Burstiness in multi-tier applications: symptoms, causes, and new models

TL;DR: In this paper, the authors proposed a simple and effective methodology for detecting burstiness symptoms in multi-tier systems rather than identifying the low-level exact cause of burstiness as traditional models would require.

Abstract: Workload flows in enterprise systems that use the multi-tier paradigm are often characterized as bursty, i.e., exhibit a form of temporal dependence. Burstiness often results in dramatic degradation of the perceived user performance, which is extremely difficult to capture with existing capacity planning models. The main reason behind this deficiency of traditional capacity planning models is that the user perceived performance is the result of the complex interaction of a very complex workload with a very complex system. In this paper, we propose a simple and effective methodology for detecting burstiness symptoms in multi-tier systems rather than identifying the low-level exact cause of burstiness as traditional models would require. We provide an effective way to incorporate this information into a surprisingly simple and effective modeling methodology. This new modeling methodology is 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 Markov-modulated process that captures well burstiness and variability of the true service process, despite inevitable inaccuracies that result from inexact and limited measurements. Detailed experimentation on a TPC-W testbed where all measurements are obtained by HP (Mercury) Diagnostics, a commercially available tool, 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 of a given system. Experimental and model prediction results are in excellent agreement and argue strongly for the effectiveness of the proposed methodology under both bursty and non-bursty workloads.