Energy-aware and revenue-enhancing Combinatorial Scheduling in Virtualized of Cloud Datacenter

Zhiming Wang, Kai Shuang, Long Yang, Fangchun Yang
2012 Journal of Convergence Information Technology  
As the scale of cloud datacenter becomes larger and larger, the energy consumption and revenue enhancing of the cloud datacenter will grow rapidly. How to improve high-throughput computing resource allocation strategy was taken into account. High-throughput computing resource consolidation is an effective method to increase resource utilization and in turn reduces energy consumption and increases revenue acquisition. However, high-throughput computing resource consolidation may lead to several
more » ... roblems, such as the freeing up of resources that can sit idling yet still drawing power and the energy consumption of communication and revenue acquisition are ignored. Based on these considerations, this paper proposes a Particle Swarm Optimized Tabu search Mechanism, aiming to maximize resource utilization and explicitly taking into account both active and idle energy consumption in minimizing finish time. While fulfilling requirements and QoS of cloud datacenter, this proposed mechanism allows turning off the spare servers, thus reducing power consumption and increasing revenue enhancing overall datacenter. We conducted extensive experiments based on the platform of CloudSim. By comparing with traditional algorithms, we prove that proposed algorithm can save energy consumption reducing by 67.5% and increase revenue enhancing by 8.14 times averagely based on the consideration of communication and QoS of cloud datacenter in minimizing finish time.
doi:10.4156/jcit.vol7.issue1.8 fatcat:vm3xgbzm6vdiho6m24tsb4fgcm