4.6 Article

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

期刊

JOURNAL OF GRID COMPUTING
卷 19, 期 2, 页码 -

出版社

SPRINGER
DOI: 10.1007/s10723-021-09548-0

关键词

Ant colony optimization; Cloud computing; Deadline; Energy; Makespan; Task scheduling

资金

  1. UGCNET Junior Research Fellowship (UGC) by the University Grants Commission, Government of India [3610/(NETNOV 2017)]
  2. Visvesvaraya PhD Scheme ofMinistry of Electronics & Information Technology, Government of India [MLA/MUM/GA/10(37)C]

向作者/读者索取更多资源

Cloud computing allows for various applications to be executed by users in a virtualized environment, but it also consumes significant energy; healthcare, scientific research, IoT tasks are deadline-sensitive, requiring efficient scheduling to reduce energy consumption; proposed approaches effectively address the trade-off between energy consumption and task completion time.
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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