With the blooming of cloud computing, the demand for data centers has been rising greatly in recent years. Their energy consumption and environmental impact has become much more significant due to the continuous growth of data center supply. It is possible to reduce the amount of energy consumed by a data center by shutting down unnecessary servers and maintaining only a subset running, such that it is enough to fulfill the resource demand. With recent advances in virtualization technology, it even became possible to consolidate the workload of multiple under-utilized servers into a single server. However, too aggressive consolidation may lead to significant degradation of data center performance. Therefore, the problem of simultaneously minimizing energy consumption and performance degradation in a data center is a complex and challenging problem. In this paper, a novel multi-objective Boolean optimization encoding for virtual machine consolidation is proposed and several approaches to solve it are described and compared. Moreover, this encoding is extended to consider anti-collocation constraints and the migration of virtual machines that are initially placed. This work is in part motivated by the great improvements in the performance of Boolean optimization solvers, thus increasing their applicability and effectiveness for a wider spectrum of complex problems. In this case, specific techniques are applied to further boost the performance, namely search space reduction by symmetry breaking and heuristic reduction of the instance size. An extensive experimental evaluation shows the suitability of the proposed solution in comparison to the state-of-the-art approaches based on stochastic methods.

中文翻译：

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使用基于约束的多目标优化进行虚拟机整合

随着云计算的兴起，近年来对数据中心的需求已大大增加。由于数据中心供应的持续增长，它们的能源消耗和环境影响变得更加重要。通过关闭不必要的服务器并仅维护一个子集运行，可以减少数据中心的能耗，从而足以满足资源需求。随着虚拟化技术的最新发展，甚至有可能将多个未充分利用的服务器的工作负载整合到单个服务器中。但是，过于激进的整合可能会导致数据中心性能显着下降。因此，同时最小化数据中心的能耗和性能下降的问题是一个复杂而具有挑战性的问题。本文提出了一种用于虚拟机整合的新型多目标布尔优化编码，并描述和比较了几种解决方案。此外，此编码已扩展为考虑反配置约束和最初放置的虚拟机的迁移。这项工作的部分动机是布尔优化求解器的性能得到了极大的提高，从而提高了它们在更广泛的复杂问题中的适用性和有效性。在这种情况下，将应用特定技术来进一步提高性能，即通过对称破坏来减小搜索空间以及实例大小的启发式减小。