Abstract
This paper addresses a process-to-machine reassignment problem arising in cloud computing environments. The problem formulation has been posed as the ROADEF/EURO challenge 2012. Our presented approach is basically a large neighborhood search that iteratively improves a given solution. In each iteration only a subset of processes is considered for reassignment and the new assignments are evaluated by a constraint program. In this paper we present our general solution approach. Furthermore, we evaluate different process selection strategies and other optimization means to improve the performance on larger instances. In addition, we present a simple way to compute tight lower bounds of the necessary costs.
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Notes
Technically the current search node only holds a reference to the base values of the root search node and a small object containing the changes that happened since then.
In practice the cost for placing \(p\) to \(m\) is cached and only updated if other processes have been placed on \(m\) in between.
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Appendix: Additional tables
Appendix: Additional tables
In the experimental section, we used solution scores instead of the underlying solution values to compare different approaches and techniques. This decision was motivated by the fact that solution scores enable the reader to easily relate our solution to the best solution that was computed by any contestant. Additionally, a score-based evaluation is less instance-dependent and thus makes it easier to compare the performance of different strategies with each other. Nevertheless, we also want to present the actual costs of the computed solutions in order to enable others to compare their solution costs directly with the solutions computed by our approach. For this reason, Table 9 states the average costs of the solutions that were computed by our approach after 1, 5 and 30 min as well as the initial cost and our computed lower bound.
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Brandt, F., Speck, J. & Völker, M. Constraint-based large neighborhood search for machine reassignment. Ann Oper Res 242, 63–91 (2016). https://doi.org/10.1007/s10479-014-1772-6
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DOI: https://doi.org/10.1007/s10479-014-1772-6