from memory and executed by the processor, causes the processor to receive information about one or more nodes from multiple forensic source submitters, where one or more nodes are associated with a network, a mass data storage system, data characteristics including at least one of data mass, data activity, bandwidth between at least two points, or latency, data storage parameters, one or more zone indicators, and one or more Internet Protocol IP addresses, and the submitters are registered contributors when providing evidence of aggregated data storage, and the processor receives and The computer code includes: using to identify a selected zone indicator received via a user interface; using a processor to select a subset of nodes based on the selected zone indicator, wherein each node in the subset of nodes is associated with the selected zone indicator; using a processor to calculate a data gravity index score for the subset of nodes based at least partially on the selected zone indicator and one or more of the data properties of each of the context-weighted subset of nodes; updating a knowledge database with the calculated data gravity index score; and outputting the calculated data gravity index score to a data gravity analysis configuration module.

[0004] The data gravity analysis configuration module can automatically generate encrypted data packets, which include automatic recommendations for one or more data storage parameters for one or more nodes on the network based on the calculated data gravity index score, and instruct the network module to send encrypted data packets to one or more nodes.

[0005] The data gravity analysis configuration module can automatically generate automatic recommendation or warning flags for one or more data storage parameters for one or more nodes on the network, based on the calculated data gravity index score. Automatic recommendations may include at least one of the following: identifying and lowering the priority of stale data on one or more nodes; performing additional local network inbound / outbound operations for one or more nodes; adjusting the bandwidth of one or more nodes; adjusting the latency of one or more nodes; adjusting the data distribution between one or more nodes; or adjusting the data capacity of one or more nodes.

[0006] The data gravity analysis configuration module can automatically generate instructions for rendering flagged items on the user interface based on the calculated data gravity index score.

[0007] The data gravity analysis configuration module can automatically generate and push alerts to one or more remote systems based on the calculated data gravity index score.

[0008] The data gravity analysis configuration module can automatically generate commands for a remote system based on the calculated data gravity index score.

[0009] The data gravity index score can be calculated according to the formula: (((data mass * data activity)^2) * bandwidth) / (latency^2). The index score for each of data mass, data activity, bandwidth, and latency is calculated for each of one or more nodes, at least in part, based on the corresponding formulas for data mass, data activity, bandwidth, and latency. Alternatively, the data gravity index score can be calculated according to the formula: (data mass * data activity * bandwidth) / (latency^2).

[0010] The data gravity index score can be calculated using a machine learning module configured to identify one or more patterns associated with the data characteristics of a subset of nodes.

[0011] The computer code can further instruct the processor to use a machine learning model to identify one or more patterns associated with the data characteristics of a subset of nodes, and then, using the machine learning model and the processor, to calculate a predicted data gravity index score for the subset of nodes, at least partially based on one or more patterns. The predicted data gravity score can be calculated without using a formula for calculating the data gravity index score.

[0012] The computer code can further cause the processor to receive a first updated data characteristic associated with a subset of nodes, and to use the processor to calculate a first updated data gravity index score associated with the subset of nodes, at least based on the first updated data characteristic; to receive a second updated data characteristic associated with a subset of nodes, and to use the processor to calculate a second updated data gravity index score associated with the subset of nodes, at least based on the second updated data characteristic; and to calculate a predicted data gravity index score, at least based on the first updated data gravity index score and the second updated data gravity index score.

[0013] The computer code can further cause the processor to receive one or more data storage parameters of a subset of nodes, determine one or more patterns associated with the one or more data storage parameters, request updated data characteristics of the subset of nodes based at least partially on the one or more patterns, receive the requested updated data characteristics of the subset of nodes, and calculate an updated data gravity index based at least partially on the received updated data characteristics of the subset of nodes.

[0014] In another embodiment, a computer implementation method for evaluating heterogeneous storage of data across multiple storage devices is disclosed. This method is used for multiple forensic source submissions. Receiving information about one or more nodes from a person, the one or more nodes being associated with a network, a mass data storage system, data characteristics including at least one of data mass, data activity, bandwidth, or latency, data storage parameters, one or more zone indicators, and one or more Internet Protocol IP addresses, and the submitter being a registered contributor when providing evidence of aggregated data storage, including receiving, identifying selected zone indicators received via a user interface, and selecting a subset of nodes based on the selected zone indicators, each node in the subset of nodes being associated with the selected zone indicators, calculating a data gravity index score for the subset of nodes based at least in part on the selected zone indicators and one or more of the data characteristics of each of the subset of nodes weighted according to context, updating a knowledge database with the calculated data gravity index scores, and outputting the calculated data gravity index scores.

[0015] The computer implementation method may further include automatically generating encrypted data packets, which are configured to include automatic recommendations for one or more data storage parameters for one node on the network based on a calculated data gravity index score, and to instruct a network module to send encrypted data packets to one or more nodes.

[0016] The computer implementation method may further include automatically generating automatic recommendations for one or more data storage parameters for a single node on the network, based on the calculated data gravity index score.

[0017] The computer implementation method may further include automatically generating automatic warning flags for one or more data storage parameters for a single node on the network, based on the calculated data gravity index score.

[0018] The computer implementation method may further include automatically generating instructions for rendering flagged items on the user interface based on the calculated data gravity index score.

[0019] The computer implementation method may further include automatically generating and pushing alerts to one or more remote systems based on the calculated data gravity index score.

[0020] The computer implementation method may further include calculating the data gravity index score using the following formula: (((data mass * data activity)^2) * bandwidth) / (latency^2).

[0021] The computer implementation method may further include calculating the data gravity index score using the following formula: (data mass * data activity * bandwidth) / (latency^2).

[0022] In another embodiment, a non-temporary computer storage medium for storing computer executable instructions is disclosed. When executed by a processor, the computer executable instructions involve the processor receiving information about one or more nodes from a plurality of forensic source submitters, where one or more nodes are a network, a mass data storage system, and data mass, data activity, bandwidth, or latency. associated with at least one of data characteristics, data storage parameters, one or more zone indicators, and one or more Internet protocol IP addresses, the submitter being a registered contributor when providing evidence of aggregated data storage, receiving, identifying a selected zone indicator received via a user interface, selecting a subset of nodes based on the selected zone indicator, wherein each of the nodes within the subset of nodes is associated with the selected zone indicator, calculating a data gravity index score for the subset of nodes based at least in part on one or more of the data characteristics of each of the subset of nodes weighted according to the selected zone indicator and context, updating a knowledge database with the calculated data gravity index score, and outputting the calculated data gravity index score.

[0023] The computer-executable instructions are further configured to cause the processor to automatically generate an encrypted data packet, including an automatic recommendation for one or more data storage parameters for one node on the network based on the calculated data gravity index score, and to instruct a network module to transmit the encrypted data packet to one or more nodes.

[0024] The computer-executable instructions are further configured to cause the processor to automatically generate an automatic recommendation for one or more data storage parameters for one node on the network based on the calculated data gravity index score.

[0025] The computer-executable instructions are further configured to cause the processor to automatically generate an automatic warning flag for one or more data storage parameters for one node on the network based on the calculated data gravity index score.

[0026] The computer-executable instructions can further cause the processor to automatically generate instructions for rendering the flagged items on the user interface based on the calculated data gravity index score.

[0027] The computer-executable instructions can further cause the processor to automatically generate and push a warning to one or more remote systems based on the calculated data gravity index score.

[0028] The computer-executable instructions can further cause the processor to calculate the data gravity index score using the following formula: (((data mass * data activity)^2) * bandwidth) / (latency^2).

[0029] The computer-executable instructions can further cause the processor to calculate the data gravity index score using the following formula: ((data mass * data activity * bandwidth) / (latency^2)).

Brief Description of the Drawings

[0030] Many of the foregoing aspects and attendant advantages of the present disclosure will be more readily understood as the following detailed description is read in conjunction with the accompanying drawings. The accompanying drawings, which are incorporated herein and form part of this specification, illustrate embodiments of the present disclosure.

[0031] Throughout the drawings, reference numbers are reused to indicate correspondence between referenced elements. The drawings are provided to illustrate embodiments of the subject matter described herein and are not intended to limit the scope thereof. Specific embodiments will be described below with reference to the drawings.

[0032] [Figure 1A] It is a diagram showing the analysis provided by the data gravity analysis system. [Figure 1B]This is an overall system diagram showing one embodiment of a data gravity analysis system. [Figure 2] A diagram showing one embodiment of the process for determining the data gravity index score. [Figure 3A] This is an overall system diagram illustrating one embodiment of a broad data center architecture. [Figure 3B] This is an overall system diagram illustrating another embodiment of a broad data center architecture. [Figure 3C] This is an overall system diagram illustrating another embodiment of a broad data center architecture. [Figure 3D] This is an overall system diagram illustrating another embodiment of a broad data center architecture. [Figure 4] This figure shows one embodiment of a user interface component. [Figure 5] A block diagram showing one embodiment of the automated zoning process. [Figure 6] This is a general system diagram illustrating one embodiment of a computing system. [Modes for carrying out the invention]

[0033] Next, embodiments of the present disclosure will be described with reference to the accompanying drawings. The terms used in the descriptions presented herein are not intended to be constrained or restrictive simply because they are used in conjunction with the detailed descriptions of the embodiments of the present disclosure. Furthermore, embodiments of the present disclosure may include several novel features, none of which alone are solely responsible for their desired attributes, nor are they essential for carrying out the embodiments of the present disclosure described herein. Furthermore, for the purposes of the present disclosure, specific aspects, advantages, and novel features of various embodiments are described herein. It should be understood that not all such advantages can necessarily be achieved according to any particular embodiment. Therefore, for example, a person skilled in the art will recognize that one embodiment may be carried out to achieve one advantage or group of advantages taught herein without necessarily achieving other advantages that can be taught or suggested herein.

[0034] As vast amounts of data are created and stored, the data is often aggregated to provide insights and improve processes or experiences. However, data is often stored in centralized locations. As more data accumulates in centralized locations, additional services and applications are more likely to be attracted to the centrally stored data. Despite the ever-increasing number of user devices (mobile communication devices, laptops, tablets, etc.), regardless of geographical location, more applications and services are attracted to centrally stored or retained data, making it nearly impossible to move data from one location to another. This can create barriers that lead to undesirable complexities when considering business location, proximity to users, regulatory constraints, compliance, and data privacy.

