Edge Storage Cartridge with Local Data Analysis
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Solution Overview
Problem
Traditional cloud data storage systems incur high costs due to excessive network traffic and inefficiencies in storing low-access sensor data, as all data is sent to centralized high-performance drives for analysis, leading to high ingress/egress costs and reduced usage.
Innovation Solution
Implementing a low-cost edge storage system with computational power to analyze data locally, retaining raw data on edge devices and sending only analysis results to the cloud, or integrating similar devices within the cloud storage system's cold storage tier, with a cartridge housing multiple data storage devices and a processor to analyze mechanical and data performance concurrently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all sensor data is sent to centralized cloud storage for analysis, then data analysis capability is improved, but network traffic and ingress/egress costs increase significantly
Solution Approach 1:
The system segments data storage and analysis functions across multiple distributed edge devices rather than concentrating them in a single cloud location. Each edge device independently stores and analyzes data locally, dividing the overall system into autonomous functional units that reduce network dependency.
Solution Approach 2:
The system transitions from a single centralized storage dimension to a multi-dimensional distributed architecture where data exists across multiple edge devices simultaneously. This spatial distribution across different physical locations enables local analysis while reducing centralized network traffic.
2Speed
If high-performance cloud drives are used for all data storage, then data access performance is improved, but storage costs increase
Solution Approach 1:
The system applies different storage qualities to different data locations: high-performance storage is used locally at edge devices for frequently accessed data, while lower-cost storage is used for archival purposes. This localized quality differentiation optimizes performance for critical operations while reducing overall storage costs.
Solution Approach 2:
The system uses multiple inexpensive edge storage devices instead of expensive high-performance cloud storage for all data. These edge devices provide sufficient performance for local operations while being more cost-effective, sacrificing some centralized speed for overall system economy.
3Quantity of substance
If multiple separate data storage devices are used in a cartridge, then storage capacity is improved, but device complexity increases
Solution Approach 1:
The system merges multiple separate data storage devices into a single cartridge assembly that is managed as one integrated unit. The cartridge player concurrently engages and coordinates multiple devices, combining their individual capacities while presenting a unified interface that simplifies system management.
Solution Approach 2:
The cartridge player is designed with universal capability to concurrently engage and manage multiple different data storage devices simultaneously. This multi-functional design allows a single player to handle various storage media types and configurations, reducing the need for specialized equipment for each device.
Data Source
AI summary
A data storage system can provide low cost and optimized performance with a cartridge housing multiple separate data storage devices and each of the data storage devices being concurrently engaged by a device player. The device player may have a processor configured to analyze mechanical performance of each data storage device and data performance of data resident in each data storage device.