KPC-Toolbox: Best recipes for automatic trace fitting using Markovian Arrival Processes

We present the KPC-Toolbox, a library of MATLAB scripts for fitting workload traces into Markovian Arrival Processes (MAPs) in an automatic way based on the recently proposed Kronecker Product Composition (KPC) method. We first present detailed sensitivity analysis that builds intuition on which trace descriptors are the most important for queueing performance, stressing the advantages of matching higher-order correlations of the process rather than higher-order moments of the distribution. Given that the MAP parameterization space can be very large, we focus on first determining the order of the smallest MAP that can fit the trace well using the Bayesian Information Criterion (BIC). The KPC-Toolbox then automatically derives a MAP that captures accurately the most essential features of the trace. Extensive experimentation illustrates the effectiveness of the KPC-Toolbox in fitting traces that are well-documented in the literature as very challenging to fit, showing that the KP...

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