A cost-effective power-aware approach for scheduling cloudlets in cloud computing environments

On a cloud platform, the user requests are managed through workload units called cloudlets which are assigned to virtual machines through cloudlet scheduling mechanism that mainly aims at minimizing the request processing time by producing effective small length schedules. The efficient request processing, however, requires excessive utilization of high-performance resources which incurs large overhead in terms of monetary cost and energy consumed by physical machines, thereby rendering cloud platforms inadequate for cost-effective green computing environments. This paper proposes a power-aware cloudlet scheduling (PACS) algorithm for mapping cloudlets to virtual machines. The algorithm aims at reducing the request processing time through small length schedules while minimizing energy consumption and the cost incurred. For allocation of virtual machines to cloudlets, the algorithm iteratively arranges virtual machines (VMs) in groups using weights computed through optimization and rescaling of parameters including VM resources, cost of utilization of resources, and power consumption. The experiments performed with a diverse set of configurations of cloudlets and virtual machines show that the PACS algorithm achieves a significant overall performance improvement factor ranging from 3.80 to 23.82 over other well-known cloudlet scheduling algorithms..