Software-directed data access scheduling for reducing disk energy consumption

Yuanrui Zhang, Jun Liu, Mahmut Kandemir

Research output: Contribution to conferencePaper

4 Scopus citations

Abstract

Most existing research in disk power management has focused on exploiting idle periods of disks. Both hardware power-saving mechanisms (such as spin-down disks and multi-speed disks) and complementary software strategies (such as code and data layout transformations to increase the length of idle periods) have been explored. However, while hardware power-saving mechanisms cannot handle short idle periods of high-performance parallel applications, prior code/data reorganization strategies typically require extensive code modifications. In this paper, we propose and evaluate a compiler-directed data access (I/O call) scheduling framework for saving disk energy, which groups as many data requests as possible in a shorter period, thus creating longer disk idle periods for improving the effectiveness of hardware power-saving mechanisms. As compared to prior software based efforts, it requires no code or data restructuring. We evaluate our approach using six application programs in a cluster-based simulation environment. The experimental results show that it improves the effectiveness of both spin-down disks and multispeed disks with doubled power savings on average.

Original languageEnglish (US)
Pages596-605
Number of pages10
DOIs
StatePublished - Oct 5 2012
Event32nd IEEE International Conference on Distributed Computing Systems, ICDCS 2012 - Macau, China
Duration: Jun 18 2012Jun 21 2012

Other

Other32nd IEEE International Conference on Distributed Computing Systems, ICDCS 2012
CountryChina
CityMacau
Period6/18/126/21/12

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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    Zhang, Y., Liu, J., & Kandemir, M. (2012). Software-directed data access scheduling for reducing disk energy consumption. 596-605. Paper presented at 32nd IEEE International Conference on Distributed Computing Systems, ICDCS 2012, Macau, China. https://doi.org/10.1109/ICDCS.2012.12