IO workload characterization revisited: a data-mining approach

Bumjoon Seo*, Sooyong Kang, Jongmoo Choi, Jaehyuk Cha, Youjip Won, Sungroh Yoon

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


Over the past few decades, IO workload characterization has been a critical issue for operating system and storage community. Even so, the issue still deserves investigation because of the continued introduction of novel storage devices such as solid-state drives (SSDs), which have different characteristics from traditional hard disks. We propose novel IO workload characterization and classification schemes, aiming at addressing three major issues: (i) deciding right mining algorithms for IO traffic analysis, (ii) determining a feature set to properly characterize IO workloads, and (iii) defining essential IO traffic classes state-of-the-art storage devices can exploit in their internal management. The proposed characterization scheme extracts basic attributes that can effectively represent the characteristics of IO workloads and, based on the attributes, finds representative access patterns in general workloads using various clustering algorithms. The proposed classification scheme finds a small number of representative patterns of a given workload that can be exploited for optimization either in the storage stack of the operating system or inside the storage device.

Original languageEnglish
Article number187
Pages (from-to)3026-3038
Number of pages13
JournalIEEE Transactions on Computers
Issue number12
Publication statusPublished - Dec 2014
Externally publishedYes


  • Classification
  • Clustering
  • IO workload characterization
  • SSD
  • Storage and operating systems


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