کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
380286 1437430 2016 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Leveraging a predictive model of the workload for intelligent slot allocation schemes in energy-efficient HPC clusters
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Leveraging a predictive model of the workload for intelligent slot allocation schemes in energy-efficient HPC clusters
چکیده انگلیسی

A proactive mechanism to learn an efficient strategy for adaptive resource clusters is proposed. In contrast to reactive techniques, that rescale the cluster to fit the past load, a predictive strategy is adopted. The cluster incoming workload is forecasted and an optimization problem is defined whose solution is the optimal action according to a utility function. Genetic-based machine learning techniques are used, including multi-objective evolutionary algorithms under the distal supervised learning setup. Experimental evaluations show that the proactive system presented in this work improves either the energetic efficiency or the number of reconfigurations of previous approaches without a loss in the quality of service. Depending on the predictability of the workload, in real world cluster scenarios additional energy savings of up to approximately 40% were measured over the best previous approach, with a 2× factor increment in the number of reconfigurations.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Applications of Artificial Intelligence - Volume 48, February 2016, Pages 95–105
نویسندگان
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