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Optimizing the Energy Efficiency of the Modern Data Centers using Ant Colony Optimization
Objectives: To propose an energy efficient Swarm based Optimization method. Methods/Statistical Analysis: In this research, a set of tasks and servers are taken as Input. The data center server energy consumption is taken as the output of the algorithm. The processing time may vary according to the number of tasks given. Task allocation is done in such a way that most-efficient-server gets the tasks first. If, average job density is low, i.e. then it works on the same system and if the value is high then it moves to next one. The algorithm follows a scheduling and divides the jobs into servers and start execution of the job tasks. Findings: In this paper, an ant colony algorithm is proposed to efficiently allocate tasks to virtual machines, which allocates resources based on the available resources and the energy consumption of each virtual machine. This ACO algorithm is implemented, executed, and evaluated using the experiments in Cloud Sim. Reduced power consumption, with throughput has been obtained. Application/Improvements: Less SLA violation has been obtained with good response time.
Ant Colony Optimization, Cloud Sim, Energy Consumption, Green Cloud Computing, Optimization, Virtualization
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