An important issue in cloud computing is the balanced flow of big data centers, which\nusually transfer huge amounts of data. Thus, it is crucial to achieve dynamic, load-balanced data\nflow distributions that can take into account the possible change of states in the network. A number\nof scheduling techniques for achieving load balancing have therefore been proposed. To the best of\nmy knowledge, there is no tool that can be used independently for different algorithms, in order to\nmodel the proposed system (network topology, linking and scheduling algorithm) and use its own\nprobability-based parameters to test it for good balancing and scheduling performance. In this paper,\na new, Probabilistic Model (ProMo) for data flows is proposed, which can be used independently with\na number of techniques to test the most important parameters that determine good load balancing\nand scheduling performance in the network. In this work, ProMo is only used for testing with two\nwell-known dynamic data flow scheduling schemes, and the experimental results verify the fact that\nit is indeed suitable for testing the performance of load-balanced scheduling algorithms.
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