Hypercube Based Genetic Algorithm for Efficient VM Migration for Energy Reduction in Cloud Computing
Abstract
If we choose to compare computing technology to coral reef then cloud computing technology is its very live and growing end. Its challenges are new and demand innovative measure to bring the size of its expending data centers under calipers and bridle its energy consumptions. Reduction in the consumption of energy is to be brought about without compromising quality-of-service and efficacy. For this, we purpose a Hypercube based Genetic Algorithm (HBGA) for efficient VM migration for energy reduction in cloud computing under QoS (Quality-of-service) constraint. The proposed HBGA technique can be implemented in two phases. First, in a data center the physical machines organize themselves in such a way as to acquire a highly scalable structure called Hypercube. The hypercube imperceptibly grates itself up or dips low in sympathy with VM instances as they mount up or get depleted. Secondly on the basis of this representation model of the compute nodes, and given the hypercube topology in which they are organized we propose three algorithms: (a)Hypercube based Node Selection Algorithm to minimize energy consumption (b) Hypercube based VM Selection Algorithm which minimizes the number of VM to be migrated. (c) To solve the problem of VM Placement we propose Hypercube based Genetic algorithm.Experimental results of comparisons between the proposed HBGA method viz-a-viz the existing solutions show a marked reduction in energy consumption of cloud computing environment.References
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