A branch-and-bound approach to virtual machine placement

Finding the best mapping of virtual machines to physical machines in cloud data centers is a very important optimization problem, with huge impact on costs, application performance, and energy consumption. Although several algorithms have been suggested to solve this problem, most of them are either simple heuristics or use off-the-shelf, mostly integer linear programming (ILP) solvers. In this paper, we propose a new approach: a custom branch-and-bound algorithm that exploits problem-specific knowledge in order to improve effectiveness. As shown by empirical results, the new algorithm performs better than state-of-the-art general-purpose ILP solvers.

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