| Summary: | In recent years, Coflow scheduling has become a research hotspot in data center network. However, it is difficult for existing non-clairvoyant Coflow schedulers to infer the task information quickly. Therefore, small tasks cannot be scheduled in time, making it fail to minimize the average task completion time. Data center network requires effective inferring model to improve the accuracy and sensitivity in inferring Coflow size. This paper proposes a machine learning based Coflow size inferring model (MLcoflow), which utilizes an extreme learning machine (ELM) to establish Coflow size inferring model to minimize training error, and uses the incomplete infor-mation in training to increase the sensitivity. Experiment results show that the accurate score and sensitivity of ELM method are 19.8% and 10.2% higher than other algorithms on average, respectively. This paper compares several schedulers by simulation. MLcoflow-based scheduler reduces the average task completion time by up to 20.1%.
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