ΔQ Generative Models: Modeling Time-Variation in Network Quality

Abstract

This work introduces a class of network performance models designed to capture variations in network quality on diverse timescales. By explicitly modeling how quality changes over time, the proposed models enable computation of performance metrics that are beyond the scope of steady-state methods such as Markov chains. We use the quality attenuation (ΔQ) metric to quantify network quality, and ΔQ generative models specify how quality attenuation varies over time. Variation over time is modeled using a finite state machine with timed state transitions. We show how the models can be used to shed light on practical problems by presenting novel results for the problem of buffer sizing. In addition to the buffer sizing results, this work presents the ΔQ generative model structure and the basic algorithms needed to work with the models.