Fast and Low-cost Search for Efficient Cloud Configurations for HPC Workloads
Vanderson Martins Do Rosario, Thais A. Silva Camacho, Ot\'avio O., Napoli, Edson Borin

TL;DR
This paper combines Bayesian Optimization and Paramount Iteration to efficiently find cost-effective cloud configurations for HPC workloads, significantly reducing search costs while maintaining high performance.
Contribution
It introduces a combined approach using Bayesian Optimization and Paramount Iteration to perform fewer, low-cost observations for optimal cloud HPC configurations.
Findings
Pareto-optimal solutions are 1.68x better than random search
Search cost is reduced by a factor of 6
Approach effectively balances cost and performance
Abstract
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.…
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Taxonomy
TopicsCloud Computing and Resource Management · IoT and Edge/Fog Computing · Parallel Computing and Optimization Techniques
