Abstract
One of the most crucial task during the design of a computing infrastructure is the decision about the proper amount of equipments required to handle a specific workload while satisfying a set of performance objectives. This problem is emphasized even more in actual computer infrastructure such as clouds, where the user can provision the resources very easily thanks to the use of virtual machines. If the system has to handle a low workload, resources can be consolidated together to reduce the costs. If however the workload is very high, resources must be replicated to gain an acceptable service level. In this paper we derive the impact on several performance indexes for both consolidation and replication when considering both open and closed workloads. In particular, we present an analytical model to determine the best consolidation or replication options that match given performance objectives specified through a set of constraints. Depending on the particular type of workload and constraints, we present either closed form expressions, heuristics or an iterative algorithm to compute the minimum number of resources required.
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Notes
The minimum number of jobs is equal to the number of workload classes, because there must be at least one job for each class in order to define a proper model.
In some cases, when the PCs are particularly tight, the actual number of iterations can be larger than \(S\) to account for the random routing considered by the technique, but its complexity is still \(O(S)\).
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This work has been partially supported by the “AWS in Education research grant” from Amazon, and by the “ForgeSDK” project sponsored by Reply S.R.L.
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Cerotti, D., Gribaudo, M., Piazzolla, P. et al. Matching performance objectives for open and closed workloads by consolidation and replication. Ann Oper Res 239, 589–612 (2016). https://doi.org/10.1007/s10479-014-1591-9
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DOI: https://doi.org/10.1007/s10479-014-1591-9