Storage System Data Relocation Based on Access Frequency
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Solution Overview
Problem
Conventional data storage systems face challenges in efficiently processing input/output requests due to the low priority and frequent disabling of relocation functions, especially under high utilization rates, which leads to suboptimal performance and inability to handle real-time processing.
Innovation Solution
A method that determines the use frequency of data and relocates it from storage means with moderate or low access speeds to those with higher access speeds during read or write requests, ensuring that high-frequency data is stored in faster storage means to enhance access efficiency without affecting input/output traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the relocation function is implemented in conventional storage systems, then data access efficiency is improved by moving frequently accessed data to faster storage means, but the relocation function has low priority and is easily disabled under high utilization rates, causing it to fail in real-time processing scenarios
Solution Approach 1:
The system proactively identifies frequently accessed data patterns and pre-moves data to optimal storage locations before performance degradation occurs. The performance monitoring module continuously tracks access patterns and triggers relocation operations in advance, ensuring the relocation function remains available and effective even under high utilization conditions.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where the performance monitoring module continuously monitors storage system performance and access patterns, and the relocation execution module adjusts data placement based on this feedback. This ensures the relocation function dynamically adapts to changing conditions and maintains high availability by responding to real-time performance metrics.
2Speed
If data is moved between storage means with different access speeds, then access efficiency for frequently used data is improved, but system complexity increases due to multiple storage means management
Solution Approach 1:
The storage system performs self-optimization through automated performance monitoring and data relocation. The system autonomously identifies access patterns and executes relocation operations without requiring manual intervention or complex external management, thereby improving I/O speed while keeping the management complexity handled internally by the system itself.
Solution Approach 2:
The storage system implements a universal data management approach where a single data structure is used across multiple storage means with different access speeds. The performance monitoring and relocation mechanisms work uniformly across all storage devices, managing complexity through a standardized interface while enabling speed optimization by placing data appropriately across different storage media.
Data Source
AI summary
Techniques involve: receiving an input/output request for data stored in a second storage means of a storage system; determining a use frequency of the data; moving the data from the second storage means into a first storage means in the storage system if it is determined that the use frequency is higher than a threshold frequency and the input/output request is a read request, an input/output speed of the first storage means being higher than that of the second storage means; and writing, if it is determined that the use frequency is higher than the threshold frequency and the input/output request is a write request that includes update data, the update data as the data into the first storage means. Accordingly, the storage performance can be improved without affecting the input/output traffic, which not only can reduce the operating cost, but also can improve user experience.