  The use of cloud computational resources has become increasingly important
for companies and researchers to access on-demand and at any moment
high-performance resources. However, given the wide variety of virtual machine
types, network configurations, number of instances, among others, finding the
best configuration that reduces costs and resource waste while achieving
acceptable performance is a hard task even for specialists. Thus, many
approaches to find these good or optimal configurations for a given program
have been proposed. Observing the performance of an application in some
configuration takes time and money. Therefore, most of the approaches aim not
only to find good solutions but also to reduce the search cost. One approach is
the use of Bayesian Optimization to observe the least amount possible of
configurations, reducing the search cost while still finding good solutions.
Another approach is the use of a technique named Paramount Iteration to make
performance assumptions of HPC workloads without entirely executing them
(early-stopping), reducing the cost of making one observation, and making it
feasible to grid search solutions. In this work, we show that both techniques
can be used together to do fewer and low-cost observations. We show that such
an approach can recommend Pareto-optimal solutions that are on average 1.68x
better than Random Searching and with a 6-time cheaper search.
