ANI/STI: Measurements, Models, and Simulation Scenarios for Internet Research

ANI/STI：互联网研究的测量、模型和模拟场景

• 批准号: 0230921
• 负责人: Sally Floyd
• 金额: --
• 依托单位: International Computer Science Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-12-01 至 2007-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0230921&HistoricalAwards=false
• 关键词: ANI; STI; Measurements; Models; Simulation

Networking researchers work from mental models of the Internet's important properties. The scenarios used in simulations and experiments reveal aspects of these mental models, often including one or more of the following: (1) Flows live for a long time and transfer a lot of data. Simple topologies, like the "dumbbell" topology, with one congested link, are sufficient to study many traffic properties(2) Flows on the congested link share a small range of round-trip times(3) Most traffic across the link is one-way (4) Reverse-path traffic is rarely congestedAll of these models can deeply affect simulation and experimental results, and therefore evaluations of research, but none of them are confirmed by measurement studies, and some are actively wrong. Some divergences from reality are unimportant, in that they don't affect the validity of simulation results, and simple models help people understand the underlying dynamics of systems. However, as a community we do not yet understand which aspects of models affect fundamental system behavior, and which aspects can safely be ignored. This project proposes to build a much richer understanding of the range of realistic network models, and of the likely relevance of different model parameters to network performance. The lack of good measurements, lack of tools for evaluating measurement results and applying their results to models, and lack of diverse and well-understood simulation scenarios based on these models, are holding back the field. This program will directly address the models used by network researchers in simulations and experiments. Building on the wealth of existing measurement results and generating new empirical results where needed, this will create high-quality tools to facilitate ongoing measurement by this group and the wider network research community, so that the results keep up with the Internet's high rate of change. Synthesizing the relevant body of measurement results will provide concise intuitions, and a range of simulation-ready models, for use by researchers. An illustration of how each aspect of the models can influence simulations and experiments will be given.This work will not create a universal model of the global Internet (an immense and unnecessary undertaking). Instead, it will focus on models used by researchers in specific research areas, motivated by addressing their pressing modeling concerns. Focusing at first on the group's own areas of expertise, particularly modeling congestion-related mechanisms at a queue in a router, and later address other research areas, help others improve their measurements and measurement tools, along with the resulting models and simulation scenarios, and improve the relationships between experiments, models, and our shared understanding of simulation results.

网络研究人员根据互联网重要属性的心理模型开展工作。模拟和实验中使用的场景揭示了这些心理模型的各个方面，通常包括以下一项或多项：（1）流存在很长时间并传输大量数据。简单的拓扑（例如具有一条拥塞链路的“哑铃”拓扑）足以研究许多流量属性(2) 拥塞链路上的流量共享小范围的往返时间(3) 通过链路的大多数流量是一(4)反向路径交通很少拥堵所有这些模型都会深刻影响模拟和实验结果，从而影响研究的评估，但没有一个得到测量研究的证实，有些模型是完全错误的。一些与现实的偏差并不重要，因为它们不会影响模拟结果的有效性，简单的模型可以帮助人们理解系统的底层动态。然而，作为一个社区，我们还不了解模型的哪些方面会影响基本系统行为，以及哪些方面可以安全地忽略。该项目建议对现实网络模型的范围以及不同模型参数与网络性能的可能相关性建立更丰富的理解。缺乏良好的测量、缺乏评估测量结果并将其结果应用于模型的工具，以及缺乏基于这些模型的多样化且易于理解的模拟场景，正在阻碍该领域的发展。该程序将直接解决网络研究人员在模拟和实验中使用的模型。以丰富的现有测量结果为基础，并在需要时生成新的实证结果，这将创建高质量的工具，以促进该小组和更广泛的网络研究社区的持续测量，从而使结果跟上互联网的高变化率。综合相关的测量结果将提供简洁的直觉和一系列可用于模拟的模型，供研究人员使用。将给出模型的各个方面如何影响模拟和实验的说明。这项工作不会创建全球互联网的通用模型（一项巨大且不必要的任务）。相反，它将重点关注研究人员在特定研究领域使用的模型，其动机是解决他们紧迫的建模问题。首先关注该小组自己的专业领域，特别是对路由器队列中与拥塞相关的机制进行建模，然后解决其他研究领域，帮助其他人改进他们的测量和测量工具，以及由此产生的模型和模拟场景，以及改善实验、模型和我们对模拟结果的共同理解之间的关系。