LRU Data Subgroup Management for Storage Tier Optimization
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Solution Overview
Problem
Existing data management systems in electronic devices lack an efficient method to dynamically prioritize and manage data elements based on access frequency and recency, leading to suboptimal storage and retrieval performance across different storage tiers.
Innovation Solution
Implementing a data structure with least recently used (LRU) lists configured as subgroups, where data elements are tracked using count variables to determine access frequency and recency, and periodically adjusted to reflect recent activity patterns, allowing for dynamic re-prioritization and movement of data elements across storage tiers based on access patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data elements are stored in faster access storage systems, then processing efficiency is improved, but storage costs increase
Solution Approach 1:
The patent segments data elements into multiple subgroups based on access patterns and stores them in different storage tiers. Frequently accessed data elements are placed in faster access storage systems, while less frequently accessed elements are stored in cheaper storage systems. This segmentation allows the system to optimize the balance between processing efficiency and storage costs by distributing data across different storage tiers rather than storing all data in the fastest storage.
Solution Approach 2:
The patent implements dynamic re-prioritization of data elements based on changing access patterns. The system periodically adjusts the storage location of data elements by moving frequently accessed elements to faster storage tiers and less frequently accessed elements to cheaper tiers. This dynamic adjustment ensures that the system continuously optimizes the balance between processing efficiency and storage costs according to actual usage patterns.
2Adaptability or versatility
If data elements are dynamically re-prioritized based on access patterns, then adaptability to changing access patterns is improved, but device complexity increases
Solution Approach 1:
The patent divides data elements into multiple subgroups with different priority levels and stores them in different storage tiers. Each subgroup is managed independently with its own LRU list, allowing the system to handle different access patterns efficiently. This segmentation provides adaptability to changing access patterns while managing complexity through modular organization.
Solution Approach 2:
The patent implements a feedback mechanism where the system periodically monitors access patterns and adjusts the storage location of data elements accordingly. When access patterns change, the system detects these changes and re-prioritizes data elements, moving frequently accessed elements to faster storage tiers. This feedback loop provides adaptability while the periodic nature of the adjustment helps manage system complexity.
Data Source
AI summary
A plurality of subgroups with a least recently used (LRU) list of data elements associated with count variables. The LRU lists have a top entry to store a most recently used data element and a bottom entry to store a least recently used data element. If a data element is accessed, then increase the value of the count variable and move the accessed data element to the top entry of the LRU list of the subgroup associated with the data element. If the value of the count variable of the accessed data element of the top entry is greater than a value of a count variable of a data element of a bottom entry of a LRU list of a subgroup with a higher priority, then swap the data element of the bottom entry with the accessed data element of the top entry.