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This work presents a novel approach in intra-data center network design and performance evaluation, based on a tailored, long-range dependence data traffic generation model for different application classes. We examine how an intra-data center network can be efficiently evaluated and optimized by applying accurate models for machine-generated data and using assumptions on current and future commercially available data center hardware. Moreover, we show that by migrating such a Fat-Tree network to its hybrid, electro-optical counterpart by employing optical circuit switching for selected, intense traffic connections, significant capital and operational expenditure cost savings can be obtained.