Abstract
Given the ever-increasing role of data centers in global energy consumption, the problem of assigning jobs (items) to servers (bins) in an energy-efficient manner has become more and more important in recent years. In that respect, the temporal bin packing problem with fire-ups (TBPP-FU) takes into account not only the number of servers used but also the switch-on processes required to operate them. Due to the challenging size of the resulting ILP models, various tailored reduction strategies can be applied to obtain more tractable formulations. In this article, we show how the information from a heuristic solution can be used to further improve these exact approaches, extending a theoretical result that was previously proven only for some very restrictive cases in the literature. Finally, the benefits of this new reduction procedure will be demonstrated based on computational tests.
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References
Andrae, A.S.G., Edler, T.: On global electricity usage of communication technology: trends to 2030. Challenges 6(1), 117–157 (2015)
Aydin, N., Muter, I., Ilker Birbil, S.: Multi-objective temporal bin packing problem: an application in cloud computing. Comput. Oper. Res. 121, 104959 (2020)
Caprara, A., Furini, F., Malaguti, E.: Uncommon Dantzig-Wolfe reformulation for the temporal Knapsack problem. INFORMS J. Comput. 25(3), 560–571 (2013)
Clautiaux, F., Detienne, B., Guillot, G.: An iterative dynamic programming approach for the temporal Knapsack problem. Eur. J. Oper. Res. 293(2), 442–456 (2021)
de Cauwer, M., Mehta, D., O’Sullivan, B.: The temporal bin packing problem: an application to workload management in data centres. In: Proceedings of the 28th IEEE International Conference on Tools with Artificial Intelligence, pp. 157–164 (2016)
Dell’Amico, M., Furini, F., Iori, M.: A branch-and-price algorithm for the temporal bin packing problem. Comput. Oper. Res. 114, 104825 (2020)
Delorme, M., Iori, M., Martello, S.: Bin packing and cutting stock problems: mathematical models and exact algorithms. Eur. J. Oper. Res. 255, 1–20 (2016)
Jones, N.: How to stop data centres from gobbling up the world’s electricity. Nature 561, 163–166 (2018)
Martinovic, J., Strasdat, N., Selch, M.: Compact integer linear programming formulations for the temporal bin packing problem with fire-ups. Comput. Oper. Res. 132, 105288 (2021)
Martinovic, J., Strasdat, N., Valério de Carvalho, J., Furini, F.: Variable and constraint reduction techniques for the temporal bin packing problem with fire-ups. Optim. Lett. (2021). https://doi.org/10.1007/s11590-021-01825-x
Scheithauer, G.: Introduction to Cutting and Packing Optimization - Problems, Modeling Approaches, Solution Methods. International Series in Operations Research & Management Science, vol. 263. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-64403-5
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Martinovic, J., Strasdat, N. (2022). A Heuristic-Based Reduction for the Temporal Bin Packing Problem with Fire-Ups. In: Trautmann, N., Gnägi, M. (eds) Operations Research Proceedings 2021. OR 2021. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-031-08623-6_20
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DOI: https://doi.org/10.1007/978-3-031-08623-6_20
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