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With the advent of big data applications, which tends to have longerexecution time, choosing the right cloud VM to run these applications hassignificant performance as well as economic implications. For example, in ourlarge-scale empirical study of 107 different workloads on three popular bigdata systems, we found that a wrong choice can lead to a 20 times slowdown oran increase in cost by 10 times.  Bayesian optimization is a technique for optimizing expensive (black-box)functions. Previous attempts have only used instance-level information (such as# of cores, memory size) which is not sufficient to represent the search space.In this work, we discover that this may lead to the fragility problem---eitherincurs high search cost or finds only the sub-optimal solution. The centralinsight of this paper is to use low-level performance information to augmentthe process of Bayesian Optimization. Our novel low-level augmented BayesianOptimization is rarely worse than current practices and often performs muchbetter (in 46 of 107 cases). Further, it significantly reduces the search costin nearly half of our case studies.  Based on this work, we conclude that it is often insufficient to usegeneral-purpose off-the-shelf methods for configuring cloud instances withoutaugmenting those methods with essential systems knowledge such as CPUutilization, working memory size and I/O wait time.

Low-Level Augmented Bayesian Optimization for Finding the Best Cloud VM