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An energy-efficient task scheduling algorithm for heterogeneous cloud computing systems

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Abstract

The massive growth of cloud computing leads to huge amounts of energy consumption and release of carbon footprints as data centers are housed by a large number of servers. Consequently, the cloud service providers are looking for eco-friendly solutions to reduce energy consumption and carbon emissions. As a result, task scheduling has drawn attention, in which efficient resource utilization and minimum energy consumption take into great consideration. This is an exigent issue, especially for the heterogeneous environment. In this work, we put forward an energy-efficient task scheduling algorithm (ETSA) to address the demerits associated with task consolidation and scheduling. The proposed algorithm ETSA takes into account the completion time and total utilization of a task on the resources, and follows a normalization procedure to make a scheduling decision. We evaluate the proposed algorithm ETSA to measure energy efficiency and makespan in the heterogeneous environment. The experimental results are compared with recent algorithms, namely random, round robin, dynamic cloud list scheduling, energy-aware task consolidation, energy-conscious task consolidation and MaxUtil. The proposed algorithm ETSA provides an elegant trade-off between energy efficiency and makespan than the existing algorithms.

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Authors and Affiliations

  1. Department of Information Technology, Veer Surendra Sai University of Technology, Burla, 768018, India

    Sanjaya K. Panda

  2. Department of Computer Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, Dhanbad, 826004, India

    Prasanta K. Jana

Authors
  1. Sanjaya K. Panda
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  2. Prasanta K. Jana
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Correspondence to Sanjaya K. Panda.

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Panda, S.K., Jana, P.K. An energy-efficient task scheduling algorithm for heterogeneous cloud computing systems. Cluster Comput 22, 509–527 (2019). https://doi.org/10.1007/s10586-018-2858-8

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  • DOI: https://doi.org/10.1007/s10586-018-2858-8

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