[0035] As businesses undergo geographical expansion (e.g., reaching new audiences, creating new channels, and joining new digital ecosystems), it becomes crucial to build the technical tools and platforms that can support and maintain data whenever businesses operate. These businesses need to consider where to place, host, and connect their data due to the challenges associated with data gravity. For a process to run successfully, it's necessary to gather data, bring users, applications, and networks into the data, and have access to the right technologies to maintain that data.

[0036] Data gravity refers to the effect where, as data accumulates, additional services or applications are more likely to be drawn to it (referred to as "data gravity"). As data is generated, it is often aggregated and stored together to provide insights into the data or the users associated with it. As data is collected, more applications and services begin to use it, making it nearly impossible to move the data. This barrier can be further exacerbated by the fact that an increasing number of devices generate and access data, regardless of geographical location. Data gravity can disrupt workflow performance, raise security concerns, and increase business costs. These negative impacts of data gravity can be complicated by regulatory requirements and other non-technical constraints.

[0037] In some embodiments, a continuous data creation lifecycle can cause and / or substantially contribute to data gravity. For example, an increase in the number of users and endpoints (e.g., user computing devices such as mobile phones, Internet of Things ("IoT") devices, and sensors) can lead to an increase in the amount of data generated. An increase in the number of users and endpoints can lead to an increase in interactions and transactions between users and machines (e.g., service provider servers). Data collected from different companies can be collected and formatted for presentation, exchange, and compliant storage. Stored or retained data can be analyzed and / or enhanced, for example, to gain a competitive advantage between companies, leading to further data aggregation and exchange. In some embodiments, an analytics platform can be provided to digest these large and constantly growing datasets and to offer analytical tools.

[0038] Figure 1A shows an example of a global distribution of a sample of datastores, shown as cloud storage systems and servers managed by the data gravity analysis system 100. The data gravity analysis system 100 performs analysis on various storage systems to determine the data gravity of the systems. In the illustrated embodiment, the analysis is segmented based on geographical region. The intensity of the data 101 is shown via a data proxy that represents the relative indication of data gravity at various locations within each region.

[0039] Data Gravity Analysis System Figure 1B shows an exemplary embodiment of the data gravity analysis system 100. The data gravity analysis system 100 may include a user interface ("UI") 102, a set of subsystems or modules 104 capable of performing functions including, but not limited to, data acquisition, data cleansing, data linkage, report generation, and other related functions, a database 106, and an analysis system 108. The UI 102 may receive data directly from a service such as a report generation service or directly from the database 106. In some embodiments, data may be collected via an automated tool or a forensic data collection agent, or from a forensic source submitter configured to collect data and submit it to the data gravity analysis system 100. In some embodiments, the data gravity analysis system 100 may aggregate data from a diverse set of location-related source variables and use the data to calculate a score (referred to as the "data gravity index score") that provides a metric for data gravity. The analysis system 108 may, for example, communicate electronically with the database 106 to send and receive data and calculate the data gravity index score.

[0040] In some embodiments, it is recognized that the data gravity index score may be a composite of other scores and may represent the type of data gravity or the center of data gravity. The data gravity analysis system 100 can weight different types of data gravity and provide cumulative scores similar to those provided by conventional indices. As an example, the center of data mass may be other entities such as a company, government, government agency or department, data center, point of presence, group of data centers, collection of scientific or research facilities, or a large city, country, region, or collection of regions that store data in a statistically significant or extreme amount that results in a data gravity effect. The center of data activity may include entities that generate or process a statistically significant or large number of transactions. For example, a top financial center in New York may be found to be the center of data activity over a certain period, or a streaming service may be found to be the center of data activity if it has a large amount of user steaming data. It is recognized that data activity may be one type of data, or it may be an umbrella of data types, such as a data activity exchange (e.g., a connection location like Marseille or Amsterdam) which allows data activity transactions to be classified or segmented by type. In embodiments where the data gravity index is a composite of other centers of mass and their respective scores, the scores can enable a ranking, distinction, and deeper understanding of the impact each score has on a particular location as a whole.

[0041] In some embodiments, the data stored or retained in database 106 may come from one or more third-party systems 130 or external databases 120.

[0042] The data gravity analysis system 100 can calculate a data gravity index score based on various parameters that can be set by rule-based parameters within the system and / or automatically determined by the system. For example, the data gravity index score may be calculated separately for metropolitan areas, geographical regions, firms, industries, etc. Thus, in some embodiments, the data gravity analysis system 100 can calculate data gravity index scores between metropolitan areas, geographical regions, firms, industries, etc., and / or provide analytical comparisons between them. The data gravity analysis system 100 can generate interactive, real-time visualizations of such scores, comparisons, and analyses, along with predictive metrics, recommended actions, and / or automated instructions for the system to perform specific actions, such as reallocating or migrating one or more datasets, calculating or reallocating application functions / services, or reallocating networking resources.

[0043] In some embodiments, the index score can provide a relative proxy for measuring data creation, aggregation, and / or processing. The data gravity index score can take into account several attributes, including but not limited to, firmographic data (e.g., industry segments, employee data, revenue data, location data, corporate entities, etc.), technografic data (e.g., information technology ("IT") spending, preferred vendors, network traffic distribution, network points of presence ("PoP"), data center ("DC") PoP, cloud PoP, etc.), and industry benchmarks (e.g., data creation / transfer rates, latency by access method, user type, location, and application type, growth rate, cloud usage, networking services, distributed services, data technologies, endpoints, user devices, application use cases, etc.). Furthermore, the data gravity analysis system 100 may include components that communicate directly with one or more systems that provide relevant data and metrics, such as an accounting system that houses expenditure data, a third-party vendor system, a third-party data vendor system, a network server, a data center server, a cloud provider server, a marketing system platform, an IoT platform, a resource management server, and a customer relationship server.

[0044] In some embodiments, the data gravity index score may include one or more of the following variables and incorporate relevant data: IT spending, colocation spending, cloud spending, headquarters locations, business unit locations, market value, data traffic, gross domestic product ("GDP"), various levels (e.g., city, metropolis, country, state, national, and region), population, number of employees, data center locations, bandwidth availability, mean latency, mean traffic, peak traffic, industry locations, etc.

[0045] In some embodiments, the data gravity index score may include one or more of the following variables: total IT spending, IT storage spending, IT server spending, increase in IT storage spending, increase in IT server spending, IT server costs, IT storage costs, bandwidth availability, mean latency, industrial locations, storage consumption rate, percentage of deployed storage, type of deployed storage, and related data.

[0046] In some embodiments, the data gravity index score may include one or more of the following variables: the amount of data generated by employees, the amount of data generated by customers, the amount of data generated by systems, the amount of data generated by machines, the amount of data generated by sensors, the amount of data generated by devices, and other related data associated with them.

[0047] In some embodiments, the data gravity index score may include one or more of the following variables: the amount of processing power required for servers running machine learning, the amount of processing power required for servers running artificial intelligence, the amount of processing power required for servers running big data, the amount of processing power required for servers running analytics, the amount of processing power required for servers running applications, the amount of processing power required for servers running services (such as the web), and related data associated with them.

[0048] In some embodiments, the data gravity index score may include one or more of the following variables and related data, calculated via: mean annual growth rate ("CAGR"), city or metropolitan bandwidth speed, intercity or intermetropolitan bandwidth speed, intercity or intermetropolitan bandwidth growth, instructions executed per second ("MIPS"), wattage, network traffic, network patterns, content delivery network traffic, content delivery network patterns, other indices, etc.: intermetropolitan latency, intermetropolitan bandwidth, per capita growth of human-machine or human-device interaction, graphics processing unit ("GPU") / central processing unit ("CPU") cost, GPU / CPU floating-point capacity ("FLOP") per second, city or metropolitan bandwidth growth.

[0049] Data Gravity Index Score Figure 2 shows an exemplary process for calculating the data gravity index score. In block 202, the amount of data to be stored or held at a given location is determined. As described herein, a given location may be a major city (e.g., Los Angeles) or a geographical region (e.g., California). In some embodiments, a region is defined. To do so, or to determine whether the data is included in a metropolitan area, locale, street, block, region, state, county, country, etc., regions can be defined by or based on latitude and longitude data. In block 204, the amount of data moving at a given location is determined. As described herein, data can move, for example, when it is being transferred from one location to another, or when it is being accessed by a user device. In block 206, the total aggregate bandwidth available for a given location or other network calculations is determined. For example, average network traffic data or peak traffic data over a period of time may be used for the calculation. The period can be any desired period from subseconds (e.g., milliseconds, microseconds, nanoseconds, etc.) to seconds, minutes, hours, days, weeks, months, quartiles, years, decades, etc.). It is also recognized that multiple network calculations may be used. In block 208, the average latency between a given location and all other locations is determined. For example, the amount of data and bandwidth data may be determined based on the automated collection or reporting of metrics from third-party systems, specific analyses performed on servers or systems that store or process data, and third-party systems that provide updated sources for such datasets. The data gravity analysis system 100 may include one or more application programming interfaces ("APIs") and / or communicate with one or more third-party APIs to securely collect data and other relevant information.

[0050] In block 210, a data gravity index score is determined based on the amount of data stored at a given location, the amount of data moving at a given location, the total aggregate bandwidth available at a given location, and the average latency between a given location and all other locations. In some embodiments, the data gravity index score can be calculated using the following formula. Data gravity = (DM*DA*BW) / L 2 In the formula, DM is data mass, DA is data activity, BW is bandwidth, and L is latency. Data mass can represent the amount of data stored or held at a given location. Data activity can represent data in motion (e.g., interaction or movement). In some embodiments, data activity can be an amplifier of data mass. Bandwidth can be the total aggregate bandwidth available at a given location. In some embodiments, bandwidth is a multiplier when calculating data gravity, for example, higher bandwidth can represent more possibilities due to more traffic and utilization (e.g., attracting more services and applications), and lower bandwidth can represent fewer possibilities. Latency can be the average latency between a given location and all other locations. In some embodiments, latency is an impediment to data gravity, for example, higher latency can represent lower possibilities (e.g., attracting more services and applications), and lower latency can represent higher possibilities. For example, a higher data gravity index score may reflect a greater gravitational pull on enterprise data growth in a given domain, while a lower data gravity index score may reflect a weaker pull. Furthermore, latency may be the speed of light, the speed through a solid or hollow fiber optic cable, measured latency, or round-trip time latency on the network.

