Workload portfolio optimization for virtualized computer systems based on semiparametric quantile function estimation
Summary. The latest technologies of server virtualization allow multiple computer workloads to share the same physical system dynamically while protecting them from interference from each other. Consolidation of workloads from stand‐alone systems into virtualized systems requires accurate capacity sizing and optimal portfolio design to maximize the benefit of virtualization. This requirement often translates into a demand for accurate estimation of high quantiles from a limited amount of data for hundreds of workloads with diverse statistical characteristics. To deal with the problem, a semiparametric method of quantile function estimation is considered. The method employs the generalized Pareto distribution to model the high quantiles that exceed a certain threshold and retains the sample quantiles below the threshold. An automatic procedure is proposed for adaptive threshold and estimator selection and for adaptive data trimming. A simulation study shows that the procedure proposed is superior to the non‐parametric sample quantile method for a variety of distributions. The procedure is applied to a portfolio optimization problem for computer workload consolidation with real data.
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Document Type: Research Article
Affiliations: IBM T. J. Watson Research Center, Yorktown Heights, USA
Publication date: 2011-08-01