LibReDE: Library for Resource Demand Estimation

LibReDE is a library for resource demand estimation. Resource demands are a common input parameter to stochastic performance models (e.g., Queueing Networks, or Queueing Petri Nets). LibReDE helps to determine resource demand values based on monitoring data from a system (e.g., CPU utilization, response time, or throughput).

A resource demand is the time a unit of work (e.g., request or transaction) spends obtaining service from a resource (e.g., CPU or hard disk) in a system. Resource demands are input parameters of widely used stochastic performance formalisms (e.g., Queueing Networks or Queueing Petri Nets). In order to obtain accurate performance predictions for a system, a performance engineer needs to determine representative values for the resource demands during performance model construction.

Given that there are no publicly available implementations of estimation approaches, a performance engineer is currently forced to implement estimation approaches on his own. This is a time-consuming and error-prone task. LibReDE is a library supporting performance engineers to determine resource demands by providing a set of ready-to-use implementations of estimation approaches. Based on the actual system and the available monitoring data, the estimation library can automatically determine a set of candidate estimation approaches and execute them. A performance engineer can then validate the resulting resource demand estimates and select the approach that yields the best results. Furthermore, the library also provides a framework that can be used as a basis by developers of estimation approaches. Through reuse, the effort for adapting existing estimation approaches or for implementing new ones, can be significantly reduced.


  • JRE: Java 1.8 or higher (64-bit)
  • Windows 7/8/10 or higher
  • GNU/Linux 64-bit (with gfortran library installed)
  • For using the graphical editor: Eclipse Standard 4.4 or higher



  • Simon Spinner
  • Johannes Grohmann
  • Samuel Kounev


  • Johannes Grohmann, johannes.grohmann(at)
    University Würzburg
    Sanderring 2, 97070 Würzburg, Germany




Eclipse Public License (EPL) v1.0


Related publications and projects

  • André Bauer, Nikolas Herbst, Simon Spinner, Ahmed Ali-Eldin, and Samuel Kounev. Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field. IEEE Transactions on Parallel and Distributed Systems, 30(4):800--813, April 2019, IEEE.
  • Simon Spinner, Johannes Grohmann, Simon Eismann, and Samuel Kounev. Online model learning for self-aware computing infrastructures. Journal of Systems and Software, 147:1 -- 16, 2019.
  • André Bauer, Johannes Grohmann, Nikolas Herbst, and Samuel Kounev. On the Value of Service Demand Estimation for Auto-Scaling. In Proceedings of 19th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (MMB 2018), Erlangen, Germany, February 26--28, 2018. Springer. February 2018.
  • Johannes Grohmann, Nikolas Herbst, Simon Spinner, and Samuel Kounev. Self-Tuning Resource Demand Estimation. In Proceedings of the 14th IEEE International Conference on Autonomic Computing (ICAC 2017), Columbus, OH, July 17--21, 2017.
  • Felix Willnecker, Markus Dlugi, Andreas Brunnert, Simon Spinner, Samuel Kounev, and Helmut Krcmar. Comparing the Accuracy of Resource Demand Measurement and Estimation Techniques. In Computer Performance Engineering - Proceedings of the 12th European Workshop (EPEW 2015), Marta Beltrán, William Knottenbelt, and Jeremy Bradley, editors, Madrid, Spain, August 2015, volume 9272 of Lecture Notes in Computer Science, pages 115--129. Springer. August 2015.
  • Simon Spinner, Giuliano Casale, Fabian Brosig, and Samuel Kounev. Evaluating Approaches to Resource Demand Estimation. Performance Evaluation, 92:51 -- 71, October 2015, Elsevier B.V.
  • Simon Spinner, Giuliano Casale, Xiaoyun Zhu, and Samuel Kounev. LibReDE: A Library for Resource Demand Estimation (Demo Paper). In Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014), Dublin, Ireland, March 22--26, 2014, pages 227--228. ACM Press, New York, NY, USA. March 2014.