You can help correct errors and omissions. Suggested CitationĪll material on this site has been provided by the respective publishers and authors. An extensive experimental evaluation shows the suitability of the proposed solution in comparison to the state-of-the-art approaches based on stochastic methods. 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. 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. Moreover, this encoding is extended to consider anti-collocation constraints and the migration of virtual machines that are initially placed. 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. Therefore, the problem of simultaneously minimizing energy consumption and performance degradation in a data center is a complex and challenging problem. However, too aggressive consolidation may lead to significant degradation of data center performance. With recent advances in virtualization technology, it even became possible to consolidate the workload of multiple under-utilized servers into a single server. 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. Their energy consumption and environmental impact has become much more significant due to the continuous growth of data center supply. With the blooming of cloud computing, the demand for data centers has been rising greatly in recent years.
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