Applying Learning Theory to Networking Problems

将学习理论应用于网络问题

• 批准号: 9734940
• 负责人: Sally Goldman
• 金额: $ 11.92万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-07-15 至 2001-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9734940&HistoricalAwards=false
• 关键词: Applying; Learning; Theory; Networking; Problems

This project will focus on applying results developed by learning theory researchers to real-life problems. Such application-based theoretical work is important so that the learning models and problems studied capture as best possible the needs of real-life problems. Working to apply theoretical techniques to real problems creates a better understanding of the real-world problems and thus can help direct the future theoretical work, facilitating the transfer of results from theory to practice. Many networking protocols (as well as other system control algorithms) utilize one or more tunable parameters, e.g. thresholds and window sizes. Frequently the methods used to set or adjust the values of the parameters are ad hoc and are based on certain assumptions about the operating environment, e.g. assuming a particular distribution on the traffic patterns in a communication network. This project will develop framework based on formal learning methods for automatic parameter tuning in system control algorithms to study if better performance can be obtained. One such networking application this project will study is that of dynamically adjusting delays of acknowledgements in the TCP protocol. Delaying acknowledgements has two main advantages. First, it allows a single acknowledgement for more than one packet. Second, if a data packet is being sent in the opposite direction, then we can piggy-back the acknowledgement too much can increase the latency. Most TCP implementations used today employ some sort of acknowledgement delay mechanism. This project investigates several schemes to dynamically adjust acknowledgement delay, including ones based on the Weighted Majority (WM) algorithms and ones based on the Exponentially Weighted Moving Average (EWMA) algorithm. These schemes will be contrasted based on efficiency and efficacy , as well as proving theoretical results about them in terms of how well they respond to "volatile" TCP connections. That is, if the connecti on occasionally switches from sparse data transmission (where the goal might be to minimize latency, as in the case of an interactive application) to dense data transmission (where the goal might be to minimize the number of acknowledgements and latency is not as important, as in the case of a file transfer) and back again. Simulation results will be used to supplement the theoretical results. Applying learning theory results to this problem and other networking problems will then provide guidance in defining new theoretical learning models that better model real-life scenarios. This project will also carefully develop and study such new learning models.

该项目将侧重于将学习理论研究人员开发的结果应用于现实生活中的问题。 这种基于应用的理论工作是重要的，以便学习模型和问题研究尽可能捕捉现实生活中的问题的需要。 努力将理论技术应用于真实的问题，可以更好地理解现实世界的问题，从而有助于指导未来的理论工作，促进理论成果向实践的转化。 许多网络协议（以及其他系统控制算法）利用一个或多个可调参数，例如阈值和窗口大小。 通常，用于设置或调整参数值的方法是自组织的，并且基于关于操作环境的某些假设，例如，假设通信网络中的业务模式的特定分布。本计画将发展以形式学习方法为基础的系统控制演算法自动参数调整架构，以研究是否能获得较佳的效能。本项目将研究的一个这样的网络应用是在TCP协议中动态调整应答延迟。 延迟确认有两个主要优点。 首先，它允许对多个数据包进行单个确认。 第二，如果数据包是以相反的方向发送的，那么我们可以背负太多的确认会增加延迟。 目前使用的大多数TCP实现都采用某种确认延迟机制。本文研究了几种动态调整确认延迟的方案，包括基于加权多数（WM）算法的方案和基于指数加权移动平均（EWMA）算法的方案。 这些方案将根据效率和功效进行对比，并证明它们对“易变”TCP连接的响应程度的理论结果。 也就是说，如果连接偶尔从稀疏数据传输（目标可能是最小化延迟，如在交互式应用程序的情况下）切换到密集数据传输（目标可能是最小化应答次数，并且延迟不那么重要，如在文件传输的情况下）并再次切换回来。 仿真结果将被用来补充理论结果。将学习理论的结果应用于这个问题和其他网络问题，将为定义新的理论学习模型提供指导，这些模型可以更好地模拟现实生活场景。 本项目还将认真开发和研究这种新的学习模式。