A Framework for Robust Measurement-Based Admission Control

综合业务网络需要承载一类具有可变比特率并需要服务质量(Qos)保证的业务。主要挑战之一是向用户提供服务质量，同时通过统计多路复用有效地共享网络资源。准入控制扮演着限制允许进入网络的流的数量以使每个单独的流获得所需的服务质量的重要角色。基于测量的接纳控制(MBAC)是一种实现这一目标的实用方法，它通过测量当前和过去的可变流量负载来决定网络中可以接纳多少流。它们包括：-估计误差对性能的影响；-流动力学的影响，例如流的到达和离开；-测量窗口大小的选择。拟议项目的目标是在MBAC中建立一个统一的框架来研究这些和其他问题。我们希望获得一套设计MBAC的指导原则，以便在广泛的系统参数范围内保持健壮性。我们的方法是从一组基本模型的分析中获得洞察力，然后通过对真实和合成业务源的模拟实验进行验证。对一个简单的无缓冲区模型进行初步研究，得到了相关性能指标作为关键参数(如系统大小、测量窗口大小和流量到达和离开速率)的简单表达式。该分析为回答上述问题提供了基本的见解。一个特别有趣的结果是确定了一个重要的参数~T_h，我们称之为MBAC问题的临界时间尺度。该参数量化了MBAC方案必须预测到未来以提供良好性能的时间量，并在很大程度上确定了适当的测量窗口大小。该研究建议在此基础上建立并扩展框架以研究以下问题：-使用聚合流量测量的MBAC；-用于缓冲链路的MBAC；-长距离相关性、多时间尺度交通动态和非平稳性的影响；-使用直接的Qos监控相对于统计参数的测量的MBAC。

Integrated-services networks are expected to carry a class of traffic that has variable bit-rates and requires Quality of Service (QoS) guarantees. One of the main challenges is the provisioning of QoS to users while efficiently sharing network resources through statistical multiplexing. Admission control plays the essential role of limiting the number of flows admitted into the network such that each individual flow obtains the desired QoS. Measurement-based admission control (MBAC) is a practical way of achieving this objective through measuring the current and past variable traffic load in deciding how many flows can be admitted into the network.Many issues have to be understood in the design of robust MBAC schemes. They include:- impact of estimation errors on performance;- effect of flow dynamics such as flow arrivals and departures;- choice of measurement window size.The goal of the proposed project is to establish a unified framework to study these and other issues in MBAC. We wish to obtain a set of guidelines for designing MBACs that are robust over a wide range of system parameters. Our methodology is to obtain insights from the analysis of a set of basic models, which are in turn validated with simulation experiments on real and synthetic traffic sources.The preliminary investigation on a simple bufferless model yields simple expressions for the relevant performance measures as a function of key parameters such as system size, measurement window size, andflow arrival and departure rates. The analysis provides basic insights for answering the questions asked above. One particularly interesting result is the identification of an important parameter ~T_hwhich we call the critical time-scale for the MBAC problem. This parameter quantifies the amount of time a MBAC scheme has to predict into the future to provide good performance, and determine to a large extent the appropriate measurement window size.The research proposes to build on this foundation and extend the framework to study the following issues:- MBAC using aggregate versus individual flow measurements;- MBAC for buffered links;- impact of long-range dependency, multiple time-scale traffic dynamics, and non-stationarities;- MBAC using direct QoS monitoring versus measurements of statistical parameters.