Dynamic Scheduling for Energy Minimization in Delay-Sensitive Stream Mining
Dynamic Scheduling for Energy Minimization in Delay-Sensitive Stream Mining
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DOI：
10.1109/tsp.2014.2347260
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发表时间：
2014-08
影响因子：
5.4
通讯作者：
Shaolei Ren;N. Deligiannis;Y. Andreopoulos;M. A. Islam;M. Schaar
中科院分区：
文献类型：
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作者：
Shaolei Ren;N. Deligiannis;Y. Andreopoulos;M. A. Islam;M. Schaar
在移动和高性能计算系统上都出现了大量的流挖掘应用，如视觉检测、在线患者监控和视频搜索和检索。这些应用受制于用户交互的响应性(即延迟)限制，同时必须针对能源效率进行优化。现代硬件的功率与性能日益多样化，这为节能带来了新的机遇，同时也带来了挑战。例如，使用低性能处理节点可以节省能量，但可能违反延迟要求，而使用高性能处理节点可以提供快速响应，但可能不必要地浪费能量。现有的调度算法在假设在流挖掘任务的整个执行过程中不断地处理和功率需求的情况下平衡能量和延迟，并且不利用硬件异构性。在本文中，我们提出了一种新的动态调度框架，用于能量最小化(DSE)，该框架利用了这种新兴的硬件异构性。通过优化确定硬件执行分类器的处理速度，DSE在满足平均延迟约束的同时最小化了平均能量消耗。为了评估DSE的性能，我们构建了一个基于Viola-Jones分类器链的人脸检测应用程序，并通过异构机系统仿真进行了实验研究。结果表明，在相同的时延要求下，DSE调度算法的平均能耗比未利用硬件异构性的传统调度算法降低了50%。我们还证明了DSE对处理节点切换开销和模型误差具有较强的鲁棒性。
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.