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IO Workload Characterization Revisited: A Data-Mining Approach

Title
IO Workload Characterization Revisited: A Data-Mining Approach
Author
원유집
Keywords
IO workload characterization; storage and operating systems; SSD; clustering; classification
Issue Date
2014-12
Publisher
IEEE
Citation
IEEE Transactions on Computers, 2014, 63(12) P.3026-3038
Abstract
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.
URI
https://ieeexplore.ieee.org/document/6598677/
ISSN
0018-9340; 1557-9956
DOI
10.1109/TC.2013.187
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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