I/O Characteristics Discovery in Cloud Storage Systems
2018 IEEE 11th International Conference on Cloud Computing (CLOUD)(2018)
摘要
The data growth from many applications in clouds poses significant challenges to cloud storage systems. To deliver the best storage and I/O performance possible, it is often required to understand and leverage the I/O characteristics based on data accesses. A number of research studies have been carried out on this topic. However, most of them either utilize a limited number of data-access attributes, restricting the general applicability of the method for different applications, or heavily rely on the domain knowledge or expertise about applications' I/O behaviors to select the best representative features, introducing bias for certain workloads. To overcome these limitations, in this study, we present a new I/O characteristic discovery methodology. This method enables capturing data-access features as many as possible to eliminate human bias. It utilizes a machine-learning based strategy to derive the most important set of features automatically, and groups data objects with a clustering algorithm (DBSCAN) to reveal I/O characteristics discovered. These I/O characteristics revealed can direct I/O performance optimizations in numerous scenarios, such as in data prefeteching and data reorganization optimizations in cloud storage systems.
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关键词
Cloud storage systems, file systems, I/O characteristics discovery
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