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: 0729586
• 负责人: Balaji Prabhakar
• 金额: $ 38万
• 依托单位: Stanford University
• 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 2011-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0729586&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 researce 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.

该计划围绕“基于流量的网络”的设计和分析进行广泛的研究议程。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。它是用户关心的基本数据单元。当前的分组交换网络，如Internet和千兆以太网，被设计为处理分组;它们不知道分组所属的流。这是因为流识别被广泛认为太昂贵而无法实现。然而，交换机或路由器识别数据流的能力可以显著提高其性能，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”网络设计新的算法和数据结构。这些算法可能会严重影响商业交换机和路由器的设计。第二个主要的推力涉及网络的流级模型的发展：模型捕获的影响，数据包级的决定流级的带宽分配和流处理时间。建模工作的一个重要组成部分是两个研究企业的统一和推广：随机网络理论和大型随机网络。前者研究的是一个典型的非随机网络在“随机输入”下的性能。后者涉及研究“随机网络”，通常受到确定性的输入。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，建模工作的目的是开发一个现实的，简单的和可用的网络流模型类。