论文标题
蓝宝石：分布式存储系统的自动配置建议
Sapphire: Automatic Configuration Recommendation for Distributed Storage Systems
论文作者
论文摘要
现代的分布式存储系统带有可配置参数的Aplethora，可控制模块行为并影响系统性能。开发人员提供的默认设置通常是特定用户案例的次优。调整参数可以提供显着的性能增长，但由于大量可配置参数，复杂的内部依赖性和非线性系统行为，这是一项艰巨的任务，需要深刻的经验和专业知识。为了克服这些困难，我们提出了一种基于自动模拟的方法Sapphire，通过利用机器学习和黑盒优化技术来推荐最佳配置。我们在Ceph上评估蓝宝石。结果表明，与默认配置相比，蓝宝石将CEPH性能显着提高到2.2倍。
Modern distributed storage systems come with aplethora of configurable parameters that controlmodule behavior and affect system performance. Default settings provided by developers are often suboptimal for specific user cases. Tuning parameters can provide significant performance gains but is a difficult task requiring profound experience and expertise, due to the immense number of configurable parameters, complex inner dependencies and non-linearsystem behaviors. To overcome these difficulties, we propose an automatic simulation-based approach, Sapphire, to recommend optimal configurations by leveraging machine learning and black-box optimization techniques. We evaluate Sapphire on Ceph. Results show that Sapphire significantly boosts Ceph performance to 2.2x compared to the default configuration.
