Trace data characterization and fitting for Markov modeling

Abstract

We propose a trace fitting algorithm for Markovian Arrival Processes (MAPs) that can capture statistics of any order of interarrival times between measured events. By studying real traffic and workload traces often used in performance evaluation studies, we show that matching higher order statistical properties, in addition to first and second order descriptors, results in increased queueing prediction accuracy with respect to algorithms that only match the mean, the coefficient of variation, and the autocorrelations of the trace. This result supports the approach of modeling traces by the interarrival time process instead of the counting process that is more frequently used in the literature. We proceed by first characterizing the general properties of MAPs using a spectral approach. Based on this result, we show how different MAPs can be combined together using Kronecker products to define a larger MAP with predefined properties of interarrival times. We then devise an algorithm that is based on this Kronecker composition and can accurately fit data traces. This MAP fitting algorithm uses nonlinear optimization that can be customized to fit an arbitrary number of moments and to meet the desired cost-accuracy tradeoff. Numerical results of the fitting algorithm on real data, such as the Bellcore Aug89 trace and a Seagate disk drive trace, indicate that the proposed fitting technique achieves increased prediction accuracy with respect to other state-of-the-art fitting methods.