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Intern
    Chair of Computer Science II - Software Engineering

    ICST Best Paper Award at SIMUTools2011

    28.03.2011

    The paper "Automated Simulation-Based Capacity Planning for Enterprise Data Fabrics" by S. Kounev, K. Bender, F. Brosig, N. Huber, and R. Okamoto received the Best Paper Award at SIMUTools2011.

    The paper of the Descartes Research Group was selected as the best conference paper from a total of 23 accepted papers selected from 77 submissions in the research track (Acceptance Rate: 23/77=29.8%). The paper is a result of a collaboration of the Descartes Research Group with GemStone Systems which is now part of VMware, Inc.

     

    Samuel Kounev, Konstantin Bender, Fabian Brosig, Nikolaus Huber, and Russell Okamoto. Automated Simulation-Based Capacity Planning for Enterprise Data Fabrics. In4th International ICST Conference on Simulation Tools and Techniques, Barcelona, Spain, 2011. [ bibslides |.pdf ]

     

    Abstract:

    Enterprise data fabrics are gaining increasing attention in many industry domains including fi nancial services, telecommunications, transportation and health care. Providing a distributed, operational data platform sitting between application infrastructures and back-end data sources, enterprise data fabrics are designed for high performance and scalability. However, given the dynamics of modern applications, system sizing and capacity planning need to be done continuously during operation to ensure adequate quality-of-service and efficient resource utilization. While most products are shipped with performance monitoring and analysis tools, such tools are typically focused on low-level profi ling and they lack support for performance prediction and capacity planning. In this paper, we present a novel case study of a representative enterprise data fabric, the GemFire EDF, presenting a simulation-based tool that we have developed for automated performance prediction and capacity planning. The tool, called Jewel, automates resource demand estimation, performance model generation, performance model analysis and results processing. We present an experimental evaluation of the tool demonstrating its effectiveness and practical applicability.

     

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