Chair of Computer Science II - Software Engineering

    GFFT Best Master Thesis Prize


    Jóakim v. Kistowski, doctoral researcher at the Chair of Software Engineering (Informatik II), wins this year's GFFT Prize 2015 for Best Master Thesis, given by the Gesellschaft zur Förderung des Forschungstransfers e.V.

    Jóakim v. Kistowski was awarded the highly competitive GFFT-Förderpreis (Gesellschaft zur Förderung des Forschungstransfers e.V.) in the category "Best Master Thesis" for his thesis with the title "Modeling Variations in Load Intensity Profiles" supervised by Nikolas Herbst and Rouben Krebs.

    The thesis produced the LIMBO tool available here.

    In addition, the following publications are a direct result from the work on the thesis:

    [4]Jóakim von Kistowski, Nikolas Roman Herbst, Daniel Zoller, Samuel Kounev, and Andreas Hotho. Modeling and Extracting Load Intensity Profiles. InProceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2015), Firenze, Italy, May 18-19, 2015. Accepted for publication, Acceptance rate: 29%. [ bib |.pdf | Abstract  ]
    [3]Jóakim von Kistowski, Nikolas Roman Herbst, and Samuel Kounev. LIMBO: A Tool For Modeling Variable Load Intensities (Demo Paper). In Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014), Dublin, Ireland, March 22-26, 2014, ICPE '14, pages 225-226. ACM, New York, NY, USA. March 2014. [ bib | DOI | slides | http |.pdf | Abstract  ]
    [2]Jóakim von Kistowski, Nikolas Roman Herbst, and Samuel Kounev. Modeling Variations in Load Intensity over Time. In Proceedings of the 3rd International Workshop on Large-Scale Testing (LT 2014), co-located with the 5th ACM/SPEC International Conference on Performance Engineering (ICPE 2014), Dublin, Ireland, March 22, 2014, pages 1-4. ACM, New York, NY, USA. March 2014. [ bib | DOI | slides | http | .pdf | Abstract  ]

    Jóakim von Kistowski, Nikolas Roman Herbst, and Samuel Kounev. Using and Extending LIMBO for the Descriptive Modeling of Arrival Behaviors. InProceedings of the Symposium on Software Performance 2014, Stuttgart, Germany, November 2014, pages 131-140. University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology. November 2014, Best Poster Award. [ bib | slides | .pdf | Abstract  ]

    Thesis Abstract: Today's software systems are expected to deliver reliable performance under highly variable load intensities while at the same time making efficient use of dynamically allocated resources. Conventional benchmarking frameworks provide limited support for emulating such highly variable and dynamic load profiles and workload scenarios. Industrial benchmarks typically use workloads with constant or stepwise increasing load intensity, or they simply replay recorded workload traces. Based on this observation, I identify the need for means allowing flexible definition of load profiles and address this by introducing two meta-models at different abstraction levels. At the lower abstraction level, the Descartes Load Intensity Meta-Model (DLIM) offers a structured and accessible way of describing the load intensity over time by editing and combining mathematical functions. The high-level Descartes Load Intensity Meta-Model (hl-DLIM) allows the description of load variations using few defined parameters that characterize the seasonal patterns, trends, bursts, and noise parts. Using these parameters, hl-DLIM is capable of describing a subset of most common load intensity variations. During the work on this thesis I developed LIMBO - an Eclipse-based tool for modeling variable load intensity profiles based on DLIM and hl-DLIM as underlying modeling formalisms. LIMBO provides visualization for DLIM instances and a model creation wizard based on hl-DLIM parameters. It also offers three automated model extraction processes with which to extract DLIM and hl-DLIM instances from existing arrival rate traces. It also offers a model-to-model transformation from hl-DLIM to DLIM. I demonstrate that both meta-models are capable of capturing real-world load profiles with acceptable accuracy, having an average median deviation of 19.9% from the original trace. This is done by comparing nine different real life traces, which were measured over duration of two weeks to seven months, to their respective model instances as extracted by the automated model extraction processes. I also evaluate the usability and accessibility of LIMBO based on a questionnaire which was answered by eight computer scientists from five different organizations within the performance engineering community.