How to parameterize models with bursty workloads

Although recent advances in theory indicate that burstiness in the service time process can be handled effectively by queueing models (e.g.,MAP queueing networks [2]), there is a lack of understanding and of practical results on how to perform model parameterization, especially when this parameterization must be derived from limited coarse measurements. We propose a new parameterization methodology based on the index of dispersion of the service process at a server, which is inferred by observing the number of completions within the concatenated busy periods of that server. The index of dispersion together with other measurements that reflect the "estimated" mean and the 95th percentile of service times are used to derive a MAP process that captures well burstiness of the true service process. Detailed experimentation on a TPC-W testbed where all measurements are obtained via a commercially available tool, the HP (Mercury) Diagnostics, shows that the proposed technique offers a simple yet powerful solution to the difficult problem of inferring accurate descriptors of the service time process from coarse measurements. Experimental and model prediction results are in excellent agreement and argue strongly for the effectiveness of the proposed methodology under bursty or simply variable workloads.