Quantifying the Temporal Characteristics of Congestion Events in the Internet

这项研究的目的是为一种易于理解并可用于未来分组网络设计的Qos度量奠定基础。本研究的前提是服务质量(Quality of Service，简称Qos)机制由于其复杂性和与用户感知性能的关系而没有得到广泛应用。因此，目前很难证明在复杂的服务质量技术上的投资是合理的，因为它们对最终客户的价值是未知的。已经存在商业网络被过度供应的情况，导致所有用户独立于服务质量要求或优先级而接收相同的服务。基于这些经验，网络客户不愿为服务质量额外付费。从历史上看，电话服务质量指标(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，该度量不适用于像Internet这样的分组网络，因为分组网络在存在拥塞的情况下会经历延迟和分组丢失。近年来，拥塞事件的时间特性对用户感知的服务质量具有主导作用，这一点变得越来越明显。互联网工程任务组(IETF)最近在基于测量的时间QoS度量方面的工作加强了拥塞的时间特征的日益重要性。一旦在网络中的瓶颈点处发生拥塞，它往往会持续存在，并导致用户可观察到的损害。因此，有必要增加我们对拥堵的时间特征的了解。为了解决这个问题，定义了一个新的Qos度量，即单位时间的拥塞事件速率，并确定了一项研究工作，以开发分析方法来研究该Qos度量。具体地说，这项研究将集中在：1)预测网络环境中类似长距离相关(LDR)的业务的拥塞事件的频率，即分数布朗运动，2)开发预测拥塞事件频率的简单近似，3)使用真实的高分辨率流量测量来验证所得到的网络预测方法，4)开发有效的技术来测量拥塞事件的速率，以及5)将本文提出的拥塞事件度量与新的基于测量的时间Qos度量相关联。给出了拥堵时段间隔时间的严格定义，并应用首发时间分析方法对拥堵时段间隔时间进行了预测。建议的研究将利用一些最新的理论结果，并开发新的方法来预测现实交通的首次命中时间。网络用户一直在努力量化他们感知到的表现。这里提出的新的Qos度量用一个易于理解的数字概括了网络性能的组成部分。同样重要的是，建议的度量的结构使其可用于网络工程。我们预计，所提出的度量与准确的预测技术相结合，将在未来的分组网络设计中发挥重要作用。

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.