Parameter estimation for performance models of distributed application systems
CASCON '95 Proceedings of the 1995 conference of the Centre for Advanced Studies on Collaborative research
Parameter inference of queueing models for IT systems using end-to-end measurements
Performance Evaluation
Performance Model Estimation and Tracking Using Optimal Filters
IEEE Transactions on Software Engineering
Automated extraction of architecture-level performance models of distributed component-based systems
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
CLOUD '12 Proceedings of the 2012 IEEE Fifth International Conference on Cloud Computing
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When creating a performance model, it is necessary to quantify the amount of resources consumed by an application serving individual requests. In distributed enterprise systems, these resource demands usually cannot be observed directly, their estimation is a major challenge. Different statistical approaches to resource demand estimation based on monitoring data have been proposed, e.g., using linear regression or Kalman filtering techniques. In this paper, we present LibReDE, a library of ready-to-use implementations of approaches to resource demand estimation that can be used for online and offline analysis. It is the first publicly available tool for this task and aims at supporting performance engineers during performance model construction. The library enables the quick comparison of the estimation accuracy of different approaches in a given context and thus helps to select an optimal one.