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1 Correction to: Soft Computing https://doi.org/10.1007/s00500-020-05462-x
Page 2: Column 2, lines 2-4, previously read: “Specifically, we consider a decision maker that, after a proper training, is able to select the most suitable heuristic for compute the placement for each VM requested by end users.”
Should read: “Specifically, we consider a decision maker that, after a proper training, is able to select the most suitable heuristic to compute the placement for each VM requested by end users.”
Page 2: Column 2, paragraph 2, lines 4-8, previously read: To evaluate its effectiveness, a comparisons against solutions widely adopted in the literature and real-world scenarios are presented, including the use of workload traces collected in a production-quality cloud datacenter.
Should read: To evaluate its effectiveness, comparisons against solutions widely adopted in the literature and real-world scenarios are presented, including the use of workload traces collected in a production-quality cloud datacenter.
Page 4: Column 1, paragraph 2, lines 2-5, previously read: In more detail, users produce a workload composed of new VM requests, which are collected by the Admission & placement module.
Should read: In more detail, users produce a workload composed of new VM requests, which are collected by the “Admission & placement” module.
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Caviglione, L., Gaggero, M., Paolucci, M. et al. Correction to: Deep reinforcement learning for multi-objective placement of virtual machines in cloud datacenters. Soft Comput 25, 12589 (2021). https://doi.org/10.1007/s00500-020-05536-w
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DOI: https://doi.org/10.1007/s00500-020-05536-w