Injecting realistic burstiness to a traditional client-server benchmark
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Estimating service resource consumption from response time measurements
Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools
Service time estimation with a refinement enhanced hybrid clustering algorithm
ASMTA'10 Proceedings of the 17th international conference on Analytical and stochastic modeling techniques and applications
Automatic, load-independent detection of performance regressions by transaction profiles
Proceedings of the 2013 International Workshop on Joining AcadeMiA and Industry Contributions to testing Automation
Indirect estimation of service demands in the presence of structural changes
Performance Evaluation
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Traditional approaches for capacity planning are based on queueing network models. However, modeling with queueing networks requires the knowledge of the service demands of each class of workloads at each device described in the model. In real systems, such service demands can be very difficult to measure. In this paper, we present an optimization-based technique to address the problem. The technique is formulated as a robust linear parameter estimation that can be used with both closed and open queueing network models. We consider the case where aggregate measurements (throughput and utilization) are available. Such measurements are typically much easier to obtain than the service demands. We present experimental results which prove the effectiveness of the constrained and robust linear estimation.