Reducing the Upfront Cost of Private Clouds with Virtual Machine Placement

Although public clouds still occupy the largest portion of the total cloud infrastructure, private clouds are attracting increasing interest from both industry and academia because of their better security and privacy control. According to the existing studies, the high upfront cost is among the most critical challenges associated with private clouds. To reduce cost and improve performance, virtual machine placement (VMP) methods have been extensively investigated for a number of years; however, few of these methods have focused on private clouds. In this paper, we propose a heterogeneous and multidimensional clairvoyant dynamic bin packing (CDBP) model, in which the scheduler can conduct more efficient VMP processes with additional information of the arrival time and duration of virtual machines to reduce the scale of the datacenter and thereby decrease the upfront cost of private clouds. In addition, a novel branch-and-bound algorithm with a divide-and-conquer strategy (DCBB) is proposed to effectively and efficiently handle the derived problem. Extensive experiments are conducted on both real-world and synthetic workloads to evaluate the accuracy and efficiency of the algorithms. The experimental results demonstrate that DCBB delivers near-optimal solutions with a much faster convergence rate than those of the other search-based algorithms that we evaluated. In particular, DCBB yields the optimal solution on the real-world workload with an order of magnitude less execution time than that required by the original branch-and-bound (BB) algorithm.