Ecosystem-Aware Storage Arrays for Edge Analytics Bandwidth
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Solution Overview
Problem
Edge computing systems face challenges with bulk data transfers from ecosystem products to secure remote services, leading to increased network bandwidth usage and security risks, as well as difficulties in tracking license and version information, which hampers analytics and license management.
Innovation Solution
Implementing ecosystem-aware storage arrays with edge architecture that perform initial analytics on data from ecosystem products, determining a sufficient amount of data to transmit to a secure remote service for further analysis, thereby reducing network bandwidth usage and enhancing security by keeping data transfers within the customer's environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ecosystem products transfer bulk data to secure remote service for troubleshooting, then the secure remote service can process all data for analytics, but network bandwidth consumption increases and security risks are introduced
Solution Approach 1:
The patent extracts and filters only the necessary data elements (license information, version information, troubleshooting data) from the bulk data, separating them from unnecessary data. This extraction approach allows the secure remote service to receive only essential information for analytics, reducing network bandwidth consumption while maintaining reliable processing capability.
Solution Approach 2:
The ecosystem products perform preliminary data filtering and preparation locally before transmission to the secure remote service. By pre-processing data to identify and extract only relevant information needed for troubleshooting and analytics, the system reduces the volume of data transmitted over the network while ensuring the secure remote service receives sufficient information for reliable processing.
2Reliability
If ecosystem products transfer all data to secure remote service, then complete analytics can be performed, but security risks increase due to external data transmission
Solution Approach 1:
The system extracts only the specific data elements required for secure remote service processing (license status, version information, troubleshooting data) while leaving sensitive operational data within the customer environment. This selective extraction maintains analytics completeness for essential metrics while minimizing security risks by reducing external data exposure.
Solution Approach 2:
The ecosystem products act as intermediaries that filter and prepare data locally before transmission to the secure remote service. This intermediary role ensures that only pre-validated, necessary information is transmitted externally, maintaining security by keeping sensitive processing within the customer environment while still enabling remote analytics.
3Reliability
If a single secure remote service processes all data from ecosystem products, then centralized analytics can be performed, but the service becomes overloaded and analytics slow down
Solution Approach 1:
The patent extracts and transmits only essential data elements (license information, version information, key troubleshooting data) to the secure remote service, removing unnecessary bulk data from the transmission. This extraction reduces the processing load on the centralized service, improving analytics processing speed while maintaining the capability for centralized analytics on critical information.
4Loss of energy
If ecosystem products are not registered with secure remote service, then network bandwidth and security are protected, but license and version tracking cannot be performed
Solution Approach 1:
The system extracts and transmits specific essential information elements (license status, version information) without requiring full bulk data registration. This selective extraction enables the secure remote service to track license and version information while conserving network bandwidth by avoiding transmission of unnecessary operational data.
Data Source
AI summary
Described herein are systems, methods, and machine-readable storage mediums relating ecosystem-aware storage arrays for unified analytics using edge architecture. According to an embodiment, a system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise receiving data associated with one or more edge devices. The operations can further comprise, based on validation of the data associated with the one or more edge devices comprising performing edge analytics, generating a first level analytics report. The operations can further comprise, based on the first level analytics report, determining a sufficient amount of the data according to a sufficiency criterion that defines the sufficient amount of the data that is to be usable by a secure remote service to generate a second level analytics report based on a core analytics analysis of the sufficient amount of the data. The operations can further comprise transmitting the sufficient amount of the data to a secure remote service.