Operational Analysis of Parallel Servers

Multicore processors promise continued hardware performance improvements even as single-core performance flattens out. However they also enable increasingly complex application software that threatens to obfuscate application-level performance. This paper applies operational analysis to the problem of understanding and predicting application-level performance in parallel servers. We present operational laws that offer both insight and actionable information based on lightweight passive external observations of black-box applications. One law accurately infers queuing delays; others predict the performance implications of expanding or reducing capacity. The former enables improved monitoring and system management; the latter enable capacity planning and dynamic resource provisioning to incorporate application-level performance in a principled way. Our laws rest upon a handful of weak assumptions that are easy to test and widely satisfied in practice. We show that the laws are broadly applicable across many practical CPU scheduling policies. Experimental results on a multicore network server in an enterprise data center demonstrate the usefulness of our laws.

多功能处理器承诺，即使单核性能变平，也可以持续的硬件性能改进。但是，它们还可以实现越来越复杂的应用程序软件，该软件可能会混淆应用程序级的性能。本文将操作分析应用于理解和预测并行服务器中的应用程序级性能的问题。我们介绍了基于轻量级被动外部观察，可提供洞察力和可操作的信息。一项法律准确地说明了排队延误；其他人则预测了扩大或降低能力的绩效含义。前者可以改进监视和系统管理；后者可以使能力计划和动态资源提供以原则性的方式合并应用程序级别的性能。我们的法律取决于一些易于测试且在实践中得到广泛满足的薄弱假设。我们表明，法律在许多实用的CPU计划政策中广泛适用。企业数据中心中多层网络服务器上的实验结果证明了我们法律的有用性。