Scout：寻找最佳云配置的经验指南
Scout: An Experienced Guide to Find the Best Cloud Configuration
摘要：
为工作负载找到正确的云配置是确保良好性能和控制运行成本的重要步骤。糟糕的云配置选择会降低应用程序性能并显着增加运行成本。虽然贝叶斯优化是有效的，适用于任何工作负载，但它是脆弱的，因为性能和工作负载很难建模（预测）。在本文中，我们提出了一种新的方法，SCOUT。SCOUT的核心观点是，使用先前的测量，即使是针对不同工作负载的测量，也可以提高搜索性能并降低搜索成本。在其核心，SCOUT提取搜索提示（资源需求的推断）从低级别的性能指标。这样的提示使SCOUT能够更有效地在搜索空间中导航-只搜索聚光灯区域。我们在Apache Hadoop和Spark上评估了107个工作负载的SCOUT。实验结果表明，我们的方法找到了更好的云配置，比最先进的方法具有更低的搜索成本。基于这项工作，我们得出结论：（i）低级别的性能信息对于以有效，高效和可靠的方式找到正确的云配置是必要的，以及（ii）搜索方法可以由历史数据指导，从而降低成本并提高性能。
英文摘要：
Finding the right cloud configuration for workloads is an essential step to ensure good performance and contain running costs. A poor choice of cloud configuration decreases application performance and increases running cost significantly. While Bayesian Optimization is effective and applicable to any workloads, it is fragile because performance and workload are hard to model (to predict). In this paper, we propose a novel method, SCOUT. The central insight of SCOUT is that using prior measurements, even those for different workloads, improves search performance and reduces search cost. At its core, SCOUT extracts search hints (inference of resource requirements) from low-level performance metrics. Such hints enable SCOUT to navigate through the search space more efficiently---only spotlight region will be searched. We evaluate SCOUT with 107 workloads on Apache Hadoop and Spark. The experimental results demonstrate that our approach finds better cloud configurations with a lower search cost than state of the art methods. Based on this work, we conclude that (i) low-level performance information is necessary for finding the right cloud configuration in an effective, efficient and reliable way, and (ii) a search method can be guided by historical data, thereby reducing cost and improving performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
