, Volume 102, Issue 1, pp 263–294 | Cite as

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

  • Manojit GhoseEmail author
  • Sawinder Kaur
  • Aryabartta Sahu


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).


Cloud Scheduling Energy reduction Real-time task Critical utilization 

Mathematics Subject Classification




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.


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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Indian Institute of Information Technology GuwahatiGuwahatiIndia
  2. 2.Indian Institute of Technology GuwahatiGuwahatiIndia

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