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.

中文翻译：

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快速、低成本搜索 HPC 工作负载的高效云配置

云计算资源的使用对于公司和研究人员随时按需访问高性能资源变得越来越重要。然而，鉴于虚拟机类型、网络配置、实例数量等多种多样，即使对于专家来说，找到既能降低成本和资源浪费又能实现可接受性能的最佳配置也是一项艰巨的任务。因此，已经提出了许多为给定程序找到这些好的或最佳配置的方法。在某些配置中观察应用程序的性能需要时间和金钱。因此，大多数方法不仅旨在找到好的解决方案，而且还旨在降低搜索成本。一种方法是使用贝叶斯优化来观察尽可能少的配置，降低搜索成本的同时仍能找到好的解决方案。另一种方法是使用一种名为 Paramount Iteration 的技术来对 HPC 工作负载进行性能假设，而无需完全执行它们（提前停止），从而降低进行一次观察的成本，并使网格搜索解决方案变得可行。在这项工作中，我们展示了这两种技术可以一起使用来进行更少和低成本的观察。我们表明，这种方法可以推荐平均比随机搜索好 1.68 倍且搜索成本低 6 倍的帕累托最优解。并使网格搜索解决方案变得可行。在这项工作中，我们展示了这两种技术可以一起使用来进行更少和低成本的观察。我们表明，这种方法可以推荐平均比随机搜索好 1.68 倍且搜索成本低 6 倍的帕累托最优解。并使网格搜索解决方案变得可行。在这项工作中，我们展示了这两种技术可以一起使用来进行更少和低成本的观察。我们表明，这种方法可以推荐平均比随机搜索好 1.68 倍且搜索成本低 6 倍的帕累托最优解。