Abstract
Presently, massive energy consumption in cloud data center tends to be an escalating threat to the environment. To reduce energy consumption in cloud data center, an energy efficient virtual machine allocation algorithm is proposed in this paper based on a proposed energy efficient multiresource allocation model and the particle swarm optimization (PSO) method. In this algorithm, the fitness function of PSO is defined as the total Euclidean distance to determine the optimal point between resource utilization and energy consumption. This algorithm can avoid falling into local optima which is common in traditional heuristic algorithms. Compared to traditional heuristic algorithms MBFD and MBFH, our algorithm shows significantly energy savings in cloud data center and also makes the utilization of system resources reasonable at the same time.
Highlights
With the fast development of cloud computing [1, 2], energy consumption is significantly increasing along with the explosive growth of cloud data center
There are two reasons which resulted in high energy consumption in cloud data center: one is rapid increasing of computers as well as the number of cloud users, which results in a significant amount of energy consumed by cloud data center due to their massive sizes [4, 5]; another reason is that resources allocation is not reasonable in cloud computing
We focus on the virtual machine (VM) with multiresources allocation in cloud data center
Summary
With the fast development of cloud computing [1, 2], energy consumption is significantly increasing along with the explosive growth of cloud data center. Based on the model and the particle swarm optimization (PSO), a multiresource energy efficiency based on particle swarm optimization (MREE-PSO) algorithm is designed and applied to VMs allocation for energy efficiency in cloud data center This algorithm can be divided into three parts: (1) particles are generated by FF (First Fit) algorithm; (2) the trust function of individual optimal solution and the global optimal solution of particles were defined to guide particle evolution; (3) the fitness function of PSO is defined as the total Euclidean distance which represents the optimal balance between resource utilization and energy consumption.
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