| Summary: | Coflow is a typical traffic model based on a parallel computing framework.Reducing the Coflow Completion Time (CCT) has become a popular research topic in cloud computing.The existing Coflow scheduling mechanism does not consider the network bottleneck in the cloud data center, which easily causes network congestion and increases the CCT.Hence, Coflow scheduling mechanism, Bamq, based on bottleneck perception is constructed.The Lagrange duality is used to optimize the Coflow scheduling model such that the Coflow flow rate and throughput are increased, whereas the CCT is reduced.The multi-level feedback queue mechanism reduces the effect of throughput on network congestion.Based on the size, width, and flow rate of the sent flow, the bottleneck factor is constructed to dynamically adjust the priority of multi-level queue, realize congestion perception, and enhance the performance of Coflow scheduling.Experiments on the Facebook dataset show that compared with the Baraat, Varys and Aalo mechanisms, the CCT of this mechanism is reduced by 21.3%, whereas the throughput is increased by 17.9% on average.The proposed mechanism can significantly reduce the CCT and effectively improve link utilization.
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