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

CSR：小：多瓶颈：它们是什么以及如何找到它们

• 批准号: 1116451
• 负责人: Calton Pu
• 金额: $ 22.5万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116451&HistoricalAwards=false
• 关键词: CSR; Small; Multi; Bottlenecks; What

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.

该项目涉及计算云、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、各种服务器以及应用程序本身，都需要协作。组件中的瓶颈可能会减慢整个系统的速度。在传统的计算机系统中（例如，通过排队论建模），典型的假设是它们的工作负载由独立的作业组成。这个假设对于旧式的面向批处理的处理和交互式用户是有效的，保证了整个系统单一瓶颈的出现。单个瓶颈可以相对容易地检测到，因为它们表现为资源达到饱和（例如，100% 利用率）。“独立作业”模型不适用于依赖于 Web 的重要一类应用程序（例如，电子商务）。流行的n层架构。 N 层系统将系统划分为处理组件的管道，例如由 Web 服务器、应用程序服务器和数据库服务器组成。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扩展性，但它还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种有趣的现象，称为多瓶颈。多瓶颈的特点是系统吞吐量受到上限的限制，无论额外的硬件如何，并且没有任何单一资源显示出接近饱和的平均利用率。 （据说，这种情况在实践中越来越常见。）使用传统的绩效评估方法时，很难发现、诊断和消除多重瓶颈。它们在云中也很重要，因为它们将是消除容易发现的单一瓶颈后留下的唯一瓶颈。该项目开发、评估和完善一种称为 Telescoping 的系统搜索方法，通过在生产云。模拟器生成明确定义的多瓶颈，以帮助完善伸缩搜索方法并调整其参数。然后，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 层基准测试收集多瓶颈的实验证据。这些实验揭示了一个丰富但未经探索的领域中鲜为人知的现象（具有依赖性的作业的性能限制）。成功可以在对具有依赖性的作业的理论理解方面带来重大的新发展，并改善 n 层系统对云的实际使用。