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Data migration on parallel disks: Algorithms and evaluation

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Abstract

Our work is motivated by the problem of managing data on storage devices, typically a set of disks. Such storage servers are used as web servers or multimedia servers, for handling high demand for data. As the system is running, to exhibit good performance, it needs to respond dynamically to changes in demand for different data items. There are known algorithms for mapping demand to a layout. When the demand changes, a new layout can be computed. In this work we study thedata migration problem, which arises when we need to change one layout to another quickly. This problem has been studied earlier where for each disk a new layout has been prescribed. However, to apply these algorithms effectively, we identify another problem that we refer to as the correspondence problem, whose solution has a significant impact on the overall solution for the data migration problem. We study algorithms for the data migration problem in more detail and identify variations of the basic algorithm that seem to improve performance in practice, even though some of the variations have poor worst-case behavior.

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Authors and Affiliations

  1. CS and EE-Systems Departments, IMSC, University of Southern California, 90089, Los Angeles, CA, USA

    Leana Golubchik

  2. Department of Computer Science and Institute for Advanced Computer Studies, University of Maryland, 20742, College Park, MD, USA

    Samir Khuller, Yoo-Ah Kim & Yung-Chun (Justin) Wan

Authors
  1. Leana Golubchik
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  2. Samir Khuller
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  3. Yoo-Ah Kim
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  4. Svetlana Shargorodskaya
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  5. Yung-Chun (Justin) Wan
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Corresponding author

Correspondence to Leana Golubchik.

Additional information

This research was supported by the NSF Awards CCR-0113192 and EIA-0091474 as well as the Okawa Research Award. This work made use of Integrated Media Systems Center Shared Facilities supported by the National Science Foundation under Cooperative Agreement No. EEC-9529152; any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect those of the National Science Foundation. This work was done while Svetlana Shargorodskaya was at the University of Maryland.

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Golubchik, L., Khuller, S., Kim, YA. et al. Data migration on parallel disks: Algorithms and evaluation. Algorithmica 45, 137–158 (2006). https://doi.org/10.1007/s00453-005-1194-6

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  • DOI: https://doi.org/10.1007/s00453-005-1194-6

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