[0051] In some embodiments, data mass ("DM"), data activity ("DA"), and bandwidth (BW) can be measured as rates. For example, data mass ("DM"), data activity ("DA"), and bandwidth ("BW") can be measured in kilobytes per second, megabytes per second, gigabytes per second, terabytes per second, petabytes per second, exibytes per second, etc. Yes, it is possible. For example, other measurements related to data transfer may be used to calculate the data gravity index score. In some embodiments, latency is measured in milliseconds. However, other measurements, including but not limited to nanoseconds, microseconds, centiseconds, and deciseconds, may be used to quantify latency.

[0052] It is recognized that the data gravity index score may be calculated using other formulas and / or selected based on control factors. For example, the data gravity index score can be calculated using the following formula: Data gravity = ((DM*DA)) 2 *BW) / L 2

[0053] As an additional example, the data gravity index score may be calculated using one of the following formulas. Data gravity = (DM*DA*AT) / L 2 Data gravity = ((DM*DA)) 2 *AT) / L 2 In the formula, AT represents average traffic, peak traffic, or average bandwidth.

[0054] Other formulas for calculating the data gravity index (for example, based on combinations of variables described herein) may include, but are not limited to, the following: Data gravity = (DM*(DA^2))*AT) / L^2 Data gravity = DM / L^2 Data gravity = DA / L^2 Data gravity = AT / L^2 Data gravity = DA*AT / L^2 Data gravity = DM*AT / L^2 Data gravity = DA^2 / L Data gravity = DM^2 / L Data gravity = AT^2 / L In some embodiments, it is recognized that the data gravity index score may be calculated by applying different formulas to different segments and / or by combining one or more formulas. In some embodiments, different variations of the formulas may be applied depending on the specific data gravity type being calculated.

[0055] In some embodiments, various types of rate limits, influences, and / or control variables can be added to the systems, methods, and equations described herein to enable various variations of the data gravity index score calculation. As further described below, the data gravity index score calculation can be used in conjunction with growth rates to perform forecasts or predictions.

[0056] In some embodiments, the data gravity analysis system 100 can calculate a metropolitan data gravity intensity score representing the data gravity intensity of a given metropolitan area (e.g., London). In some embodiments, geographic coordinates, labels, zip codes, or other data sources can be used to determine geographic boundaries and / or filter datasets to focus on datasets within those boundaries.

[0057] In some embodiments, the data gravity analysis system 100 can calculate an inter-metropolitan data gravity index score that represents the data gravity intensity between two metropolitan areas (for example, between Los Angeles and New York City).

[0058] In some embodiments, the data gravity analysis system 100 analyzes the data gravity of a specific industry (e.g., the manufacturing of computers and electronic products) in a large city (e.g., Silicon Valley). It is possible to calculate a gravity index score for industrial metropolitan data, which represents the degree of gravity.

[0059] In some embodiments, the data gravity analysis system 100 can calculate a metropolitan enterprise data gravity index score that represents the data gravity intensity of a specific enterprise (e.g., Walmart or Coca-Cola) within a metropolitan area (e.g., Atlanta or the Research Triangle).

[0060] The data gravity analysis system 100 can provide automated tools for calculating, exploring, planning, and reporting data gravity index scores. Calculation, exploration, planning, and reporting of data gravity index scores can be performed by electronic selection or browsing of one or more features provided in the UI, and the data for calculating data gravity index scores can be stored or maintained in, for example, one or more databases 106 of the data gravity analysis system 100. As described herein, subsystems or modules 104 of the data gravity analysis system 100 can assist with data import / loading and transformation / cleansing / curation when data is ingested into the system 100. Once ingested, the data can be processed by other subsystems or modules 104 (e.g., performing analysis) to assist in calculating data gravity index scores. In some embodiments, data gravity index scores can be post-processed or made available simultaneously for generating reports, etc.

[0061] In some embodiments, the data gravity index score can be visualized to provide further insights. For example, the data gravity index score can be represented as dots on a map, where the size of the dots represents the data gravity index score associated with various regions or metropolitan areas (e.g., larger dots indicate greater data gravity intensity).

[0062] Current backhaul architectures cannot address the following limitations / difficulties related to data gravity, for example: (1) limited data exchange across multiple internal and / or external platforms, (2) maintaining local data copies for data compliance, and (3) limitations on simultaneous multi-dataset analysis in a high-performance manner at a global point of presence. Therefore, in some embodiments, the data gravity analysis system 100 can provide techniques to overcome one or more of these limitations / difficulties.

[0063] Extensive data center architecture Figure 3A shows one embodiment of the broad data center architecture 300. In some embodiments, the broad data center architecture 300 can address problems caused by data gravity, for example, by providing a connected community approach between enterprises, connectivity, and cloud and content providers. For example, the broad data center architecture 300 can integrate the core (e.g., data lake), cloud (e.g., data archive), and edge (e.g., data ingestion) at the center of data exchange. In another example, the broad data center architecture 300 can implement a secure, hybrid IT and data-centric architecture globally at points of presence of the business. In some embodiments, the broad data center architecture 300 can collect data used by the data gravity analysis system 100 to determine a data gravity index score.

[0064] In some embodiments, the broad data center architecture 300 may, for example, reverse traffic flow and, for example, enable users, networks, and the cloud, By providing tools and technologies that can be brought to beta-hosted enterprise data, barriers associated with data gravity can be removed. In other words, the broad data center architecture 300 can, for example, place data at the center of the architecture, leverage user interconnection, and bring the cloud and users to the data. In doing so, the broad data center architecture 300 can provide enterprises and service providers with a secure and neutral meeting place to host their infrastructure that is close but isolated from one another. In some embodiments, the broad data center architecture 300 may be a multi-tenant data center platform.

[0065] In some embodiments, the broad data center architecture 300 can reduce enterprise risk, for example, by enabling more secure data exchange. Optionally, the broad data center architecture 300 can reduce costs associated with bandwidth (e.g., maintaining or increasing bandwidth) and infrastructure (e.g., maintaining redundant infrastructure). Optionally, the broad data center architecture 300 can increase enterprise revenue, for example, by enabling unlimited (e.g., geographically) data analytics performance. Furthermore, such analytics can be partitioned or siloed (e.g., physically or virtually) to prevent the sharing of analytics and / or data from specific clients or under specific permissions with other clients, or sharing outside of permitted permissions.

[0066] In the exemplary embodiment shown in Figure 3A, the extensive data center architecture 300 electronically communicates with a data gravity analysis system 100 that receives information about various data centers and then generates a data gravity index score. The extensive data center architecture 300 may also include a network hub 302, a control hub 304, a data hub 306, and a workflow interconnection system 308. The network hub 302 can consolidate or localize traffic to ingress / exit points to optimize or improve network performance. In some embodiments, the network hub 302 facilitates the exchange of information between the user side and the host side. On the user side, the network hub 302 can access mobile backhaul, virtual private network ("VPN") terminals, Internet of Things ("IoT") gateways, and internet drain. The mobile backhaul may include a transport network connecting the core network and the wireless access network of the mobile network. A VPN allows users to establish a secure connection over an insecure internet that extends the private network, so that they can send and receive data as if their devices were directly connected to the private network. The IoT gateway includes a system that connects IoT devices, equipment systems, sensors, and the cloud. The internet drain is the point where a user leaves the private network of an internet service provider and accesses the router from another network. On the host or service provider side, the network hub 302 can be connected via Multiprotocol Label Switching ("MPLS") links, carrier Ethernet, data center, and cloud interconnects. In this way, the network hub 203 can consolidate and localize traffic at ingress / exit points to improve or optimize network performance and reduce costs.MPLS is a private connection that links data centers and branch offices, typically managed by a service provider that claims to offer specific network performance, quality, and availability. Carrier Ethernet is an application of Ethernet technology that enables network providers to offer Ethernet services to customers and enable the use of Ethernet technology in data centers and cloud interconnects. NECT can be operated by a third-party service provider.

[0067] The data hub 306 can localize data aggregation, staging, analysis, streaming, and data management to optimize or improve data. In some embodiments, data collected and processed at the network hub 302 is delivered to the data hub 306 for further operations. For example, unstructured data may be stored in a data lake. Some data may be stored in a high-performance computing cluster, including nodes that operate in parallel to improve processing speed. Other data may be streamed and analyzed via an integration process that collects data from different sources (after computational storage) into structured datasets and stores them in a centralized data warehouse.

[0068] The control hub 304 can host adjacent security and IT controls, for example, to improve security posture and IT operations. In some embodiments, data from the data hub 306 is then distributed to the control hub 306, where the data may be subject to IT operations or security controls. IT operations may include management processes such as monitoring, logging, virtualization, and management of structured data. Security controls may include administrative security, operational security (operational applications), and physical security (infrastructure). Here, data center infrastructure management ("DCIM") software and portals can be used to assist in controls to improve data security.

[0069] The Workflow Interconnection System 308 can add a Software-Defined Network (SDN) overlay to service change multi-cloud and business-to-business ("B2B") application ecosystems. The Workflow Interconnection System 308 can connect hubs across major cities (e.g., New York, Los Angeles, and Seoul) and regions (e.g., North America, Europe, Southeast Asia, etc.) to enable secure, high-performance distributed workflows. Optionally, the Workflow Interconnection System 308 can enable virtual interconnects tailored to or responding to business needs based on type, speed, destination, time, or ecosystem participants. In some embodiments, the Workflow Interconnection System 308 may be an SX fabric on Digital Realty's PlatformDIGITAL®. The Workflow Interconnection System 308 can support a variety of digital services, including, for example, Software as a Service ("SaaS"), Platform as a Service ("PaaS"), Infrastructure as a Service ("IaaS"), and Location as a Service ("LaaS"). SaaS includes a delivery model in which software is licensed on a subscription basis and centrally hosted by or on behalf of a provider. PaaS is a type of cloud computing offering in which a service provider provides a platform to a client, enabling the client to develop, run, and manage business applications without having to build and maintain the infrastructure that such software development processes typically require. IaaS includes online services that provide APIs used to access components of underlying network infrastructure that can be provided to clients on demand, so that the infrastructure resources are owned by the service provider.LaaS is a location data delivery model in which privately protected physical location data, obtained through multiple sources including carriers, Wi-Fi, IP addresses, and landlines, is made available to enterprise customers via APIs. The Workflow Interconnection System 308 may be used to service-chain multi-cloud and B2B application ecosystems and connect hubs across metropolitan and regional areas to enable secure, high-performance distributed workflows.

