A Genetic Algorithm Scheduling Approach for Virtual Machine Resources in Cloud Data Centers

Nowadays, power consumption of data centers has huge impacts on environments. Researchers are seeking to find effective solutions to make data centers reduce power consumption while keep the desired quality of service or service level objectives. Virtual Machine (VM) technology has been widely applied in data center environments due to its seminal features, including reliability, flexibility, and the ease of management. We present genetic algorithm scheduling approach to reduce data center power consumption, while guarantee the performance from users’ perspective . We use live migration and switching idle nodes to the sleep mode allow Cloud providers to optimize resource usage and reduce energy consumption.We have validated our approach by conducting a performance evaluation study using the CloudSim toolkit. The experimental results show that the proposed algorithm achieves reduced energy consumption in data centers.