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Journal of Heuristics

, Volume 25, Issue 3, pp 339–375 | Cite as

Virtual machine consolidation using constraint-based multi-objective optimization

  • Miguel Terra-NevesEmail author
  • Inês Lynce
  • Vasco Manquinho
Article
  • 155 Downloads

Abstract

With the blooming of cloud computing, the demand for data centers has been rising greatly in recent years. Their energy consumption and environmental impact has become much more significant due to the continuous growth of data center supply. It is possible to reduce the amount of energy consumed by a data center by shutting down unnecessary servers and maintaining only a subset running, such that it is enough to fulfill the resource demand. With recent advances in virtualization technology, it even became possible to consolidate the workload of multiple under-utilized servers into a single server. However, too aggressive consolidation may lead to significant degradation of data center performance. Therefore, the problem of simultaneously minimizing energy consumption and performance degradation in a data center is a complex and challenging problem. In this paper, a novel multi-objective Boolean optimization encoding for virtual machine consolidation is proposed and several approaches to solve it are described and compared. Moreover, this encoding is extended to consider anti-collocation constraints and the migration of virtual machines that are initially placed. This work is in part motivated by the great improvements in the performance of Boolean optimization solvers, thus increasing their applicability and effectiveness for a wider spectrum of complex problems. In this case, specific techniques are applied to further boost the performance, namely search space reduction by symmetry breaking and heuristic reduction of the instance size. An extensive experimental evaluation shows the suitability of the proposed solution in comparison to the state-of-the-art approaches based on stochastic methods.

Keywords

Virtual machine consolidation Multi-objective optimization Pseudo-Boolean optimization Live migration Cloud computing 

Notes

Acknowledgements

The authors would like to thank Professors Salvador Pinto Abreu and Pedro Salgueiro from Universidade de Évora for granting the authors permission to use their cluster. This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references UID/CEC/50021/2013, SFRH/BD/111471/2015, CMU/AIR/0022/2017 and PTDC/CCI-COM/31198/2017.

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.INESC-ID / Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal

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