4th International ICST Conference on Simulation Tools and Techniques

Research Article

Automated Simulation-Based Capacity Planning for Enterprise Data Fabrics

Download708 downloads
  • @INPROCEEDINGS{10.4108/icst.simutools.2011.245569,
        author={Samuel Kounev and Konstantin Bender and Fabian Brosig and Nikolaus Huber and Russell Okamoto},
        title={Automated Simulation-Based Capacity Planning for Enterprise Data Fabrics},
        proceedings={4th International ICST Conference on Simulation Tools and Techniques},
        publisher={ICST},
        proceedings_a={SIMUTOOLS},
        year={2012},
        month={4},
        keywords={enterprise data fabrics simulation performance prediction capacity planning},
        doi={10.4108/icst.simutools.2011.245569}
    }
    
  • Samuel Kounev
    Konstantin Bender
    Fabian Brosig
    Nikolaus Huber
    Russell Okamoto
    Year: 2012
    Automated Simulation-Based Capacity Planning for Enterprise Data Fabrics
    SIMUTOOLS
    ICST
    DOI: 10.4108/icst.simutools.2011.245569
Samuel Kounev1,*, Konstantin Bender1, Fabian Brosig1, Nikolaus Huber1, Russell Okamoto2
  • 1: Karlsruhe Institute of Technology
  • 2: VMware, Inc.
*Contact email: kounev@kit.edu

Abstract

Enterprise data fabrics are gaining increasing attention in many industry domains including financial 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 profiling 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.