[0070] In some embodiments, the network hub 302, control hub 304, data hub 306, and workflow interconnection system 308 function collectively under a broader data center architecture 300. The network hub 302 may be directly connected to the workflow interconnection system 308 to provide provisioned virtual interactions tailored to business needs based on type, speed, destination, time, or ecosystem participants. The data hub 306 may be connected to the network hub 302, so that the network hub 302 inputs static data into the data hub 306 for further operation, and the data hub 306 outputs and returns dynamic data to the network hub 302. The control hub 304 may be connected to the network hub 302 for policy inspection and enforcement.

[0071] Figure 3B shows another embodiment of the extensive data center architecture 300, including the data gravity analysis system 100.

[0072] In some embodiments, the broad data center architecture 300 can recommend data ingestion techniques. These recommendations may be automated and based on one or more factors, such as data type, data age, data volume, and data location. In some embodiments, machine learning may be used to provide predictive analytics that indicate recommendations on how such data can be ingested and / or stored. Machine learning may include artificial intelligence such as neural networks, genetic algorithms, and clustering.

[0073] In some embodiments, the broad data center architecture 300 can facilitate data rotation of obsolete data (e.g., archiving, downgrading, or deprioritizing). By monitoring and tracking data access and exchange history, the broad data center architecture 300 can identify, for example, obsolete data that is no longer current or outdated. Once obsolete data is identified, the broad data center architecture 300 can, for example, deprioritize the obsolete data and limit or reduce the amount of resources consumed, such as maintaining hard copies of the obsolete data for legal compliance or transferring the obsolete data to another location to facilitate user access. Automation tools such as scripts, backend processes, and data migration processes can be used to update, filter, store, migrate, and / or delete obsolete data.

[0074] In some embodiments, the broad data center architecture 300 can streamline existing IT portfolios by, for example, integrating legacy data centers, introducing new regional IT hosting zones, and standardizing infrastructure deployments. In doing so, the broad data center can advantageously, for example, increase the speed at which distributed IT is deployed, reduce the number of data center vendors, and implement a flexible infrastructure.

[0075] In some embodiments, the extensive data center architecture 300 can rewire the network, for example, by implementing local network inlets / outlets, optimizing / improving network segments / topologies, and instantiating multi-cloud connectivity. In doing so, the extensive data center 300 can advantageously, for example, reduce latency and improve throughput, increase bandwidth per employee, provide high-performance multi-cloud connectivity, and improve global traffic management. In some embodiments, the extensive data center architecture 300 can implement changes in response to analysis provided by the data gravity analysis system 100. It can automatically generate and command commands (internal or external).

[0076] In some embodiments, the broad data center architecture 300 can implement hybrid IP control, for example, by implementing ingress / exit control points, hosting the IT and security stack at the ingress / exit points, and simplifying infrastructure management. In doing so, the broad data center 300 can advantageously reduce IT vulnerability points, improve security posture, reduce operational complexity, and securely connect SaaS-based security and operational services directly.

[0077] In some embodiments, the broad data center architecture 300 can optimize or improve data exchange by, for example, performing data staging / aggregation, integrating public / private data sources, and hosting data and analytics adjacent to network inlet / outlet points. In doing so, the broad data center 300 can advantageously optimize or improve data exchange between users, things, networks, and the cloud, maintain data compliance and authority, enable real-time intelligence across workflows, and address the challenges of data gravity.

[0078] In some embodiments, the broad data center architecture 300 can interconnect global workflows through service chain multicloud and B2B applications, interconnect inter-city and inter-regional hubs, and directly connect digital ecosystems locally and globally. In doing so, the broad data center 300 can advantageously enable, for example, SDN-based interconnection, dynamically connect workflow participants and services, securely integrate digital ecosystems globally, and enable secure B2B workflow collaboration.

[0079] In some embodiments, the broad data center architecture 300 may include tools for understanding and complying with regulatory compliance, security, privacy, and import / export rules from different jurisdictions (e.g., states and countries). For example, the broad data center architecture 300 may include one or more rule-based systems that store rules for different jurisdictions and / or validity periods, and then overlay those rules onto data and analytics to generate recommendation or instruction packages on how to reallocate or update data. In some embodiments, the broad data center architecture 300 may include a service that provides automated recommendation reports delivered to customers based on changes in the data gravity index over time. For example, recommendations may include one or more of the following based on the data gravity index number: increasing capacity in region X, increasing capacity in metropolitan Y, rerouting data from region X to region Y, increasing bandwidth or adding redundant routes between metropolitan Y and metropolitan Z, creating new routes from metropolitan Y to metropolitan Z, changing security controls in metropolitan Z, or rebalancing stored data between region X and region B.

[0080] In some embodiments, the broad data center architecture 300 can generate and provide data gravity index score predictions based on historical data. The predictions can be used to provide recommendations to users (e.g., companies) based on, for example, expected growth areas (e.g., regions, metropolises, or countries where data is accumulating rapidly). The predictions may include various industry predictions, such as the predicted increase in data gravity for different industries, such as banking and financial services, pharmaceuticals, mining, and natural resources.

[0081] In some embodiments, the growth rate (e.g., economic growth rate, firm growth rate, or industry growth rate) is used. The annual growth rate ("YoY growth") index, CAGR, and other factors described herein can be used to forecast or predict changes in the data gravity index score of a region, country, city, or geographic area over a period of time (e.g., one week, one month, one year, ten years, etc.). The data used for forecasting or predicting may be provided by a third-party system (e.g., a database or server). As described herein, such forecasts or predictions can drive business decisions, for example, in expanding into another city or region, increasing or maintaining current capacity in light of anticipated demand, or increasing or decreasing the workforce.

[0082] In some embodiments, machine learning, pattern recognition, and inspection, or other methods may be used to predict or forecast changes in the data gravity index score. Machine learning may be used to predict or forecast changes in the data gravity index score with or without the data gravity index formulas described herein. In some embodiments, the broad data center architecture 300 may use the data gravity index to generate and run simulations to assist in planning and projection (e.g., what-if analysis). These simulations may include, for example, impact analysis (e.g., failure X occurred and these were its effects) and remediation (e.g., providing recommended changes to one or more features such as networks, exchanges, etc., based on the impact analysis) along with the expected effects of such changes.

[0083] Figures 3C and 3D show another embodiment of the extensive data center architecture 300, including the data gravity analysis system 100.

[0084] In some embodiments, the data gravity analysis system 100 and / or the broader data center architecture 300 may electronically communicate with other systems or components such as user interface dashboards, mobile applications, third-party platforms, and other applications. Electronic communication may be via direct connection, wireless connection, or API calls. For example, the data gravity analysis system 100 and / or the broader data center architecture 300 may electronically communicate with, or include, artificial intelligence or machine learning systems or rule engines. Various data items may be cleansed, segmented, and / or anonymized in order to perform correlation analysis and / or machine learning on the data in order to provide predictive analytics or to recommend decisions. Electronic communication may be via monolithic services, microservices, or serverless technologies.

[0085] In some embodiments, the data gravity analysis system 100 can apply machine learning to examine forensic data sources or other data sources and supply data for calculations with or without data gravity formulas. The data gravity analysis system 100 can also apply machine learning to search for data gravity indices or subtypes or patterns within indices and create new predictions. Furthermore, the data gravity analysis system 100 can examine predictions and forecasts derived from data gravity information.

[0086] In some embodiments, the data gravity analysis system 100 may trigger or initiate processes or other assignments or automations that are initiated or started by the results of the data gravity index score. Alternatively, the data gravity analysis system 100 may automatically start or initiate workflows by machine learning that looks at the data gravity analysis system 100 or the data gravity index score and other variables. In some embodiments, the data gravity analysis system 100 can look at workflows, pull data from those workflows (e.g., provisioning workflows or deployments), and use that data to create updates to the data gravity index. In some embodiments, data The Data Gravity Analysis System 100 can explore the optimization options described herein in some modeling or evaluations that use the Data Gravity Index in a system such as the Data Gravity Analysis System 100 or Digital Realty's PlatformDIGITAL®.

[0087] Exemplary User Interface Figure 4 shows one embodiment of user interface features that may be available in UI 102 of the data gravity analysis system 100 and / or a broader data center architecture 300. UI 102 may provide controls that allow the user to select one or more zones, such as metropolitan areas, industries, or geographical attributes. UI 102 may provide controls that allow the user to select specific participants, such as user demographics, applications, or infrastructure. UI 102 may provide controls that allow the user to select specific business objectives, such as real estate reviews or a stream of commercial analysis. UI 102 may also include controls that allow the user to select and / or control outputs, such as data gravity analysis, a set of automated recommendations, or a set of forecasts.

[0088] Exemplary segmentation / filtering In some embodiments, the Data Gravity Analysis System 100 and / or the Broad Data Center Architecture 300 may be used on various platforms to ingest and analyze terabytes, petabytes, or even exabytes of data and attributes from locations of global enterprises in different regions, such as those with specific variables in each major city. For example, the objective of one analysis might be to identify potential business expansions in the financial industry, so that the Data Gravity Analysis System 100 and / or the Broad Data Center Architecture 300 can segment the data into specific industries and apply the analysis only to data related to banks and accounting firms in London. In another example, the objective might be to analyze potential business expansions in the healthcare industry, so that the Data Gravity Analysis System 100 and / or the Broad Data Center Architecture 300 can apply different segmentation or filtering to specific industries and apply the analysis only to data related to hospitals, long-term care centers, pharmaceutical companies, and healthcare providers in London. As another example, the analysis could focus on data from all Global 2000 Digital Media Companies locations in Central America, so that the Data Gravity Analysis System 100 and / or the broader Data Center Architecture 300 can segment the data to apply the analysis only to data related to digital media companies in Panama, Costa Rica, Nicaragua, Honduras, El Salvador, Guatemala, and Belize. As yet another example, the analysis could be based on data from specific companies in all regions, so that the Data Gravity Analysis System 100 and / or the broader Data Center Architecture 300 can segment the data to apply the analysis only to data related to specific companies across all countries.

[0089] Combined analysis In some embodiments, a data gravity analysis system 100 and / or a broader data center architecture 300 can be used to analyze larger areas and groups of areas. For example, the data gravity analysis system 100 and / or the broader data center architecture 300 can generate a data gravity index for a specific zone and compare it with the data gravity indexes of other zones. For example, the data gravity index for London may be analyzed together with the data gravity indexes for Amsterdam and Paris. The data gravity analysis system 100 and / or the broader data center architecture 300 can generate a composite data gravity index that includes the indices for London, Amsterdam, and Paris.

