A Framework for Robust Measurement-Based Admission Control

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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.