Trace data characterization and fitting for Markov modeling

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 previous work. 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 algorith...

• Type: Journal
• Year: 2010
• Where: PE
• Authors: Giuliano Casale, Eddy Z. Zhang, Evgenia Smirni
• Keywords: Algorithm, Interarrival Times, Markovian Arrival, Optimization, PE 2010