The wide variety of virtual machine types, network configurations, number of instances, among others configuration tweaks, in cloud computing, makes the finding of the best configuration a hard problem. Trying to reduce costs and resource underutilization while achieving acceptable performance can be a hard task even for specialists. Thus, many approaches to find these optimal or almost optimal configurations for a given program were proposed in the literature. Observing the performance of an application in the cloud takes time and money. Therefore, most of the approaches aim not only to find good solutions but also to reduce the search cost. One of those approaches relies on Bayesian Optimization, which analyzes fewer configurations, reducing the search cost while still finding good solutions. Another approach found in the literature is the use of a technique named Paramount Iteration, which enables users to reason about cloud configurations' cost and performance without executing the application to its completion (early-stopping) this approach reduces the cost of each observation. In this work, we show that both techniques can be used together to do fewer and lower-cost observations. We demonstrate that such an approach can recommend solutions that are 1.68x better on average than Random Searching and with a 6x cheaper search.

Agradecimentos: ao CNPq (313012/2017-2) e CAPES (PROCAD 2966/2014), e também a Fapesp (2013/08293-7) e Petrobras pelo apoio financeiro.

Given the wide variety of cloud computing resources for creating high-performance computer clusters and their complex performance relationship with applications, finding the optimal, or near-optimal, cluster is a complex problem. As a result, several approaches have been proposed to find the optimal, or near-optimal, cluster for a given high-performance computing workload, while reducing the search cost.Among the approaches found in the literature, Bayesian optimization is one of the most known and applied. However, it is still possible to increase its performance by integrating it with historical data related to workload behavior. In this context, we suggest the PB 3 Opt approach, which introduces a bias in the Bayesian optimization expected improvement acquisition function. The new acquisition function uses the ranking of computer clusters of previously explored workloads that have the same behavior as the workload being optimized. Our experimental results show that PB 3 Opt classifies the behavior of workloads in groups so that the average-ranking has 88.7% similarity with the ranking of the workload. With this, PB 3 Opt finds, for almost 95% of workloads, a solution that is less than or equal to 1.2 × worse than the optimal computer cluster. In addition, the PB 3 Opt works well when combined with the paramount iterations technique and is capable of reducing the search cost significantly.

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