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Adaptive task scheduling strategy in cloud: when energy consumption meets performance guarantee

World Wide Web volume 20, pages 155–173 (2017)Cite this article

Abstract

Energy efficiency of cloud computing has been given great attention more than ever before. One of the challenges is how to strike a balance between minimizing the energy consumption and meeting the quality of services such as satisfying performance and resource availability in a timely manner. Many studies based on the online migration technology attempt to move virtual machine from low utilization of hosts and then switch it off with the purpose of reducing energy consumption. In this paper, we aim to develop an adaptive task scheduling strategy. In particular, we first model the virtual machine energy from the perspective of the cloud task scheduling, then we propose a genetic algorithm to achieve adaptive regulations for different requirements of energy and performance in cloud tasks (E-PAGA). Then we design two types of the fitness function for choosing the next generation with different preferences on energy and performance. As a result, we can adaptively adjust the energy and performance target before assigning the task in cloud, which is able to meet various requirements from different users. From the extensive experiments, we pinpoint several important observations which are useful in configuring real cloud data centers: 1) we prove that guaranteeing the minimum total task time usually leads to low energy consumption to some extent; 2) we must pay the price of the sacrificed performance if only taking into account the energy optimization; 3) we come to the conclusion that there is always an optimal condition of energy-efficiency ratio in the cloud data center, and more importantly the specific conditions of the optimal energy-efficiency ratio can be obtained.

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Acknowledgments

This research was supported by National Natural Science Foundation of China (61373015, 61300052), the National High Technology Research and Development Program of China (863 Program) (No. 2007AA01Z404), Research Fund for the Doctoral Program of High Education of China (No. 20103218110017), a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the Fundamental Research Funds for the Central Universities, NUAA (No. NP2013307), Funding of Jiangsu Innovation Program for Graduate Education KYLX_0287, the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Nan Jing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Yao Shen & Xiaolin Qin

  2. School of Computer Science & Information Technology, RMIT University, Melbourne, VIC, 3000, Australia

    Zhifeng Bao

  3. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Jian Shen

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  1. Yao Shen
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  2. Zhifeng Bao
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  3. Xiaolin Qin
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  4. Jian Shen
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Correspondence to Yao Shen or Xiaolin Qin.

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Shen, Y., Bao, Z., Qin, X. et al. Adaptive task scheduling strategy in cloud: when energy consumption meets performance guarantee. World Wide Web 20, 155–173 (2017). https://doi.org/10.1007/s11280-016-0382-4

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  • DOI: https://doi.org/10.1007/s11280-016-0382-4

Keywords

  • Cloud computing
  • Cloud task scheduling
  • Energy-aware optimization
  • Genetic algorithms