[0090] The Data Gravity Analysis System 100 and / or the Broad Data Center Architecture 300 can analyze data gravity from a global perspective to flag areas of potential concern and provide recommendations based on predicted growth areas. For example, artificial intelligence and machine learning can be used to build models for predicting growth areas, which may be trained using data such as data creation rate, storage capacity, processing capacity, industry growth, changes in cloud usage, population growth, and annual deployment rates of enterprise storage (hard disk drives, solid-state drives, and tape storage) for on-premises, service chain, multi-cloud, and B2B applications. Optionally, the Data Gravity Analysis System 100 and / or the Broad Data Center Architecture 300 may also forecast the intensity and pull of enterprise data growth in various regions, acting as a proxy for measuring expected data creation, aggregation, and processing.

[0091] Exemplary Use Case - Planning and Executing Resource Allocation As an example use case, the Data Gravity Analysis System 100 and / or the broader Data Center Architecture 300 can be used to tailor a conglomerate's data storage plan, taking into account plans to expand its operations in London. The data used may include GDP, population, number of employees, technographics, network data related to infrastructure, IT spending data, research and development costs, industry forecasts, firm growth forecasts, and expected employee locations, and may include data from all 2000 global companies in London, such as the British Petroleum Company plc, HSBC, Prudential plc, Legal & General Group, and Aviva. It may also consider competitors or similar companies in adjacent industries, and industry averages or best practices may be examined to consider each of the above data points.

[0092] The data gravity analysis system 100 and / or the broader data center architecture 300 may include electronic tools for segmenting or filtering data, for example, for a specific industry, such as the financial sector in this example. Other types of attributes, such as geographical sub-regions, may also be used to segment or filter data.

[0093] A data gravity analysis system 100 and / or a broader data center architecture 300 can use one or more of the above formulas to analyze data and determine a data gravity index. Various variations of the formulas can be used to calculate various types of data gravity indices. For example, if bandwidth is not considered, the following formula may be used: Data gravity = (Data mass * Data activity) / Latency^2

[0094] The data gravity analysis system 100 and / or the broader data center architecture 300 can automatically generate customized recommendations for resource allocation to support business expansion, such as expected data gravity indices, as well as recommended data storage footprints that take into account business needs, workflow profiles, and workload attributes, and expected employee interconnections. For example, it can also provide expansion forecasts to determine what the expected future data load will be and to understand regional network capacity and storage parameters (e.g., regions for potential business expansion). These forecasts can be used to provide recommendations on how much growth can be supported, where growth will be best, and / or what changes need to be made to increase capacity as needed.

[0095] Based on the recommendations, the data gravity analysis system 100 and / or the broader data center architecture 300 can select specific companies, data centers, and application servers that are permitted to participate in zones determined based on the recommended allocation. In some embodiments, parameters may be automatically selected and implemented using the broader data center architecture 300 or other systems.

[0096] In one embodiment, a data gravity analysis system 100 and / or a broader data center architecture 300 ("System") can perform resource allocation analysis via one or more processors, for example, as shown in the embodiment of Figure 5.

[0097] In block 510, the system begins by ingesting data from an external source. The system may include a user interface that allows the user to select a set of data, apply one or more filters to the data, restrict the data selection to one or more segments, perform data cleansing, and / or set parameters to link data from different datasets. In some embodiments, the system may receive data directly from a third-party system, make calls to request data from a third-party system, or receive batch data updates. The system may aggregate data from various sources and / or provide automatic linking of data to create logical relationships between different datasets. As an example, the system may be used to evaluate healthcare data within New Mexico.

[0098] In block 520, the system calculates the data gravity index using the following formula and selected dataset. ((Data mass * Data activity * Bandwidth) / (Latency^2) Here, bandwidth is measured as the amount of data that can be transferred from one point to another within a network in a given amount of time, and latency is the time it takes for a data packet to be transmitted and acknowledged. Bandwidth can be expressed as bitrate and measured in bits per second (bps). The data gravity index can be calculated on various sub-regions within a selected area (e.g., a city, a county, etc.), and then the data gravity index can be determined using the data gravity scores on the sub-regions. For example, the system may determine that the data gravity index for New Mexico is 158,306 based on the data gravity scores of each county in New Mexico.

[0099] In block 530, the system analyzes the data gravity index to determine capacity in various areas. For example, the data gravity index may show that while New Mexico as a whole has capacity, Bernarillo County's high data gravity score indicates that its capacity is insufficient for significant growth without additional storage and infrastructure.

[0100] In block 540, the system analyzes data such as current workflow patterns, data generation patterns, data transmission patterns, and data deletion patterns. This analysis can be performed by analyzing data from specific users, entities, applications, storage / work centers, etc., along with time data, so that the broad data center architecture 300 can determine the current and expected workflows and expected times for such workflows.

[0101] In block 550, the system automatically generates recommendations for a set of resource allocation plans. For example, the system aggregates data that would normally be stored in Bernarillo County into adjacent counties such as Valencia County, Torrance County, Santa Fe County, and Chibolla County. It can be recommended to do so. The system can indicate that Sandoval County is a neighboring county, but its data gravity score is moderately high, so that data concentration in that county is not recommended.

[0102] Optionally, the system can analyze the provided consumption data to recommend specific network and infrastructure purchases. For example, the system could recommend Bernarillo and Sandoval counties to lease additional capacity from a cloud provider or multi-tenant provider.

[0103] Optionally, the system can provide a recommended data routing scheme. For example, the system could also indicate that because Torrance County has a moderate data gravity score, data should only be concentrated in Torrance County for six months, and after six months, the data collected in Torrance County should be collected in Valencia County.

[0104] It is recognized that various analyses may be used, for example, to recommend deleting data from a specific data center via offsite archiving, rerouting workflows to use a specific network or data center, recommending increased storage capacity and networking technology, moving processing to different locations to facilitate work as data volumes increase, and moving capacity to address changes based on events (current or future), such as sporting events, which increase data output from IoT or mobile devices near a specific location.

[0105] In block 560, the system collects data after a recommendation has been made and uses that data for its analysis. This feedback loop may be used to retrain a machine learning model or to determine the level of variance between the predicted and actual results. This feedback loop may also be used to generate an updated recommendation. For example, the system may determine that its prediction regarding Torrance County's capacity is incorrect and that excess capacity is being depleted faster than expected, and as a result, the system generates an updated recommendation to automatically stop concentrating data in Torrance County after 3 months (instead of 6 months).

[0106] While resource allocation examples are provided, it is recognized that numerous use cases may be utilized. For example, the data gravity index can be used to determine data archiving plans, recommend hardware spending, recommend cloud computing rentals, determine growth area options, provide warnings of potential system failures due to capacity overload, plan new residential communities, plan new commercial communities, recommend multi-tenant data center storage, plan data processing capacity needs, and plan network capacity and routing needs. Furthermore, analysis may be performed at other levels, such as across multiple metropolitan areas, states, countries, or regions, for a more comprehensive analysis.

[0107] Exemplary Processing System In some embodiments, any of the systems, devices, servers, or components referenced herein may take the form of a computing system, as shown in Figure 6, which illustrates a block diagram of one embodiment of a certain computing system 600. An exemplary computing system 600 includes a processor 610, which may include one or more conventional microprocessors having hardware circuits that read computer executable instructions and cause parts of the hardware circuits to perform operations specifically defined by those circuits. The computing system may also include memory 630, such as random access memory ("RAM") for temporary storage of information and read-only memory ("ROM") for persistent storage of information, which can store some or all of the computer executable instructions before they are communicated to the processor for execution. The computing system may also include one or more mass storage devices 640, such as a hard drive, diskette, CD-ROM drive, DVD-ROM drive, or optical media storage device, which can store computer executable instructions for a relatively long period of time, including when the computer system is turned off. Typically, modules of the computing system are connected using a standards-based bus system. In different embodiments, the standards-based bus system may be, for example, Peripheral Interconnect ("PCI"), Microchannel, Small Computer System Interface ("SCSI"), Industry Standard Architecture ("ISA"), and Extended ISA ("EISA") architectures. Furthermore, the functions provided in the components and modules of the computing system may be combined into fewer components and modules, or further separated into additional components and modules. The illustrated structure of the computing system 600 may also be used to implement other computing components and systems described herein. It is recognized that the components described herein may be implemented as various types of components. For example, a server may be implemented as a module running on a computing device, a mainframe may be implemented on a non-mainframe server, a server or other computing device may be implemented using two or more computing devices, and / or various components may be implemented using a single computing device.

[0108] Furthermore, various embodiments may be used, and it is recognized that some of the blocks in Figure 6 can be combined, separated into subblocks, and rearranged to be executed in different orders and / or in parallel.

[0109] In one embodiment, the computing system 600 is a server, workstation, mainframe, or minicomputer. In other embodiments, the system may be an IBM, Macintosh, or Linux® / Unix compatible personal computer, laptop computer, tablet, handheld device, mobile phone, smartphone, smartwatch, personal digital assistant, car system, tablet, or other user device. The server may include various types of servers such as a database server (e.g., Oracle, DB2, Informix, Microsoft SQL Server, MySQL, or Ingres), application server, data loader server, or web server. Furthermore, the server may run various software for data visualization, distributed file systems, distributed processing, web portals, enterprise workflows, form management, etc.

[0110] The computing system 600 may generally be controlled and tuned by operating system software such as, for example, Windows 7, Windows 8, Windows 10, Unix, Linux (and its variations, e.g., Debian, Linux Mint, Fedora, and Red Hat), SunOS, Solaris, Maemo, MeeGo, BlackBerry tablet OS, Android, webOS, Sugar, Symbian OS, MAC OS® X, or iOS, or other operating systems. In other embodiments, the computing system 600 may be controlled by a proprietary operating system. Conventional operating systems control and schedule computer processes for execution, perform memory management, and manage file systems, networks, etc. It provides I / O services, and in particular, user interfaces such as graphical user interfaces ("GUI").

