Storage Device Data Segment Hash Caching to Reduce Search Time
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Solution Overview
Problem
Existing storage devices face challenges in optimizing the time required to search for data segments from a data segment cache, particularly due to collisions in hash-based caching systems that lead to inefficient search performance.
Innovation Solution
Implementing a hash table with N buckets in the storage device's data segment cache, where the controller determines a target bucket for caching based on a hash function using a seed value, and relocates data segments when the bucket exceeds a threshold to minimize collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a hash-based data segment cache is used to speed up data retrieval, then search speed is improved, but collisions in hash buckets cause search time to increase
Solution Approach 1:
The patent applies dynamics by making the hash function adaptive rather than static. The system dynamically adjusts the hash function parameters (seed values, polynomial coefficients) based on the distribution of data in the cache buckets. When collisions are detected in certain buckets, the hash function is updated to redistribute data more evenly, transforming the static hashing problem into a dynamic optimization process that continuously improves search performance.
2Productivity
If data segments are cached in a hash table with N buckets, then cache efficiency is improved, but bucket collisions lead to degraded search performance
Solution Approach 1:
The patent implements parameter changes by modifying the hash function's parameters (seed values, polynomial coefficients, modulo operations) to optimize data distribution. The system monitors collision rates in different buckets and adjusts the hash function parameters accordingly. This allows the cache to maintain high efficiency while dynamically preventing search performance degradation caused by collisions.
Solution Approach 2:
The system employs feedback mechanisms by monitoring the distribution of data segments across hash buckets and using this information to adjust the hash function. When collisions are detected, the system feeds this information back into the hash function optimization process, updating parameters to reduce future collisions. This closed-loop approach ensures both cache efficiency and reliable search performance are maintained.
Data Source
AI summary
A storage device may determine a target data segment from among a plurality of data segments, execute a hash function on the target data segment, and cache the target data segment in a data segment cache based on a result of executing the hash function on the target data segment. The data segment cache may be a hash table including N buckets each of which is able to cache one or more data segments. The hash function may be a function which outputs an index of the target bucket based on N, an index of the target data segment, and a seed value.