sis 16(9): e2

Research Article

Flexible Performance Prediction of Data Center Networks using Automatically Generated Simulation Models

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  • @ARTICLE{10.4108/eai.24-8-2015.2260961,
        author={Piotr Rygielski and Samuel Kounev and Phuoc Tran-Gia},
        title={Flexible Performance Prediction of Data Center Networks using Automatically Generated Simulation Models},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={3},
        number={9},
        publisher={ACM},
        journal_a={SIS},
        year={2015},
        month={8},
        keywords={performance modeling, data center networks, meta-modeling},
        doi={10.4108/eai.24-8-2015.2260961}
    }
    
  • Piotr Rygielski
    Samuel Kounev
    Phuoc Tran-Gia
    Year: 2015
    Flexible Performance Prediction of Data Center Networks using Automatically Generated Simulation Models
    SIS
    EAI
    DOI: 10.4108/eai.24-8-2015.2260961
Piotr Rygielski1,*, Samuel Kounev1, Phuoc Tran-Gia1
  • 1: University of Wuerzburg
*Contact email: piotr.rygielski@uni-wuerzburg.de

Abstract

Using different modeling and simulation approaches for predicting network performance requires extensive experience and involves a number of time consuming manual steps regarding each of the modeling formalisms. In this paper, we propose a generic approach to modeling the performance of data center networks. The approach offers multiple performance models but requires to use only a single modeling language. We propose a two-step modeling methodology, in which a high-level descriptive model of the network is built in the first step, and in the second step model-to-model transformations are used to automatically transform the descriptive model to different network simulation models. % We automatically generate three performance models defined at different levels of abstraction to analyze network throughput. By offering multiple simulation models in parallel, we provide flexibility in trading-off between the modeling accuracy and the simulation overhead. We analyze the simulation models by comparing the prediction accuracy with respect to the simulation duration. We observe, that in the investigated scenarios the solution duration of coarser simulation models is up to 300 times shorter, whereas the average prediction accuracy decreases only by 4 percent.