Storage Remapping via Pre-computed Mapping Lists
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Solution Overview
Problem
Current data storage management systems are inefficient in accessing historical data, as they often require on-demand computation to locate backup data, slowing down I/O requests for historical data access.
Innovation Solution
Implementing a remapping technique that uses pre-computed and cached region maps to facilitate faster access to historical data by identifying storage locations and generating mapping lists for zones with anticipated access requests, and updating these lists to reflect changes in backup data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If on-demand computation is used to locate backup data, then storage management flexibility is maintained, but access speed to historical data deteriorates
Solution Approach 1:
The patent pre-computes and stores mapping lists that directly associate historical data addresses with their backup locations in the time store. When access requests are received, the system performs simple lookups in these pre-computed mapping lists rather than computing backup locations on-demand. This preliminary action of creating and storing the mapping relationships resolves the contradiction by enabling fast access while maintaining manageable system complexity through structured data organization.
2Productivity
If mapping lists are cached for all zones, then access efficiency improves, but storage space consumption increases
Solution Approach 1:
The patent implements selective caching of mapping lists based on access patterns. Instead of caching all mapping lists uniformly, the system identifies and caches only those mapping lists corresponding to zones with high anticipated access requests. This local quality approach optimizes storage space utilization by allocating cache resources to the most frequently accessed zones, thereby improving overall data access efficiency without proportionally increasing total storage space consumption.
Solution Approach 2:
The system manages cached mapping lists by deleting least recently used (LRU) lists when storage space becomes constrained. This discarding and recovering mechanism allows the system to dynamically adjust the cached mapping lists based on available storage space and changing access patterns, maintaining optimal data access efficiency while adapting to varying storage space availability.
Data Source
AI summary
A technique for remapping data in a storage management system is disclosed. In one particular exemplary embodiment, the technique may be realized as a method for remapping data in a storage management system having backup data that enable restoration of historical data of a storage system. The method may comprise receiving at least one access request for a first set of historical data. The method may also comprise identifying a second set of historical data, wherein the first set of historical data is a subset of the second set of historical data. The method may further comprise identifying, in the storage management system, one or more storage locations containing backup data for the second set of historical data. The method may additionally comprise storing a mapping list of the one or more identified storage locations to facilitate subsequent access requests for any of the second set of historical data.