CSR:Small: Multi-Bottlenecks: What They Are and How to Find Them

该项目致力于计算云，大规模共享基础设施，为大多数用户和应用程序提供几乎无限的硬件。为了实现可扩展的性能，系统的所有组件，从硬件到操作系统、中间件、各种服务器和应用程序本身，都需要协作。组件中的瓶颈会减慢整个系统的速度。在传统的计算机系统（例如，如排队论所模拟的），一个典型的假设是他们的工作量由独立的作业组成。这种假设对于旧式的面向批处理和交互式用户是有效的，它保证了整个系统出现单一瓶颈。单个瓶颈可以相对容易地检测到，因为它们表现为达到饱和的资源（例如，“独立作业”模型不适用于面向web的应用程序的重要类别（例如，电子商务），依赖于流行的N层架构。N层系统将系统划分为处理组件的流水线，例如，包括web服务器、应用服务器和数据库服务器。虽然n层架构在Web服务器和应用程序服务器层支持良好的性能可伸缩性，但它也在其他层和组件之间引入了几个（有时是意外的）强依赖性。这些依赖性产生了一个有趣的现象，称为多瓶颈。多瓶颈的特点是系统吞吐量受到上限的限制，而不管额外的硬件，没有一个单一的资源显示平均利用率接近饱和。（有趣的是，这种情况在实践中越来越普遍。传统的性能评估方法难以发现、诊断和消除多瓶颈。它们在云中也很重要，因为它们将是移除容易发现的单个瓶颈后剩下的唯一瓶颈。该项目开发、评估和改进了一种称为望远镜的系统搜索方法，通过在生产云上运行大规模实验来发现多瓶颈。模拟器生成定义明确的多瓶颈，以帮助改进望远镜搜索方法并调整其参数。然后，在Open Cirrus、Amazon EC2和Emulab等生产云上的RUBiS和RUBBoS（电子商务应用程序）等n层基准测试收集了多瓶颈的实验证据。这些实验揭示了一个鲜为人知的现象，在一个丰富的，但未探索的领域（性能限制的工作与依赖关系）。成功可以在理论上理解具有依赖性的作业方面带来重大的新发展，并通过n层系统改善云的实际使用。

This project addresses computing clouds, large-scale shared infrastructures that offer practically unlimited hardware to most users and applications. In order to achieve scalable performance, all components of the system, from hardware to operating system, middleware, various servers, and the application itself, need to cooperate. Bottlenecks in components can slow down the entire system. In traditional computer systems (e.g., as modeled by queuing theory), a typical assumption is that their workloads consist of independent jobs. This assumption, which is valid for old-style batch-oriented processing and interactive users, guarantees the appearance of single bottlenecks for an entire system. Single bottlenecks can be relatively easily detected, since they appear as resources reaching saturation (e.g., 100% utilization).The "independent jobs" model does not hold for the important class of web-facing applications (e.g., e-commerce) that rely on the popular n-tier architecture. N-tier systems divide the system into a pipeline of processing components, e.g., consisting of web servers, application servers, and database servers. While the n-tier architecture supports good performance scalability at the web server and application server tiers, it also introduces several (sometimes unexpected) strong dependencies among other tiers and components. These dependencies produce an interesting phenomenon called multi-bottleneck. Multi-bottlenecks are characterized by system throughput limited by a ceiling regardless of additional hardware, and no single resource shows average utilization anywhere near saturation. (Anecdotally, this is an increasingly common situation in practice.) Multi-bottlenecks are difficult to find, diagnose, and remove when using traditional performance evaluation methods. They are also important in clouds since they will be the only bottlenecks left after the removal of easily spotted single bottlenecks.This project develops, evaluates, and refines a systematic search method, called Telescoping, to find multi-bottlenecks by running large scale experiments on production clouds. A simulator generates well-defined multi-bottlenecks to help refine the Telescoping search method and tune its parameters. Then, n-tier benchmarks such as RUBiS and RUBBoS (e-commerce applications) on production clouds such as Open Cirrus, Amazon EC2, and Emulab, gather experimental evidence on multi-bottlenecks. These experiments shed light on a little-known phenomenon in a rich, but unexplored area (performance limits of jobs with dependencies). Success can lead to significant new developments in the theoretical understanding of jobs with dependencies and improve practical uses of clouds by n-tier systems.