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  1. Proceedings of the 9th Latin America Networking Conference (LANC '16)
  2. An Experimental Comparison of Algorithms for Virtual Machine Placement Considering Many Objectives
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An Experimental Comparison of Algorithms for Virtual Machine Placement Considering Many Objectives
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An Experimental Comparison of Algorithms for Virtual Machine Placement Considering Many Objectives

Content Provider ACM Digital Library
Author López-Pires, Fabio Zalimben, Saúl Amarilla, Augusto Ferreira, Rodrigo Benítez, Leonardo Barán, Benjamín
Abstract Cloud computing datacenters provide thousands to millions of virtual machines (VMs) on-demand in highly dynamic environments, requiring quick placement of requested VMs into available physical machines (PMs). Due to the randomness of customer requests, the Virtual Machine Placement (VMP) should be formulated as an online optimization problem. This work presents a formulation of a VMP problem considering the optimization of the following objective functions: (1) power consumption, (2) economical revenue, (3) quality of service and (4) resource utilization. To analyze alternatives to solve the formulated problem, an experimental comparison of five different online deterministic heuristics against an offline memetic algorithm with migration of VMs was performed, considering several experimental workloads. Simulations indicate that First-Fit Decreasing algorithm (A4) outperforms other evaluated heuristics on average. Experimental results prove that an offline memetic algorithm improves the quality of the solutions with migrations of VMs at the expense of placement reconfigurations.
Starting Page 1
Ending Page 8
Page Count 8
File Format PDF
ISBN 9781450345910
DOI 10.1145/2998373.2998374
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-10-13
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Cloud computing Online algorithms Experimental comparison Virtual machine placement Optimization
Content Type Text
Resource Type Article
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