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Publication Detail
Dynamic scheduling for energy minimization in delay-sensitive stream mining
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Journal Article
  • Authors:
    Ren S, Deligiannis N, Andreopoulos Y, Islam MA, Van Der Schaar M
  • Publication date:
    15/10/2014
  • Pagination:
    5439, 5448
  • Journal:
    IEEE Transactions on Signal Processing
  • Volume:
    62
  • Issue:
    20
  • Status:
    Published
  • Print ISSN:
    1053-587X
Abstract
Numerous stream mining applications, such as visual detection, online patient monitoring, and video search and retrieval, are emerging on both mobile and high-performance computing systems. These applications are subject to responsiveness (i.e., delay) constraints for user interactivity and, at the same time, must be optimized for energy efficiency. The increasingly heterogeneous power-versus-performance profile of modern hardware presents new opportunities for energy saving as well as challenges. For example, employing low-performance processing nodes can save energy but may violate delay requirements, whereas employing high-performance processing nodes can deliver a fast response but may unnecessarily waste energy. Existing scheduling algorithms balance energy versus delay assuming constant processing and power requirements throughout the execution of a stream mining task and without exploiting hardware heterogeneity. In this paper, we propose a novel framework for dynamic scheduling for energy minimization (DSE) that leverages this emerging hardware heterogeneity. By optimally determining the processing speeds for hardware executing classifiers, DSE minimizes the average energy consumption while satisfying an average delay constraint. To assess the performance of DSE, we build a face detection application based on the Viola-Jones classifier chain and conduct experimental studies via heterogeneous processor system emulation. The results show that, under the same delay requirement, DSE reduces the average energy consumption by up to 50% in comparison to conventional scheduling that does not exploit hardware heterogeneity. We also demonstrate that DSE is robust against processing node switching overhead and model inaccuracy.
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