Abstract
This article presents and evaluates twenty-four novel bi-objective efficient heuristics for the simultaneous optimization of makespan and robustness in the context of the static robust tasks mapping problem for datacenters. The experimental analysis compares the proposed methods over realistic problem scenarios. We study their accuracy, as well as the regions of the search space they explore, by comparing versus state-of-the-art Pareto fronts, obtained with four different specialized versions of well-known multi-objective evolutionary algorithms.
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Acknowledgment
B. Dorronsoro would like to acknowledge the Spanish MINECO and ERDF for the support provided under contract TIN2014-60844-R (the SAVANT project). The work of S. Nesmachnow is partly funded by ANII and PEDECIBA, Uruguay.
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Nesmachnow, S., Dorronsoro, B. (2018). A Comparative Analysis of Accurate and Robust Bi-objective Scheduling Heuristics for Datacenters. In: Medina, J., Ojeda-Aciego, M., Verdegay, J., Perfilieva, I., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2018. Communications in Computer and Information Science, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-319-91479-4_19
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