Energy-Efficient Virtual Machine Scheduling across Cloudlets in Wireless Metropolitan Area Networks

Abstract

Nowadays, with the development of wireless communication, people are relying on mobile devices heavily due to various application deployment and plentiful service experience of customers in wireless metropolitan area network (WMAN). The computing resources of the mobile devices are limited as they are restricted with physical size, battery capacity, etc. In order to release the resource limitation, cloudlet, an effective paradigm, is introduced to host the computing tasks offloaded from the mobile devices. Compared with the cloud computing, the cloudlets are deployed closer to provide customers with fewer task offloading delay. Currently, the offloaded task scale is increasing, which generates large quantities of energy exposure for task implementation among cloudlets. Taking advantage of live virtual machine (VM) migration technology, the energy consumption of cloudlets could be definitely reduced. But such migration across cloudlets also decreases the implementation performance of the tasks. Therefore, it is still a challenge to jointly optimize the execution performance and the energy consumption for cloudlet management in WMAN. In this paper, a novel virtual machine (VM) scheduling method is proposed to balance the implantation time and the energy consumption to cope with the above challenge. Specifically, a collection of available migration polices are obtained through heuristically searching of destination cloudlets for the running computing tasks. Then, simple additive weighting (SAW) and multiple criteria decision making (MCDM) techniques are leveraged to select the optimal VM scheduling strategy across cloudlets in WMAN. Finally, experimental results and evaluations validate our proposed method is both effective and feasible.