Collaborative Research: Flow Level Models and the Design of Flow-aware Networks

协作研究：流级模型和流感知网络的设计

• 批准号: 0728554
• 负责人: Devavrat Shah
• 金额: $ 38万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-10-01 至 2012-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0728554&HistoricalAwards=false
• 关键词: Collaborative; Research; Flow; Level; Models

This program undertakes a broad research agenda centered around the design and analysis of ``Flow-based Networks''. A flow is a collection of packets that belong to the same ``transaction'', such as a datagram, an ftp transfer, or a web download. It is the fundamental unit of data that a user cares about. Current packet-switched networks, like the Internet and Gigabit Ethernet, are designed to process packets; they are unaware of the flow to which a packet belongs. This is because flow-recognition is widely considered to be too expensive to implement. However, a switch or a router's ability to recognize flows can lead to a marked improvement in its performance, to a better use of its resources, and to much more secure networks. The first major aim of this program is to design novel algorithms and data structures for high-speed, "flow-aware" networks. Such algorithms could heavily influence the design of commercial switches and routers. A second major thrust concerns the development of flow-level models of networks: models which capture the impact of packet-level decisions on flow-level bandwidth allocation and flow processing times. An important component of the modeling work is the unification and generalization of two research enterprises: Stochastic Network Theory, and Large Random Networks. The former studies the performance of a, typically non-random, queueing network subject to "random inputs". The latter concerns the study of "random networks", usually subject to deterministic inputs. A successful outcome of these efforts can help answer questions such as the throughput and flow delay of a particular bandwidth allocation scheme, and the effect of routing topology on end-to-end performance. In other words, the modeling effort aims to develop a realistic, simple and usable class of models for network flows.

该计划承担了围绕“基于流的网络”的设计和分析的广泛研究议程。流是属于同一“事务”的数据包的集合，例如数据报、FTP 传输或 Web 下载。它是用户关心的数据的基本单位。当前的数据包交换网络，如互联网和千兆位以太网，旨在处理数据包；他们不知道数据包所属的流。这是因为人们普遍认为流量识别的实施成本太高。然而，交换机或路由器识别流量的能力可以显着提高其性能，更好地利用其资源，并使网络更加安全。该计划的第一个主要目标是为高速“流量感知”网络设计新颖的算法和数据结构。此类算法可能会严重影响商业交换机和路由器的设计。第二个主要推动力涉及网络流级模型的开发：捕获数据包级决策对流级带宽分配和流处理时间的影响的模型。建模工作的一个重要组成部分是两个研究领域的统一和推广：随机网络理论和大型随机网络。前者研究受“随机输入”影响的通常非随机的排队网络的性能。后者涉及“随机网络”的研究，通常受到确定性输入的影响。这些努力的成功结果可以帮助回答诸如特定带宽分配方案的吞吐量和流延迟以及路由拓扑对端到端性能的影响等问题。换句话说，建模工作旨在开发一类现实、简单且可用的网络流模型。