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Workloads and resource usage patterns in enterprise applications often show burstiness resulting in large degradation of the perceived user performance. In this paper, we propose a methodology for detecting burstiness symptoms in multi-tier applications but, rather than identifying the root cause of burstiness, we incorporate this information into models for performance prediction. The 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 times of that server. The index of dispersion is used to derive a Markov-modulated process that captures burstiness and variability of the service process at each resource well and that allows us to define queueing network models for performance prediction. Experimental results and performance model predictions are in excellent agreement and argue for the effectiveness of the proposed methodology under both bursty and nonbursty workloads. Furthermore, we show that the methodology extends to modeling flash crowds that create burstiness in the stream of requests incoming to the application.