[0111] An exemplary computing system 600 includes one or more commonly available input / output ("I / O") devices and interfaces 620, such as a keyboard, mouse, touchpad, speaker, microphone, or printer. In one embodiment, the I / O devices and interfaces 620 include one or more display devices, such as a touchscreen, display, or monitor, which enable the visual presentation of data to a user. More specifically, the display devices provide, for example, a GUI, application software data, and a multimedia presentation. The processor 610 can communicate with the display devices, which can perform some of the functions defined by computer executable instructions. For example, some computer executable instructions could define an operation to display an image on the display device, such as one of the screenshots included in this application. The computing system may also include one or more multimedia devices 650, such as a speaker, video card, graphics accelerator, and microphone. Those skilled in the art will understand, in light of this disclosure, that a system including all the hardware components, such as the processor 610, display devices, memory 630, and mass storage device 640, necessary to perform the operations shown in this application is within the scope of this disclosure.

[0112] In the embodiment shown in Figure 6, the I / O devices and interfaces provide communication interfaces to various external devices and systems. The computing system may be electronically coupled to a network 670, which includes one or more of the following: a LAN, WAN, the Internet, or a cloud computing network, via, for example, wired, wireless, or a combination of wired and wireless communication links. The network communicates with various systems or other systems 680 and various data sources 690 via wired or wireless communication links.

[0113] Information may be provided to the computing system 600 via network 670 from one or more data sources. Network 670 can communicate with other data sources 690 or other computing devices 680, such as a third-party survey provider system or database. Data source 690 may include one or more internal or external data sources. In some embodiments, one or more of the databases or data sources are relational databases such as Sybase, Oracle, Postgres, CodeBase, MySQL, and Microsoft® SQL Server, as well as NoSQL databases (e.g., Couchbase, Cassandra, or MongoDB), flat file databases, entity-relational databases, object-oriented databases, cloud-based databases (e.g., Amazon RDS, Azure SQL, Microsoft Cosmos DB, Azure Database for MySQL, Azure Database for MariaDB, Azure Cache for Redis, Azure Managed Instance for Apache Cassandra, Google Bare Metal Solution for Oracle on Google Cloud, Google Cloud SQL, Google Cloud Spanner, Google Cloud Big Table, Google Firestore, Google Firebase Realtime Database, Google Memorystore, Google MogoDb Atlas, Amazon Aurora, Amazon DynamoDB, Amazon Redshift, Amazon ElastiCache, Amazon Memorystore for Redis) It may be implemented using other types of databases such as yDB, Amazon DocumentDB, Amazon Keyspaces, Amazon Neptune, Amazon Timestream, or Amazon QLDB, or record-based databases.

[0114] In the embodiment shown in Figure 6, the computing system 600 also includes a data gravity analysis module 660 which may be executed by the processor 610 to perform one or more of the processes described herein. The system may include components such as software components, object-oriented software components, class components, task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, or variables, as an example.

[0115] In some embodiments, all functions described herein are performed on a single device. Alternatively, a distributed environment may be implemented in which functions are performed collectively on two or more devices communicating with each other. Furthermore, although one embodiment of the data gravity analysis 660 has been described using a computing system, it is recognized that a user or customer system may also be implemented as a computing system. For example, the processor 610 may include a central processing unit ("CPU") capable of performing the functions described herein. Furthermore, the processor 610 may include a graphics processing unit ("GPU"), a dedicated processor (e.g., a dedicated programmable microprocessor, a field-programmable gate array ("FPGA")), or a custom chip (e.g., an application-specific integrated circuit ("ASIC")) capable of performing some of the calculations or processing necessary to calculate (or predict or forecast) the data gravity index.

[0116] Generally, as used herein, the term “module” refers to logic embodied in hardware or firmware, or a set of software instructions written in a programming language such as Java, Lua, C, or C++, which may have entry and exit points. A software module may be compiled and linked into an executable program, installed in a dynamic link library, or written in an interpreted programming language such as BASIC, Perl, Ruby, or Python. It will be understood that a software module may be called from other modules, from itself, or in response to detected events or interrupts. Software instructions may be embedded in firmware such as an EPROM. It will be further understood that a hardware module may consist of connected logic units such as gates and flip-flops, or a programmable unit such as a programmable gate array or a processor. Modules described herein are preferably implemented as software modules, but may be represented in hardware or firmware. Generally, modules described herein refer to logic modules that, despite their physical configuration or storage, can be combined with other modules or divided into submodules.

[0117] The term "remote" is understood to include systems, data, objects, devices, components, or modules that are not accessible via the local bus and are not stored locally. Therefore, remote data may include systems that are physically stored in the same room and connected to the computing system via a network. In other situations, remote devices may also be located in separate geographical areas, such as different locations or countries.

[0118] List of Exemplary Numbered Embodiments The following is a list of exemplary numbered embodiments. The features listed in the following list of exemplary embodiments can be combined with additional features disclosed herein. Furthermore, additional combinations of features of the invention that are not specifically listed in the following list of exemplary embodiments and do not include the same features as the specific embodiments listed below are disclosed herein. For brevity, the following list of exemplary embodiments does not specify all inventive aspects of the present disclosure. The following list of exemplary embodiments is not intended to specify any important or essential features of the subject matter described herein. 1. A system for evaluating heterogeneous storage of data across multiple storage devices, Processor and Memory and Data gravity analysis configuration module, A knowledge database stored in memory, Computer code stored in memory, which is retrieved from memory and executed by the processor, is then given to the processor: Receiving information about one or more nodes from multiple forensic source submitters, wherein one or more nodes are Network and Mass data storage system and, Data characteristics including data mass, data activity, bandwidth between at least two points, or latency, Data storage parameters, One or more zone indicators, One or more Internet Protocol IP addresses, Associated with, The submitter is a registered contributor when providing evidence of aggregated data storage, and will receive, The processor is used to identify the selected zone indicator received via the user interface, The process involves using a processor to select a subset of nodes based on a selected zone indicator, wherein each node in the subset of nodes is associated with the selected zone indicator. Using a processor, calculate the data gravity index score of a subset of nodes based at least partially on one or more of the data characteristics of each of the selected zone indicators and context-weighted subsets of nodes, Updating the knowledge database with the calculated data gravity index score, The calculated data gravity index score is output to the data gravity analysis configuration module, Computer code to perform this, A system equipped with these features. 2. The system according to Embodiment 1, wherein the data gravity analysis configuration module is configured to automatically generate encrypted data packets, which include automatic recommendations for one or more data storage parameters for one or more nodes on the network based on a calculated data gravity index score, and which is configured to instruct a network module to send encrypted data packets to one or more nodes. 3. The data gravity analysis configuration module calculates the data gravity index score and then stores one or more data for one or more nodes on the network. The system according to embodiment 1 or 2, which is configured to automatically generate automatic parameter recommendation or warning flags. 4. The automatic recommendations are as follows: Identifying old data on one or more nodes and lowering its priority. Performing additional local network access for one or more nodes, Adjusting the bandwidth of one or more nodes, Adjusting the latency of one or more nodes, Adjusting the data distribution between one or more nodes, or Adjusting the data capacity of one or more nodes, The system according to Embodiment 3, comprising at least one of the following. 5. The system according to any one of embodiments 1 to 4, wherein the data gravity analysis configuration module is configured to automatically generate instructions for rendering flagged items on the user interface based on the calculated data gravity index score. 6. The system according to any one of embodiments 1 to 5, wherein the data gravity analysis configuration module is configured to automatically generate and push alerts to one or more remote systems based on the calculated data gravity index score. 7. The system according to any one of embodiments 1 to 6, wherein the data gravity analysis configuration module is configured to automatically generate commands for a remote system based on the calculated data gravity index score. 8. The data gravity index score is calculated using the formula: A system according to any one of Embodiments 1 to 7, calculated according to (((data mass * data activity)^2) * bandwidth) / (latency^2). 9. The system according to Embodiment 8, wherein the respective index scores for data mass, data activity, bandwidth, and latency are calculated for each of one or more nodes, at least in part, based on the corresponding formulas for data mass, data activity, bandwidth, and latency. 10. The data gravity index score is calculated using the formula: (Data mass * Data activity * Bandwidth) / (Latency^2) A system according to any one of embodiments 1 to 9, calculated according to the following: 11. The system according to any one of Embodiments 1 to 10, wherein the data gravity index score is calculated using a machine learning module configured to identify one or more patterns associated with the data characteristics of a subset of nodes. 12. The computer code is further sent to the processor. Using a machine learning model, identify one or more patterns associated with the data characteristics of a subset of nodes. Using a processor via a machine learning model, we calculate a predicted data gravity index score for a subset of nodes based at least partially on one or more patterns. The system according to any one of Embodiments 1 to 11. 13. The system according to Embodiment 12, wherein the predicted data gravity score is calculated without using a formula for calculating the data gravity index score. 14. The computer code is further sent to the processor. The first updated data characteristics associated with a subset of nodes are received. The processor is used to calculate a first updated data gravity index score associated with a subset of nodes, based at least partially on the first updated data characteristics. Receive a second updated data characteristic associated with a subset of nodes. Using the processor, based at least partially on the second updated data characteristics, We then calculate a second updated data gravity index score associated with a subset of nodes. The predicted data gravity index score is calculated based at least partially on the first updated data gravity index score and the second updated data gravity index score. The system according to any one of Embodiments 1 to 13. 15. The computer code is further sent to the processor. Receive one or more data storage parameters from a subset of nodes, Determine one or more patterns associated with one or more data storage parameters. Request updated data characteristics of a subset of nodes based at least partially on one or more patterns. Allow the node subset to receive the requested updated data characteristics. The updated data gravity index is calculated based on at least some of the received updated data characteristics of a subset of nodes. The system described in Embodiment 14. 16. A computer implementation method for evaluating heterogeneous storage of data across multiple storage devices, comprising the following steps: Receiving information about one or more nodes from multiple forensic source submitters, wherein one or more nodes are Network and Mass data storage system and, Data characteristics including at least one of data mass, data activity, bandwidth, or latency, Data storage parameters, One or more zone indicators, One or more Internet Protocol IP addresses, Associated with, The submitter is a registered contributor when providing evidence of aggregated data storage, and will receive, Identifying the selected zone indicator received via the user interface, The selection involves selecting a subset of nodes based on a selected zone indicator, where each node in the subset is associated with the selected zone indicator. Calculate a data gravity index score for a subset of nodes based at least partially on one or more of the data characteristics of the selected zone indicator and the context-weighted subset of nodes, Updating the knowledge database with the calculated data gravity index score, Output the calculated data gravity index score, A computer implementation method, including the steps involved, when implemented by one or more computing devices composed of specific executable instructions for a particular purpose. 17. A specific executable instruction, The system includes an automatic recommendation for one or more data storage parameters for one node on the network based on a calculated data gravity index score, and further includes automatically generating encrypted data packets, configured to instruct a network module to send encrypted data packets to one or more nodes. The computer implementation method described in Embodiment 16. 18. A specific executable instruction, Based on the calculated data gravity index score, one node on the network This further includes automatically generating automatic recommendations for one or more data storage parameters, Computer implementation method according to embodiment 16 or 17. 19. A specific executable instruction, This further includes automatically generating automatic warning flags for one or more data storage parameters for a single node on the network, based on the calculated data gravity index score. A computer implementation method according to any one of embodiments 16 to 18. 20. A specific executable instruction, This further includes automatically generating instructions for rendering flagged items on the user interface based on the calculated data gravity index score. A computer implementation method according to any one of embodiments 16 to 19. 21. A specific executable instruction, This further includes automatically generating and pushing alerts to one or more remote systems based on the calculated data gravity index score. A computer implementation method according to any one of embodiments 16 to 20. 22. A specific executable instruction is expressed as follows: (((Data Mass * Data Activity)^2) * Bandwidth) / (Latency^2) A computer implementation method according to any one of embodiments 16 to 21, further comprising calculating a data gravity index score using 23. A specific executable instruction is expressed as follows: (Data mass * Data activity * Bandwidth) / (Latency^2) A computer implementation method according to any one of embodiments 16 to 22, further comprising using to calculate a data gravity index score. 24. When executed by the processor, the processor must perform at least the following: Receiving information about one or more nodes from multiple forensic source submitters, wherein one or more nodes are Network and Mass data storage system and, Data characteristics including at least one of data mass, data activity, bandwidth, or latency, Data storage parameters, One or more zone indicators, One or more Internet Protocol IP addresses, Associated with, The submitter is a registered contributor when providing evidence of aggregated data storage, and will receive, Identifying the selected zone indicator received via the user interface, The selection involves selecting a subset of nodes based on a selected zone indicator, where each node in the subset is associated with the selected zone indicator. Calculate a data gravity index score for a subset of nodes based at least partially on one or more of the data characteristics of the selected zone indicator and the context-weighted subset of nodes, Updating the knowledge database with the calculated data gravity index score, Output the calculated data gravity index score, A non-temporary computer storage medium that stores computer-executable instructions for performing certain actions. 25. Based on the calculated data gravity index score, one on the network It further stores computer executable instructions for automatically generating encrypted data packets, which include automatic recommendations for one or more data storage parameters for a node and are configured to instruct a network module to send encrypted data packets to one or more nodes. A non-temporary computer storage medium according to Embodiment 24. 26. Further store computer executable instructions that automatically generate automatic recommendations for one or more data storage parameters for a single node on the network based on the calculated data gravity index score. A non-temporary computer storage medium according to Embodiment 24 or 25. 27. Further store computer executable instructions that automatically generate automatic warning flags for one or more data storage parameters for a single node on the network based on the calculated data gravity index score. A non-temporary computer storage medium according to any one of embodiments 24 to 26. 28. Further store computer executable instructions that automatically generate instructions for rendering flagged items on the user interface based on the calculated data gravity index score. A non-temporary computer storage medium according to any one of embodiments 24 to 27. 29. Further store computer executable instructions that automatically generate and push alerts to one or more remote systems based on the calculated data gravity index score. A non-temporary computer storage medium according to any one of embodiments 24 to 28. 30. The following formula: (((Data Mass * Data Activity)^2) * Bandwidth) / (Latency^2) A non-temporary computer storage medium according to any one of embodiments 24 to 29, further storing computer-executable instructions for calculating a data gravity index score using the aforementioned instructions. 31. The following formula: ((Data mass * Data activity * Bandwidth) / (Latency^2) A non-temporary computer storage medium according to any one of embodiments 24 to 30, further storing computer-executable instructions for calculating a data gravity index score using the aforementioned instructions.

