Quantifying the Temporal Characteristics of Congestion Events in the Internet

量化互联网拥塞事件的时间特征

• 批准号: 0125410
• 负责人: Victor Frost
• 金额: --
• 依托单位: University of Kansas Center for Research Inc
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-15 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0125410&HistoricalAwards=false
• 关键词: Quantifying; Temporal; Characteristics; Congestion; Events

The objective of this research is to lay the foundation for a QoS metric that can be easily understood and can be used for the design of future packet networks. The premise of the proposed research is that Quality of Service (QoS) mechanisms have not been widely employed because of both their complexity and their lack of relationship to user perceived performance. Therefore, it is currently difficult to justify the investment in complex QoS technologies while their value to the end customer is unknown. There have been cases where commercial networks have been over provisioned, resulting in all users, independent of QoS requirement or priority, receiving the same service. Based on these experiences network customers are reluctant to pay extra for QoS. Historically, the telephony QoS metric, call blocking probability, was not only easily understood but provided the foundation for network design. Clearly this metric is not suitable for packet networks like the Internet, since packet networks experience delay and packet loss in the presence of congestion. Recently, it has become increasingly apparent that temporal characteristics of the congestion episodes have the dominant effect on user perceived QoS. Reinforcing the increasing importance of the temporal characteristics of congestion is the recent work in the Internet Engineering Task Force (IETF) on measurement-based temporal QoS metrics. Once congestion occurs at a bottleneck point in the network it tends to persist and cause a user observable impairment. Thus, there is a need to increase our understanding of the temporal characteristics of congestion. To address this issue a new QoS metric is defined, the rate of congestion events per unit time, and a research effort identified to develop analytic methodologies to study this QoS metric. Specifically, this research will focus on: 1) predicting the frequency of congestion events for long range dependent (LDR) like traffic, i.e., fractional Brownian motion, in a network context, 2) developing simple approximations for predicting the frequency of congestion events, 3) validating the resulting prediction methodologies for networks using real high resolution traffic measurements, 4) developing efficient techniques to measure the rate of congestion events, and 5) relating the congestion event metric proposed here to new measurement-based temporal QoS metrics. A rigorous definition is proposed for the time between congestion episodes, which leads to the application of first hitting time analysis for its prediction. The proposed research will exploit some recent theoretical results as well develop new methods for predicting first hitting times for a realistic traffic. Network users have been struggling to quantify their perceived performance. The new QoS metric proposed here summarizes the network component of performance in one easily understandable number. Equally important, the proposed metric is structured in such a way that it can be used for network engineering. We expect the proposed metric, combined with accurate prediction techniques, will play an important role in designing future packet networks.

本研究的目的是为易于理解并可用于未来分组网络设计的QoS指标奠定基础。 所提出的研究的前提是，服务质量（QoS）机制没有被广泛采用，因为他们的复杂性和他们缺乏关系，用户感知的性能。 因此，目前很难证明在复杂的QoS技术上的投资是合理的，而它们对最终客户的价值是未知的。 在某些情况下，商业网络被过度配置，导致所有用户接收相同的服务，而与QoS要求或优先级无关。 基于这些经验，网络客户不愿意为QoS支付额外的费用。 从历史上看，电话QoS度量（呼叫阻塞概率）不仅易于理解，而且为网络设计提供了基础。 显然，这个度量不适合于像因特网这样的分组网络，因为分组网络在拥塞的情况下会经历延迟和分组丢失。最近，它已经变得越来越明显，时间特性的拥塞事件有主导作用的用户感知的QoS。 加强拥塞的时间特性的重要性日益增加是最近的工作在互联网工程任务组（IETF）的测量为基础的时间QoS指标。 一旦拥塞发生在网络中的瓶颈点，它往往会持续下去，并导致用户可观察到的损害。 因此，有必要增加我们的时间特性的拥堵的理解。 为了解决这个问题，定义了一个新的QoS度量，每单位时间的拥塞事件的速率，并确定了一个研究工作，以开发分析方法来研究这个QoS度量。 具体而言，本研究将集中在：1）预测拥塞事件的频率为长距离相关（LDR）一样的交通，即，分数布朗运动，在一个网络的情况下，2）开发简单的近似预测拥塞事件的频率，3）使用真实的高分辨率流量测量验证网络的预测方法，4）开发有效的技术来测量拥塞事件的速率，和5）相关的拥塞事件度量这里提出的新的基于测量的时间QoS度量。 提出了一个严格的定义的时间之间的拥塞事件，这导致了应用的第一次击中时间分析其预测。该研究将利用一些最新的理论成果，以及开发新的方法来预测一个现实的交通首次击中时间。 网络用户一直在努力量化他们的感知性能。 这里提出的新的QoS度量总结了网络组件的性能在一个容易理解的数字。 同样重要的是，所提出的度量结构的方式，它可以用于网络工程。 我们期望所提出的度量，结合准确的预测技术，将在设计未来的分组网络中发挥重要作用。