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PREDICTING MACROSCOPIC DYNAMICS IN LARGE DISTRIBUTED SYSTEMS - PARTI

机译：预测大型分布式系统中的宏观动力学-PARTI

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Society increasingly depends on large distributed systems, such as the Internet and Web-based service-oriented architectures deployed over the Internet. Such systems constantly evolve as new software components are injected to provide increased functionality, better performance and enhanced security. Unfortunately, designers lack effective methods to predict how new components might influence macroscopic behavior. Lacking effective methods, designers rely on engineering techniques, such as: analysis of critical algorithms at small scale and under limiting assumptions; factor-at-a-time simulations conducted at modest scale; and empirical measurements in small test beds. Such engineering techniques enable designers to characterize selected properties of new components but reveal little about likely dynamics at global scale. In this paper, we outline an approach that can be used to predict macroscopic dynamics when new components are deployed in a large distributed system. Our approach combines two main methods: scale reduction and multidimensional data analysis techniques. Combining these methods, we can search a wide parameter space to identify factors likely to drive global system response and we can predict the resulting macroscopic dynamics of key system behaviors. We demonstrate our approach in the context of the Internet, where researchers, motivated by a desire to increase user performance, have proposed new algorithms to replace the standard congestion control mechanism. Previously, the proposed algorithms were studied in three ways: using analytical models of single data flows, using empirical measurements in test beds where a few data flows compete for bandwidth, and using simulations at modest scale with a few sequentially varied parameters. In contrast, by applying our approach, we simulated configurations covering four-tier network topologies, spanning continental and global distances, comprising routers operating at state-of-the-art speeds and transporting more than 105 simultaneous data flows with varying traffic patterns and temporary spatiotemporal congestion. Our findings identify the main factors influencing macroscopic dynamics of Internet congestion control, and define the specific combination of factors that must hold for users to realize improved performance. We also uncover potential for one proposed algorithm to cause widespread performance degradation. Previous engineering studies of the proposed congestion control algorithms were unable to reveal such essential information.
机译：社会越来越依赖大型分布式系统，例如Internet和通过Internet部署的基于Web的面向服务的体系结构。随着注入新的软件组件以提供增强的功能，更好的性能和增强的安全性，此类系统会不断发展。不幸的是，设计人员缺乏有效的方法来预测新组件如何影响宏观行为。缺乏有效的方法，设计人员依赖于工程技术，例如：在有限的假设下以小规模分析关键算法;以适度的规模进行一次因子模拟;和在小型测试床上进行的经验测量。此类工程技术使设计人员能够表征新组件的选定特性，但几乎没有揭示全球范围内可能发生的动态变化。在本文中，我们概述了一种在大型分布式系统中部署新组件时可用于预测宏观动态的方法。我们的方法结合了两种主要方法：规模缩减和多维数据分析技术。结合这些方法，我们可以搜索广阔的参数空间，以识别可能导致全局系统响应的因素，并且可以预测关键系统行为的宏观动态。我们在Internet的上下文中展示了我们的方法，在此背景下，出于提高用户性能的渴望，研究人员提出了新算法来取代标准的拥塞控制机制。以前，以三种方式研究了所提出的算法：使用单个数据流的分析模型，在一些数据流争夺带宽的测试台上使用经验测量，以及使用具有几个顺序变化的参数的适度规模的仿真。相比之下，通过采用我们的方法，我们模拟了涵盖四层网络拓扑结构的配置，跨越了大陆和全球范围的距离，包括以最新速度运行的路由器，并以不同的流量模式和临时性传输了105多个同时的数据流时空拥塞。我们的发现确定了影响互联网拥塞控制宏观动态的主要因素，并定义了用户要实现更高性能所必须具备的特定因素组合。我们还发现了一种提出的算法导致广泛的性能下降的潜力。先前对所提出的拥塞控制算法的工程研究无法揭示这些基本信息。

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