I/O Characteristics Discovery in Cloud Storage Systems

Jiang Zhou, Dong Dai, Yu Mao, Xin Chen, Yu Zhuang, Yong Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Cloud Computing, CLOUD 2018 - Part of the 2018 IEEE World Congress on Services
PublisherIEEE Computer Society
Pages170-177
Number of pages8
ISBN (Electronic)9781538672358
DOIs
StatePublished - Sep 7 2018
Event11th IEEE International Conference on Cloud Computing, CLOUD 2018 - San Francisco, United States
Duration: Jul 2 2018Jul 7 2018

Publication series

NameIEEE International Conference on Cloud Computing, CLOUD
Volume2018-July
ISSN (Print)2159-6182
ISSN (Electronic)2159-6190

Conference

Conference11th IEEE International Conference on Cloud Computing, CLOUD 2018
Country/TerritoryUnited States
CitySan Francisco
Period07/2/1807/7/18

Keywords

  • Cloud storage systems
  • File systems
  • I/O characteristics discovery

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