Non-Volatile Storage Parameter Optimization via Crowdsourced Data
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Solution Overview
Problem
Existing storage systems, such as SSDs, face challenges in optimizing operating parameters to enhance performance and longevity, as determining the optimal settings can be time-consuming and difficult, especially when adjusting parameters in the field.
Innovation Solution
A crowdsourcing system that collects and analyzes performance data from similar memory systems to dynamically select and update operating parameters, using a server to identify optimal settings based on aggregated data from multiple devices, allowing for real-time adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a storage system determines optimal operating parameters through its own internal analysis, then the system can identify suitable parameters, but the process requires a significant amount of time and data
Solution Approach 1:
The patent uses crowdsourced data from multiple storage systems of the same type to create a virtual copy of the parameter optimization process. Instead of each system analyzing its own performance alone, the system copies the optimization approach and applies it using aggregated data from the crowd, significantly reducing the time and data requirements for individual systems.
Solution Approach 2:
The patent merges the optimization efforts of multiple storage systems into a single centralized analysis. By combining data from many systems of the same type, the invention creates a collective intelligence that determines optimal parameters faster and more accurately than any individual system could alone.
2Productivity
If operating parameters are changed in the field, then performance can be optimized, but the process is challenging and complex
Solution Approach 1:
The patent implements a feedback mechanism where storage systems report their performance data to a centralized server, which analyzes the data and returns optimized parameter recommendations. This feedback loop simplifies the field parameter change process by providing automated, data-driven guidance rather than requiring complex manual analysis.
Solution Approach 2:
The patent introduces a centralized server as an intermediary between storage systems and the parameter optimization process. This intermediary handles the complex data analysis and coordination, allowing field systems to simply report performance and receive optimized parameters without managing the complexity themselves.
3Productivity
If more data is collected from multiple storage systems, then optimal parameters can be identified faster, but requires a crowdsourcing infrastructure
Solution Approach 1:
The patent makes the crowdsourcing server multi-functional by using it for both data collection and parameter optimization analysis. The same infrastructure that aggregates performance data from multiple systems also processes this data to identify optimal parameters, eliminating the need for separate specialized systems.
Solution Approach 2:
The patent implements self-service by having storage systems automatically report their own performance data to the crowdsourcing server without requiring manual intervention. The systems self-identify, self-report, and self-receive optimized parameters, reducing the operational complexity despite the distributed infrastructure.
Data Source
AI summary
A system and method for optimization of non-volatile storage operational parameters is provided. The method may utilize a crowdsourcing server to gather performance data for multiple storage devices connected to respective remotely located host systems. The performance data may be transmitted at regular intervals or at predefined events and the crowdsourcing server may aggregate and determine improved operating parameters for each different type of storage device based on the data gathered for multiple instances of that type of storage device. The optimized operating parameters may be selected from pre-stored and pre-qualified operating parameter sets on the respective storage devices that the crowdsourcing server is aware of. The system includes a non-volatile memory and a controller that is configured to log performance data and transmit at least a portion of that logged data to the crowdsourcing server. The controller may retrieve pre-stored operating parameters based on a crowdsourcing server recalibration command.