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Energy-efficient Tasks Scheduling Heuristics with Multi-constraints in Virtualized Clouds

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Reducing energy consumption has become an important task in cloud datacenters. Many existing scheduling approaches in cloud datacenters try to consolidate virtual machines (VMs) to the minimum number of physical hosts and hence minimize the energy consumption. VM live migration technique is used to dynamically consolidate VMs to as few PMs as possible; however, it introduces high migration overhead. Furthermore, the cost factor is usually not taken into account by existing approaches, which will lead to high payment cost for cloud users. In this paper, we aim to achieve energy reduction for cloud providers and payment saving for cloud users, and at the same time, without introducing VM migration overhead and without compromising deadline guarantees for user tasks. Motivated by the fact that some of the tasks have relatively loose deadlines, we can further reduce energy consumption by proactively postponing the tasks without waking up new physical machines (PMs). A heuristic task scheduling algorithm called Energy and Deadline Aware with Non-Migration Scheduling (EDA-NMS) algorithm is proposed, which exploits the looseness of task deadlines and tries to postpone the execution of the tasks that have loose deadlines in order to avoid waking up new PMs. When determining the VM instant types, EDA-NMS selects the instant types that are just sufficient to guarantee task deadline to reduce user payment cost. The results of extensive experiments show that our algorithm performs better than other existing algorithms on achieving energy efficiency without introducing VM migration overhead and without compromising deadline guarantees.

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The work on this paper has been supported by the Scientific and Technological Research Program for Guangxi Educational Commission grants 2013YB113, the National Natural Science Foundation of China grants 61662017, the Guangxi Key Laboratory Fund of Embedded Technology and Intelligent Systems (Guilin University of Technology).

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Correspondence to Yi Zhang.

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Zhang, Y., Cheng, X., Chen, L. et al. Energy-efficient Tasks Scheduling Heuristics with Multi-constraints in Virtualized Clouds. J Grid Computing 16, 459–475 (2018).

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