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      Incorporating Energy-Aware Mechanism into Workflow Scheduling Policy in Heterogeneous Distributed Systems

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      https://www.riss.kr/link?id=A100248612

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      다국어 초록 (Multilingual Abstract)

      In the past decades, plenty scheduling policies have been proposed for improving the execution performance of workflow applications. However, few of them have addressed the issue of energy conservation. In this paper, a novel scheduling metric, namely Minimal Energy Consumption Path, is introduced to reducing the energy consumption when scheduling large-scale workflows. The proposed scheduling metric can be incorporated into current schedulers so as to enhance their capability of energy conservation as well as maintain their scheduling performance. A lot of experiments are performed by using different scheduler and experimental settings to evaluate the proposed scheduling metric and the results indicate it is effective for reducing the storage-related energy consumption, especially when the target workflows are data-intensive.
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      In the past decades, plenty scheduling policies have been proposed for improving the execution performance of workflow applications. However, few of them have addressed the issue of energy conservation. In this paper, a novel scheduling metric, namely...

      In the past decades, plenty scheduling policies have been proposed for improving the execution performance of workflow applications. However, few of them have addressed the issue of energy conservation. In this paper, a novel scheduling metric, namely Minimal Energy Consumption Path, is introduced to reducing the energy consumption when scheduling large-scale workflows. The proposed scheduling metric can be incorporated into current schedulers so as to enhance their capability of energy conservation as well as maintain their scheduling performance. A lot of experiments are performed by using different scheduler and experimental settings to evaluate the proposed scheduling metric and the results indicate it is effective for reducing the storage-related energy consumption, especially when the target workflows are data-intensive.

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      목차 (Table of Contents)

      • Abstract
      • 1. Introduction
      • 2. Related Work
      • 3. Problem Formulation
      • 4. Energy-aware Heuristic Metric
      • Abstract
      • 1. Introduction
      • 2. Related Work
      • 3. Problem Formulation
      • 4. Energy-aware Heuristic Metric
      • 4.1. Energy Consumption Model of Task Execution
      • 4.2. Energy-aware Scheduling Policy
      • 5. Experiments and Performance Comparison
      • 5.1. Experimental Settings
      • 5.2. Comparison of Performance and Analysis
      • 6. Conclusion
      • References
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