Soft Computing

, Volume 22, Issue 9, pp 3033–3048 | Cite as

Engineering simulated evolution for integrated power optimization in data centers

Methodologies and Application
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Abstract

Cloud computing has evolved as the next-generation platform for hosting applications ranging from engineering to sciences, and from social networking to media content delivery. The numerous data centers, employed to provide cloud services, consume large amounts of electrical power, both for their functioning and their cooling. Improving power efficiency, that is, decreasing the total power consumed, has become an increasingly important task for many data centers for reasons such as cost, infrastructural limits, and mitigating negative environmental impact. Power management is a challenging optimization problem due to the scale of modern data centers. Most published work focuses on power management in computing nodes and the cooling facility in an isolated manner. In this paper, we use a combination of server consolidation and thermal management to optimize the total power consumed by the computing nodes and the cooling facility. We describe the engineering of an evolutionary non-deterministic iterative heuristic known as simulated evolution to find the best location for each virtual machine (VM) in a data center based on computational power and data center heat recirculation model to optimize total power consumption. A “goodness” function which is related to the target objectives of the problem is defined. It guides the moves and helps traverse the search space using artificial intelligence. In the process of evolution, VMs with high goodness value have a smaller probability of getting perturbed, while those with lower goodness value may be reallocated via a compound move. Results are compared with those published in previous studies, and it is found that the proposed approach is efficient both in terms of solution quality and computational time.

Keywords

Cloud computing Power management Resource provisioning Virtual machine assignment Combinatorial optimization Simulated evolution Non-deterministic algorithms NP hard problems 

Notes

Acknowledgements

The authors acknowledge King Fahd University of Petroleum and Minerals (KFUPM) for all support. The work was conducted as part of project COE-572132-2.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Computer Engineering and Center for Communications and IT Research, Research InstituteKing Fahd University of Petroleum and MineralsDhahranKingdom of Saudi Arabia
  2. 2.Department of Computer EngineeringKing Fahd University of Petroleum and MineralsDhahranKingdom of Saudi Arabia

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