Int. J. of Applied Mathematics, Computational Science and Systems Engineering

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Article

On Improving Peak and Off-Peak Energy Consumption Pattern in Cloud Data Centers

Author(s): Thusoyaone J. Moemi, Obeten O. Ekabua

Abstract: Using the subscription basis and the pay asyou- go model, cloud computing delivers to consumer’s infrastructure, platform and software applications as services. The initiative is to deliver the design of the next generation data centers that enables users to access and deploy applications on-demand from anywhere at competitive cost. Data centers are expensive to maintain and ‘unfriendly’ to the environment because they require massive amounts of energy during peak and off-peak periods. High carbon emissions in data centers lead to overheating which affects the machines lifetime and reliability. Therefore, to make adequate use of the precious energy resource, it is pertinent to know the amount of energy required per instance in a data center. Consequently, in this research article, we developed and implemented energy efficiency models and optimization algorithms for improving delivery of on-demand energy resources during peak and off-peak periods in a cloud computing environment. This was achieved by developing a load balancing model, called LBVMA model, which supports energy reduction in our data centers. The experimental results show significantly the efficiency derived from reduced energy consumption. The reduction and efficient energy usage (EEU) helps to improve delivery of on-demand energy resources in a cloud computing environment.

Keywords: Cloud Computing, Energy Efficiency, Models, Algorithms, Data Center, Service Delivery.

Pages: 27-32

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