Previous [ 1] [ 2] [ 3] [ 4] [ 5] [ 6] [ 7] [ 8] [ 9] [ 10] [ 11] [ 12] [ 13] [ 14] [ 15] [ 16]

Journal of Information Science and Engineering, Vol. 32 No. 6, pp. 1575-1593 (November 2016)

Multi Target Dynamic VM Consolidation in Cloud Data Centers Using Genetic Algorithm

Department of Electrical and Electronics Engineering
Amirkabir University of Technology
Tehran, 1591634311 Iran
E-mail: {ehsan_arianyan; htaheri; sharifian_s}

Fast IT development combined with diverse range of requests for IT services have led to the establishment of huge energy hungry data centers all around the world. Consolidation is proposed as one of the most effective methods for energy saving in modern cloud data centers. This paper addresses multi target resource allocation for cloud data centers that applies a holistic view to the resource allocation problem. More importantly, this paper proposes an enhanced energy efficient resource allocation algorithm based on genetic algorithm which takes the energy consumption of both cooling systems and IT equipment into consideration. Results of simulations using Cloudsim simulator validates the applicability of the proposed algorithms which shows up to 23.77%, 31.59%, 61.17%, and 26.52% reductions in energy consumption, SLA violation, number of virtual machine migrations, and execution time, respectively, in comparison with state of the art.

Keywords: cloud computing, data center, energy consumption, genetic algorithm, resource allocation

Full Text () Retrieve PDF document (201611_10.pdf)

Received September 8, 2015; revised April 5 & June 21, 2016; accepted June 25, 2016.
Communicated by Jan-Jan Wu.