- Special Issue: Middleware
- Open Access
Sizing multi-tier systems with temporal dependence: benchmarks and analytic models
© The Brazilian Computer Society 2010
Received: 7 March 2010
Accepted: 8 August 2010
Published: 21 September 2010
Temporal dependence, as a synonym for burstiness, is often found in workloads (i.e., arrival flows and/or service times) in enterprise systems that use the multi-tier paradigm. Despite the fact that burstiness has deleterious effects on performance, existing modeling and benchmarking techniques do not provide an effective capacity planning for multi-tier systems with temporal dependence. In this paper, we first present strong evidence that existing models cannot capture bursty conditions and accurately predict performance. Therefore, we propose a simple and effective sizing methodology to integrate workload burstiness into models and benchmarking tools used in system sizing. This modeling methodology is based on the index of dispersion which jointly captures variability and burstiness of the service process in a single number. We report experimentation on a real testbed that validates the accuracy of our modeling technique by showing that experimental and model prediction results are in excellent agreement under both bursty and non-bursty workloads. To further support the capacity planning process under burstiness, we propose an enhanced benchmarking technique that can emulate workload burstiness in systems. We find that most existing benchmarks, like the standard TPC-W benchmark, are designed to assess system performance only under non-bursty conditions. In this work, we rectify this deficiency by introducing a new module into existing benchmarks, which allows to inject burstiness into the arrival stream in a controllable and reproducible manner by using the index of dispersion as a single turnable knob. This approach enables a better understanding of system performance degradation due to burstiness and makes a strong case for the usefulness of the proposed benchmark enhancement for capacity planning of enterprise systems.