A New Model for Energy Consumption Optimization under Cloud Computing and its Genetic Algorithm

How to reduce energy consumption under the restraints of satisfying customer service level by effective resource allocation and scheduling has become a key issue in cloud computing. In this paper, we propose a new resources-allocation and scheduling architecture for energy consumption optimization. Based on this architecture, a new energy consumption optimization model is designed to meet the real-time Service Level Agreement (SLA). The proposed model optimizes energy consumption both on system level and component level. On system level, a new virtual machine deployment algorithm based on grouping genetic algorithm is proposed to minimize systems´ idle energy consumption, which abstracts the mapping between virtual machines and servers into a multidimensional variable packing problem. On component level, dynamic voltage power adjustment technology is used to reduce energy consumption on execution. Therefore, energy consumption can be reduced on both levels with premise of meeting users´ requirements. Experimental results show that compared with other algorithms, the proposed one can greatly reduce the total energy consumption of cloud computing systems under the same conditions.