基于用户行为分析的海量小文件存储系统缓存替换策略

Chenyun Liu, Shun Ding, Liang Ye, Xingyu Chen, Wenhao Zhu
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引用次数: 1

摘要

常见的缓存消除策略是为了提高特定场景下文件的命中率。在实际场景中,不同的用户行为往往表现出很大的差异,通用的缓存替换策略并不能全面实现良好的性能。针对这些问题,本文设计了一种基于用户行为分析的文件系统缓存替换策略(LFU-UB)。首先建立日志分析模块,清除用户访问记录信息,挖掘关联规则,然后将关联参数传递给计算模型。然后通过缓存替换模块选择优先级最低的几个小文件。最后,优先级最低的资源被新资源取代。通过在海量小文件存储环境下的对比实验,验证了LFU-UB策略的有效性;它具有比一般缓存策略更高的命中率,可以有效地减少缓存负载。
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Cache Replacement Strategy Based on User Behaviour Analysis for a Massive Small File Storage System
Common cache elimination strategies are to improve the hit ratio of files in specific scenarios. In real scenarios, different users' behaviours often show great differences, and a general cache replacement strategy cannot comprehensively achieve good performance. Considering these problems, this paper designs a cache replacement strategy based on user behaviour analysis for file systems (LFU-UB). First, a log analysis module is built to clean the user's access record information and mine association rules, and then the association parameters are transmitted to the computing model. Then several small files with the lowest priority are selected through the cache replacement module. Finally, resources with the lowest priority are replaced by new resources. The effectiveness of LFU-UB strategy is proved by comparison experiments in the storage environment of massive small files; It has a higher hit ratio than the general cache strategy and can effectively reduce the cache load.
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