Due to the expanding utilization of cloud computing services, power consumption in cloud data centers has increased significantly. The number of active physical hosts impacts data center power usage, so the number of active physical hosts should be decreased. To achieve this goal, cloud data centers use virtualization technology to consolidate multiple virtual machines on a single physical server, using state-of-the-art virtual machine placement algorithms. Specifically, bin packing algorithms

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... ave been widely used to place a set of items, i.e., cloudlets and virtual machines, into a set of bins, i.e., virtual machines and physical hosts. However, a set of cloud services, i.e., cloudlets, are characterized as realtime and need to be provided within strict deadlines. In this paper, a cloud resource allocation framework is proposed to provide a compromise between two goals. The proposed framework uses the optimal physical host MIPS to achieve minimum possible power consumption while satisfying virtual machine-based cloudlets' deadline constraints. The proposed framework includes two modules, namely cloudlet allocator and virtual machine allocator. A set of widely used bin packing algorithms is exploited and compared in both modules. Firstly, the algorithms exploited in the cloudlet allocator module include first-fit, best-fit, and round-robin. The evaluation results showed that the round-robin algorithm provides the best outcomes in terms of real-time constraints. Round-robin could allocate an increasing number of cloudlets to virtual machines without scarifying the deadline constraints. Secondly, the algorithms used in the comparison in the virtual machine allocator module include first-fit, best-fit, next-fit, and worst-fit. The results showed that the best-fit algorithm reduces power consumption among all other algorithms under consideration. The results also suggest that setting the physical host CPU MIPS to optimal MIPS achieves the least consumed power.