Data Transfer Scheduling for Maximizing Throughput of Big-Data Computing in Cloud Systems - 2018


Many huge-knowledge computing applications are deployed in cloud platforms. These applications normally demand concurrent data transfers among computing nodes for parallel processing. It is necessary to seek out the most effective transfer scheduling resulting in the least information retrieval time-the maximum throughput in different words. However, the present ways cannot achieve this, as a result of they ignore link bandwidths and the diversity of knowledge replicas and methods. In this Project, we tend to aim to develop a max-throughput knowledge transfer scheduling to minimize the data retrieval time of applications. Specifically, the matter is formulated into mixed integer programming, and an approximation algorithm is proposed, with its approximation ratio analyzed. The extensive simulations demonstrate that our algorithm will get close to optimal solutions.

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