[0119] Additional Embodiments Each of the processes, methods, and algorithms described in the preceding sections may be embodied in a code module executed by one or more computer systems or computer processors, including computer hardware, and may be fully or partially automated. The code module may be stored in any type of non-temporary computer-readable medium or computer storage device, such as a hard drive, solid-state memory, or optical disc. The systems and modules may also be transmitted as data signals generated on various computer-readable transmission media, including wireless-based and wired / cable-based media (e.g., as part of a carrier wave or other analog or digital propagation signal), and may take various forms (e.g., as part of a single or multiplexed analog signal, or as multiple individual digital packets or frames). The processes and algorithms may be implemented in application-specific circuits, partially or entirely. The results of the disclosed processes and process steps may be stored permanently or otherwise in any type of non-temporary computer storage, such as volatile or non-volatile storage.

[0120] The various features and processes described above may be used independently of each other or combined in various ways. All possible combinations and partial combinations are This is intended to fall within the scope of this disclosure. Furthermore, in some implementations, certain methods or process blocks may be omitted. The methods and processes described herein are also not limited to any particular sequence, and the blocks or states associated therewith may be executed in other appropriate sequences. For example, the described blocks or states may be executed in an order other than the specifically disclosed order, or multiple blocks or states may be combined into a single block or state. The exemplary blocks or states may be executed in series, in parallel, or in any other way. Blocks or states may be added to or removed from the exemplary embodiments disclosed. The exemplary systems and components described herein may be configured differently from those described. For example, elements may be added, removed, or rearranged compared to the exemplary embodiments disclosed.

[0121] In particular, conditional language such as “can,” “could,” “might,” or “may,” unless otherwise specified or understood to have a different meaning in the context in which they are used, is generally intended to convey that a particular embodiment includes certain features, elements, and / or steps, but other embodiments do not. Therefore, such conditional language is not generally intended to mean that features, elements, and / or steps are required in any way in one or more embodiments, or that one or more embodiments necessarily include logic for determining whether these features, elements, and / or steps should be included in or performed in any particular embodiment, with or without user input or prompting.

[0122] As used herein, the terms “determine” or “determining” encompass a wide range of actions. For example, “determining” can include calculating, computing, processing, deriving, generating, retrieving, looking up (e.g., searching for a table, database, or another data structure) or confirming via a hardware element without user intervention. It can also include receiving (e.g., receiving information) or accessing (e.g., accessing data in memory) via a hardware element without user intervention. Furthermore, it can include resolving, selecting, choosing, or establishing via a hardware element without user intervention.

[0123] As used herein, the terms “provide” or “providing” encompass a wide variety of actions. For example, “providing” could include storing a value in a storage device for subsequent retrieval, transmitting a value directly to a recipient via at least one wired or wireless medium, or transmitting or storing a reference to a value. “Providing” could also include encoding, decrypting, encrypting, deciphering, confirming, or verifying via hardware elements.

[0124] As used herein, the term “message” encompasses a wide variety of formats for communicating information (e.g., sending or receiving). A message may include machine-readable aggregates of information, such as XML documents, fixed-field messages, and comma-separated messages. In some implementations, a message may include signals used to transmit one or more representations of information. Although described in the singular, it will be understood that a message may consist of multiple parts, be transmitted, stored, received, etc.

[0125] As used herein, “receive” or “receiving” may include a specific algorithm for obtaining information. For example, receiving may include sending a request message for information. The request message may be sent over a network as described above. The message may be transmitted in accordance with one or more well-defined machine-readable standards known in the art. The request message may be stateful, in which case the requesting device and the device to which the request is sent maintain state between requests. The request message may be stateless, in which case the request state information is included in the message exchanged between the requesting device and the device serving the request. An example of such state information is a unique token that may be generated by either the requesting or serving device and included in the exchanged message. For example, the response message may include state information indicating which request message caused the serving device to send the response message.

[0126] As used herein, “generate” or “generating” may include specific algorithms for creating information based on or using other input information. Generating may include retrieving input information, such as from memory or as input parameters provided to hardware performing the generation. Once retrieved, generating may include combining the input information. Combining may be performed via specific circuitry that can provide an output indicating the result of the generation. Combining may be performed dynamically, for example, by dynamically selecting an execution path based on the input information and the operating characteristics of a device (e.g., available hardware resources, power level, power supply, memory level, network connectivity, bandwidth, etc.). Generating may also include storing the generated information in a memory location. The memory location may be identified as part of a request message to initiate the generation. In some implementations, generating may return location information that identifies where the generated information can be accessed. Location information may include memory locations, network locations, file system locations, etc.

[0127] As used herein, “activate” or “activating” may mean causing or triggering a mechanical, electronic, or electromechanical change of state in a device. Activating a device can change the device or its associated features from a first state to a second state. In some implementations, activation may include changing a feature from a first state to a second state, such as changing the visual state of the lenses of stereoscopic glasses. Activating may include generating a control message indicating a desired change of state and providing the device with the control message to change its state.

[0128] Any process description, element, or block in the flowcharts described herein and / or shown in the accompanying drawings should be understood as potentially representing a module, segment, or portion of code containing one or more executable instructions for performing a particular logical function or step in the process. As will be understood by those skilled in the art, alternative implementations in which elements or functions may be removed and executed in an order different from the illustrated or described order, including substantially simultaneously or in reverse order, depending on the functions they contain, are included within the scope of the embodiments described herein.

[0129] All of the methods and processes described herein may be embodied in software code modules executed by one or more general-purpose computers and may be partially or fully automated. For example, the methods described herein may be executed by a computing system and / or any other suitable computing device. The method may be executed on a computing device in response to the execution of software instructions or other executable code read from a tangible computer-readable medium. A tangible computer-readable medium is a data storage device that can store data that is readable by a computer system. An example of a computer-readable medium is a readable medium. This includes dedicated memory, random access memory, other volatile or non-volatile memory devices, CD-ROMs, magnetic tapes, flash drives, spin disks (hard drives), and optical data storage devices.

[0130] It should be emphasized that many variations and modifications can be made to the embodiments described above, and that elements of these variations should be understood to be found in other acceptable examples. All such modifications and variations are intended to be within the scope of this disclosure. The above description details specific embodiments. However, it will be understood that, no matter how detailed the foregoing description may be, the systems and methods can be implemented in many ways. Also, as stated above, the use of specific terms when describing particular features or aspects of the systems and methods should not be construed as meaning that the terms are redefined herein to include any particular characteristics of the features or aspects of the systems and methods to which the terms relate.

