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

ProMo: A Probabilistic Model for Dynamic Load-Balanced Scheduling of Data Flows in Cloud Systems