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Exact algorithms for energy-efficient virtual machine placement in data centers

机译:在数据中心内高效节能虚拟机的精确算法

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Virtual machine placement (VMP) and power management are essential topics in the development of cloud computing and data centers. The assignment of a virtual machine to physical machine impacts the energy consumption, the makespan, and the idle time of physical machines. In this paper, we formulate the problem as a three-dimension bin-packing optimization to minimize the energy cost of working machines and idle machines. By considering the CPU and memory requirements from a virtual machine, the assignment is constrained under the capacities of the physical machine. Inspired by the best-fit decreasing algorithm, four variants of this exact algorithm are developed to address the multiple-objective problem under multiple-capacity constraints. Experimental results demonstrate the effectiveness of the proposed algorithms on small-, medium- and large-scale instances profiled from data centers. The results indicate that the algorithms assigning virtual machines to the physical machines of best-fit hosting time is competitive in cases with loose capacity constraints, and the energy-efficiency best-fit algorithm produces efficient assignments when a makespan limit is required on the physical machines. The algorithm combining the fit rules has a linear computing time concerning the numbers of physical and virtual machines, and a stable performance that obtains gaps of results lower than 5.8% compared to an on-the-shelf mixed-integer linear program solver.
机译:虚拟机放置(VMP)和电源管理是云计算和数据中心开发中必不可少的主题。将虚拟机分配给物理机会影响能耗,工期和物理机的空闲时间。在本文中,我们将问题表达为三维装箱优化,以最大程度地减少工作机和闲置机的能源成本。通过考虑虚拟机的CPU和内存要求,分配被限制在物理机的容量范围内。受最佳拟合递减算法的启发,开发了该精确算法的四个变体,以解决多容量约束下的多目标问题。实验结果证明了该算法在从数据中心剖析的小,中和大型实例上的有效性。结果表明,在容量限制宽松的情况下,将虚拟机分配给最适合的托管时间的物理机的算法具有竞争性,并且当需要对物理机的制造期进行限制时,能效最适合的算法会产生高效的分配。结合了拟合规则的算法具有与物理机和虚拟机数量有关的线性计算时间,并且具有稳定的性能,与现成的混合整数线性程序求解器相比,其结果差距小于5.8%。

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