piwik-script

Intern
    Chair of Computer Science II - Software Engineering

    LibReDE

    Library for Resource Demand Estimation

    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.

    LibReDE is a library of ready-to-use implementations of state-of-the-art 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.

    More information can be found on the following pages:

    If you have any questions, please contact Simon Spinner or Johannes Grohmann.

     

    Mailing List

    To stay updated on our tools, please subscribe to our descartes-tools mailing list (low traffic, only announcements related to our tools)

    Your E-mail address:
    Your Name (optional):

    Publications

    2019

    • Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field. A. Bauer; (2019, Juni).
       
    • Utilizing Clustering to Optimize Resource Demand Estimation Approaches. J. Grohmann; S. Eismann; A. Bauer; M. Zuefle; N. Herbst; S. Kounev; in 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) (2019). 134–139.
       
    • Online model learning for self-aware computing infrastructures. S. Spinner; J. Grohmann; S. Eismann; S. Kounev; in Journal of Systems and Software (2019). 147 1–16.
       

    2018

    • On the Value of Service Demand Estimation for Auto-Scaling. A. Bauer; J. Grohmann; N. Herbst; S. Kounev; in Proceedings of 19th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (MMB 2018) (2018). (Bd. 10740) 142–156.
       
    • Using Machine Learning for Recommending Service Demand Estimation Approaches. J. Grohmann; N. Herbst; S. Spinner; S. Kounev; in Proceedings of the 8th International Conference on Cloud Computing and Services Science (CLOSER 2018) (2018). 473–480.
       

    2017

    • Design and Evaluation of a Proactive, Application-Aware Auto-Scaler. A. Bauer; N. Herbst; S. Kounev; in Proceedings of the 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017) (2017).
       
    • Self-Tuning Resource Demand Estimation. J. Grohmann; N. Herbst; S. Spinner; S. Kounev; in Proceedings of the 14th IEEE International Conference on Autonomic Computing (ICAC 2017) (2017). 21–26.
       

    2016

    • Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism. A. Bauer; (2016, November).
       
    • Chameleon: Design and Evaluation of a Proactive, Application-Aware Elasticity Mechanism. A. Bauer; (2016, Oktober).
       
    • Automated Parameterization of Performance Models from Measurements. G. Casale; S. Spinner; W. Wang; in Proceedings of the 7th ACM/SPEC International Conference on Performance Engineering (ICPE 2016) (2016).
       
    • A Reference Architecture for Online Performance Model Extraction in Virtualized Environments. S. Spinner; J. Walter; S. Kounev; in Proceedings of the 2016 Workshop on Challenges in Performance Methods for Software Development (WOSP-C’16) co-located with 7th ACM/SPEC International Conference on Performance Engineering (ICPE 2016) (2016).
       

    2015

    • Evaluating Approaches to Resource Demand Estimation. S. Spinner; G. Casale; F. Brosig; S. Kounev; in Performance Evaluation (2015). 92 51–71.
       
    • Comparing the Accuracy of Resource Demand Measurement and Estimation Techniques. F. Willnecker; M. Dlugi; A. Brunnert; S. Spinner; S. Kounev; H. Krcmar; in Computer Performance Engineering - Proceedings of the 12th European Workshop (EPEW 2015), M. Beltrán, W. Knottenbelt, J. Bradley (Hrsg.) (2015). (Bd. 9272) 115–129.
       

    2014

    • LibReDE: A Library for Resource Demand Estimation. S. Spinner; J. Walter; (2014, November).
       
    • LibReDE: A Library for Resource Demand Estimation. S. Spinner; G. Casale; X. Zhu; S. Kounev; in Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014) (2014). 227–228.
       

    2011

    • Evaluating Approaches to Resource Demand Estimation. S. Spinner; Thesis; Karlsruhe Institute of Technology (KIT); Am Fasanengarten 5, 76131 Karlsruhe, Germany. (2011, Juli).