Task scheduling model and virtual machine deployment algorithm for energy consumption optimization in cloud computing

In cloud computing, how to allocate and schedule resources effectively and how to reduce energy consumption under the restraint of satisfying the customer service level have become key issues that cannot be ignored. Since there is a lack of studies on energy consumption by resources allocation and scheduling, 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 optimize energy consumption both on the system level and the component level. On the former level, a new virtual machine deployment algorithm based on grouping genetic algorithm is proposed to minimize systems' idle energy consumption, which abstract the mapping between virtual machines and servers into a multidimensional variable packing problem. On the latter level, dynamic voltage power adjustment technology is used to reduce execution energy consumption. Therefore, energy consumption can be minimized on the 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.