Claims

1. A system for evaluating heterogeneous storage of data across multiple storage devices, Processor and Memory and Data gravity analysis configuration module, The knowledge database stored in the aforementioned memory, Computer code stored in the memory, which, when retrieved from the memory and executed by the processor, results in the following being sent to the processor: Receiving information about one or more nodes from multiple forensic source submitters, wherein the one or more nodes are Network and Mass data storage system and, Data characteristics including data mass, data activity, bandwidth between at least two points, or latency, Data storage parameters, One or more zone indicators, One or more Internet Protocol IP addresses and Associated with, The aforementioned submitter is a registered contributor when providing evidence of aggregated data storage, and receives, Using the aforementioned processor, the selected zone indicator received via the user interface is identified, Using the aforementioned processor, select a subset of nodes based on the selected zone indicator, wherein each of the nodes in the subset of nodes is associated with the selected zone indicator. Using the processor, calculate a data gravity index score for the subset of nodes based at least partially on one or more of the selected zone indicators and the data characteristics of each of the context-weighted subset of nodes; The knowledge database is updated with the calculated data gravity index score. Toto, The calculated data gravity index score is output to the data gravity analysis configuration module. Using the processor, automatically generate automatic recommendations for one or more data storage parameters for one or more nodes on the network based on the calculated data gravity index score, Computer code to perform this, Equipped with, The aforementioned automatic recommendations are as follows: Identifying old data on one or more of the aforementioned nodes and lowering its priority, Performing additional local network access for one or more of the aforementioned nodes, Adjusting the bandwidth of one or more of the aforementioned nodes, Adjusting the latency of one or more of the aforementioned nodes, Adjusting the data distribution between one or more of the aforementioned nodes, or Adjusting the data capacity of one or more of the aforementioned nodes, A system that includes at least one of the following.

2. The system according to claim 1, wherein the data gravity analysis configuration module is further configured to automatically generate encrypted data packets, which include automatic recommendations for one or more data storage parameters for one or more nodes on the network based on the calculated data gravity index score, and which are configured to instruct a network module to transmit encrypted data packets to one or more nodes.

3. The system according to claim 1 or 2, wherein the data gravity analysis configuration module is further configured to automatically generate instructions for rendering flagged items on a user interface based on the calculated data gravity index score.

4. The system according to any one of claims 1 to 3, wherein the data gravity analysis configuration module is further configured to automatically generate and push alerts to one or more remote systems based on the calculated data gravity index score.

5. The system according to any one of claims 1 to 4, wherein the data gravity analysis configuration module is further configured to automatically generate commands for a remote system based on the calculated data gravity index score.

6. The aforementioned data gravity index score is given by the formula: (((Data mass * Data activity)^2) * Bandwidth) / (Latency^2) The system according to any one of claims 1 to 5, calculated according to the following:

7. The system according to claim 6, wherein the index score for each of the data mass, data activity, bandwidth, and latency is calculated for each of the one or more nodes, at least in part, based on the corresponding formulas for the data mass, data activity, bandwidth, and latency.

8. The aforementioned data gravity index score is given by the formula: (Data mass * Data activity * Bandwidth) / (Latency^2) The system according to any one of claims 1 to 5, calculated according to the following:

9. The system according to any one of claims 1 to 8, wherein the data gravity index score is calculated using a machine learning module configured to identify one or more patterns associated with the data characteristics of a subset of the nodes.

10. The computer code further provides the processor with: A machine learning model is used to identify one or more patterns associated with the data characteristics of a subset of the nodes. The processor is used via the machine learning model to calculate a predicted data gravity index score for a subset of the nodes, based at least partially on one or more patterns. The system according to any one of claims 1 to 9.

11. The system according to claim 10, wherein the predicted data gravity index score is calculated without using a formula for calculating the data gravity index score.

12. The computer code further provides the processor with: The first updated data characteristics associated with a subset of the aforementioned nodes are received. Using the processor, calculate a first updated data gravity index score associated with a subset of the nodes, based at least partially on the first updated data characteristics. To receive a second updated data characteristic associated with a subset of the aforementioned nodes, Using the processor, a second updated data gravity index score associated with a subset of the nodes is calculated, at least partially based on the second updated data characteristics. An updated predicted data gravity index score is calculated based at least partially on the first updated data gravity index score and the second updated data gravity index score. The system according to any one of claims 1 to 9.

13. The computer code further provides the processor with: The node receives one or more data storage parameters from a subset of the aforementioned nodes. Determine one or more patterns associated with the one or more data storage parameters. Based at least partially on one or more of the above patterns, the updated data characteristics of the subset of the nodes are requested. The subset of the nodes receives the requested updated data characteristics. The updated data gravity index score is calculated based on at least a portion of the received updated data characteristics of the subset of the nodes. The system according to claim 12.

14. A computer implementation method for evaluating heterogeneous storage of data across multiple storage devices, comprising the following steps: Receiving information about one or more nodes from multiple forensic source submitters, wherein the one or more nodes are Network and Mass data storage system and, Data characteristics including at least one of data mass, data activity, bandwidth, or latency, Data storage parameters, One or more zone indicators, One or more Internet Protocol IP addresses and Associated with, The aforementioned submitter is a registered contributor when providing evidence of aggregated data storage, to receive, Identifying selected zone indicators received via the user interface, Selecting a subset of nodes based on the selected zone indicator, wherein each node in the subset of nodes is associated with the selected zone indicator. Calculating a data gravity index score for a subset of nodes based at least partially on one or more of the selected zone indicators and the data characteristics of each of the context-weighted subset of nodes, The knowledge database is updated with the calculated data gravity index score. Output the calculated data gravity index score, and Based on the calculated data gravity index score, automatically generate automatic recommendations for one or more data storage parameters for one node on the network. If implemented by one or more computing devices consisting of specific executable instructions for, those steps include, The aforementioned automatic recommendations are as follows: Identifying old data on one or more of the aforementioned nodes and lowering its priority, Performing additional local network access for one or more of the aforementioned nodes, Adjusting the bandwidth of one or more of the aforementioned nodes, Adjusting the latency of one or more of the aforementioned nodes, Adjusting the data distribution between one or more of the aforementioned nodes, or Adjusting the data capacity of one or more of the aforementioned nodes, A computer implementation method that includes at least one of the following.

15. The aforementioned specific executable instruction, The system includes, based on the calculated data gravity index score, automatic recommendations for one or more data storage parameters for one node on the network, and further includes automatically generating encrypted data packets, configured to instruct a network module to send encrypted data packets to the one or more nodes. The computer implementation method according to claim 14.

16. The aforementioned specific executable instruction, The further includes automatically generating an automatic warning flag for one or more data storage parameters for one node on the network based on the calculated data gravity index score, The computer implementation method according to claim 14 or 15.

17. The aforementioned specific executable instruction, The further includes automatically generating instructions for rendering flagged items on the user interface based on the calculated data gravity index score, The computer implementation method according to any one of claims 14 to 16.

18. The aforementioned specific executable instruction, The further includes automatically generating and pushing alerts to one or more remote systems based on the calculated data gravity index score, The computer implementation method according to any one of claims 14 to 17.

19. The aforementioned specific executable instruction is expressed as follows: (((Data mass * Data activity)^2) * Bandwidth) / (Latency^2) The computer implementation method according to any one of claims 14 to 18, further comprising calculating the data gravity index score using the method.

20. The aforementioned specific executable instruction is expressed as follows: (Data mass * Data activity * Bandwidth) / (Latency^2) The computer implementation method according to any one of claims 14 to 18, further comprising calculating the data gravity index score using the method.

21. When executed by the processor, the processor will have at least the following: Receiving information about one or more nodes from multiple forensic source submitters, wherein the one or more nodes are Network and Mass data storage system and, Data characteristics including at least one of data mass, data activity, bandwidth, or latency, Data storage parameters, One or more zone indicators, One or more Internet Protocol IP addresses and Associated with, The aforementioned submitter is a registered contributor when providing evidence of aggregated data storage, and receives, Identifying the selected zone indicator received via the user interface, Selecting a subset of nodes based on the selected zone indicator, wherein each node in the subset of nodes is associated with the selected zone indicator, Calculating a data gravity index score for a subset of nodes based at least partially on one or more of the selected zone indicators and the data characteristics of each of the context-weighted subset of nodes, The knowledge database is updated with the calculated data gravity index score, Output the calculated data gravity index score, Using the aforementioned processor, automatically generate automatic recommendations for one or more data storage parameters for one node on the network based on the calculated data gravity index score, Have them do it, The aforementioned automatic recommendations are as follows: Identifying old data on one or more of the aforementioned nodes and lowering its priority, Performing additional local network access for one or more of the aforementioned nodes, Adjusting the bandwidth of one or more of the aforementioned nodes, Adjusting the latency of one or more of the aforementioned nodes, Adjusting the data distribution between one or more of the aforementioned nodes, or Adjusting the data capacity of one or more of the aforementioned nodes, A non-temporary computer storage medium for storing computer executable instructions, comprising at least one of the following.

22. The system further stores computer executable instructions for automatically generating encrypted data packets, which include automatic recommendations for one or more data storage parameters for one node on the network based on the calculated data gravity index score, and are configured to instruct a network module to send encrypted data packets to the one or more nodes. The non-temporary computer storage medium according to claim 21.

23. The system further stores computer executable instructions that automatically generate automatic warning flags for one or more data storage parameters for one node on the network based on the calculated data gravity index score. A non-temporary computer storage medium according to claim 21 or 22.

24. Based on the calculated data gravity index score, the system further stores computer executable instructions that automatically generate instructions for rendering flagged items on the user interface. A non-temporary computer storage medium according to any one of claims 21 to 23.

25. The system further stores computer executable instructions that automatically generate and push warnings to one or more remote systems based on the calculated data gravity index score. A non-temporary computer storage medium according to any one of claims 21 to 24.

26. The following formula: (((Data mass * Data activity)^2) * Bandwidth) / (Latency^2) A non-temporary computer storage medium according to any one of claims 21 to 25, further storing computer-executable instructions for calculating the data gravity index score using the aforementioned data gravity index score.

27. The following formula: ((Data mass * Data activity * Bandwidth) / (Latency^2) A non-temporary computer storage medium according to any one of claims 21 to 25, further storing computer-executable instructions for calculating the data gravity index score using the aforementioned data gravity index score.

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