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Scheduling real time tasks in an energy-efficient way using VMs with discrete compute capacities

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Abstract

Cloud computing has emerged to be a promising computing paradigm of the recent time. As the high energy consumption in the cloud system creates several problems, the cloud service providers need to focus on the energy consumption along with providing the required service to their users. Cloud system needs to efficiently execute various real-time applications and designing energy-efficient scheduling algorithms for these applications has gained the research momentum. In this paper, we consider scheduling of real-time tasks for a virtualized cloud system which provides VMs with discrete compute capacities. Depending on the characteristics of the tasks, we divide the problem into four subproblems and propose solution for each subproblem. For the subproblem with arbitrary execution time and deadline of tasks, we use four different methods to cluster the tasks depending on their deadline values. Experiment is performed in CloudSim tool to make a comparison among the clustering methods and results show that the clustering method can be chosen based on the specification of the cloud system. We also made a comparison of our approach with standard energy-efficient scheduling technique both for the synthetic data sets and for the real world trace and we observed an average energy reduction of around \(17\%\) and \(15\%\) for the synthetic data sets and for the real world trace respectively (as compared to the baseline policy).

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Acknowledgements

The authors would like to express their sincere thanks and gratitude to the Editor-in-Chief of the journal and the reviewers for minutely reviewing the article. This has increased the quality of the article to a great extent.

Author information

Correspondence to Manojit Ghose.

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The preliminary version of this article was published in HPCC 2017 [1].

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Cite this article

Ghose, M., Kaur, S. & Sahu, A. Scheduling real time tasks in an energy-efficient way using VMs with discrete compute capacities. Computing 102, 263–294 (2020) doi:10.1007/s00607-019-00738-z

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Keywords

  • Cloud
  • Scheduling
  • Energy reduction
  • Real-time task
  • Critical utilization

Mathematics Subject Classification

  • 68U02