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

该项目解决了计算云，大规模共享基础设施，为大多数用户和应用程序提供了几乎无限的硬件。为了实现可伸缩的性能，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。组件中的瓶颈可能会降低整个系统的运行速度。在传统的计算机系统中（例如，通过排队理论建模），一个典型的假设是它们的工作负载由独立的工作组成。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个瓶颈。单个瓶颈可以相对容易地检测到，因为它们出现在资源达到饱和时（例如，100%的利用率）。“独立工作”模型并不适用于依赖流行的n层架构的面向web的重要应用程序（例如，电子商务）。n层系统将系统划分为处理组件的管道，例如，由web服务器、应用服务器和数据库服务器组成。虽然n层体系结构在web服务器和应用服务器层上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一些（有时是意想不到的）强依赖性。这些依赖关系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，而不考虑额外的硬件，并且没有单个资源的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。）在传统的性能评估方法中，多瓶颈难以发现、诊断和消除。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、评估并改进了一种称为telescope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。模拟器生成定义良好的多瓶颈，以帮助改进伸缩搜索方法并调整其参数。然后，在生产云（如Open Cirrus、Amazon EC2和Emulab）上的n层基准测试，如rubi和RUBBoS（电子商务应用程序），收集关于多瓶颈的实验证据。这些实验揭示了一个丰富但尚未探索的领域中鲜为人知的现象（具有依赖性的工作的性能限制）。成功可以导致对具有依赖性的作业的理论理解的重大新发展，并通过n层系统改进云的实际使用。

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.