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Energy and Makespan Aware Scheduling of Deadline Sensitive Tasks in the Cloud Environment

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Abstract

Cloud computing enables the execution of various applications submitted by the users in the virtualized Cloud environment. However, the Cloud infrastructure consumes a significant amount of electrical energy to provide services to its users that have a detrimental effect on the environment. Many of these applications (tasks), like those belonging to the healthcare system, scientific research, the Internet of Things (IoT), and others, are deadline-sensitive. Hence efficient scheduling of tasks is essential to prevent deadline violation, decrease makespan, and at the same time reduce energy consumption. To address this issue, we have considered the bi-objective optimization problem of minimization of energy and makespan and have proposed two scheduling approaches for independent, deadline-sensitive tasks in a heterogeneous Cloud environment. Our first approach is a greedy heuristic based on the Linear Weighted Sum technique. The second one is based on Ant Colony Optimization and uses a combination of heuristic search and positive feedback of information to improve the solution. Both approaches use a three-tier model where tasks are scheduled by taking into account the properties of three entities- tasks, VMs, and hosts. Moreover, we have proposed a suitable strategy for scaling of Cloud resources to improve energy-efficiency and task schedulability. Extensive simulations using Google Cloud trace-logs and comparison with some state-of-art approaches validate the effectiveness of our proposed scheduling techniques in achieving a proper trade-off between the energy consumption of the virtualized Cloud infrastructure and the average makespan of the tasks.

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Data Availability

The Google Cloud traces data analysed during the current study has been obtained from ”Borg cluster traces from Google” (https://github.com/google/cluster-data).

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Acknowledgements

This research is supported by the UGC-NET Junior Research Fellowship (UGC-Ref. No.: 3610/(NET-NOV 2017)) provided by the University Grants Commission, Government of India and Visvesvaraya PhD Scheme of Ministry of Electronics & Information Technology, Government of India, being implemented by the Digital India Corporation (Ref. No. MLA/MUM/GA/10(37)C).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Calcutta, Kolkata, 700098, India

    Anurina Tarafdar, Sunirmal Khatua & Rajib K. Das

  2. Advanced Computing and Microelectronics Unit, Indian Statistical Institute, Kolkata, 700108, India

    Mukta Debnath

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  1. Anurina Tarafdar
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  2. Mukta Debnath
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Correspondence to Sunirmal Khatua.

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Tarafdar, A., Debnath, M., Khatua, S. et al. Energy and Makespan Aware Scheduling of Deadline Sensitive Tasks in the Cloud Environment. J Grid Computing 19, 19 (2021). https://doi.org/10.1007/s10723-021-09548-0

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