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PACO-VMP: Parallel Ant Colony Optimization for Virtual Machine Placement
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DOI：10.1016/j.future.2021.11.019.png)
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Abstract
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The Virtual Machine Placement (VMP) problem is a challenging optimization task that involves the assignment of virtual machines to physical machines in a cloud computing environment. The placement of virtual machines can significantly affect the use of resources in a cluster, with a subsequent impact on operational costs and the environment. In this paper, we present an improved algorithm for VMP, based on Parallel Ant Colony Optimization (PACO), which makes effective use of parallelization techniques and modern processor technologies. We achieve solution qualities that are comparable with or superior to those obtained by other nature-inspired methods, with our parallel implementation obtaining a speed-up of up to 2002x over recent serial algorithms in the literature. This allows us to rapidly find high-quality solutions that are close to the theoretical minimum number of Virtual Machines. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Virtual Machine Placement
Ant Colony Optimization
Swarm intelligence
Parallel MAX-MIN Ant System
Parallel Ant Colony Optimization
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