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Model-based throughput prediction in data center networks

机译:数据中心网络中基于模型的吞吐量预测

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In this paper, we address the problem of performance analaysis in computer networks. We present a new meta-model designed for the performance modeling of network infrastructures in modern data centers. Instances of our metamodel can be automatically transformed into stochastic simulation models for performance prediction. We evaluate the approach in a case study of a road traffic monitoring system. We compare the performance prediction results against the real system and a benchmark. The presented results show that our approach, despite of introducing many modeling abstractions, delivers predictions with errors less than 32% and correctly detects bottlenecks in the modeled network.
机译:在本文中,我们解决了计算机网络中的性能分析问题。我们提出了一个新的元模型,旨在对现代数据中心中的网络基础架构进行性能建模。我们的元模型实例可以自动转换为随机仿真模型以进行性能预测。我们在道路交通监控系统的案例研究中评估了该方法。我们将性能预测结果与实际系统和基准进行比较。呈现的结果表明,尽管引入了许多建模抽象,但我们的方法仍能提供误差小于32%的预测,并能正确检测出建模网络中的瓶颈。

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