Coflow Scheduling With Unknown Prior Information in Data Center Networks

摘要

In order to solve the problem of flow scheduling in cluster computing framework, the scheduling strategy based on coflow has become a research hot spot. A coflow is a collection of data flows between two different stages of the same parallel computing task. Coflow scheduling in the case of unknown prior information depends on the data flow information of the sent part to infer the data size of coflow and allocate the scheduling sequence for coflow, which is easy to cause congestion. In this paper, we design an effective coflow scheduling mechanism namely, Classification According to Ports Number (CAPN). In the mechanism, firstly, coflows are quickly classified according to the Few Ports Number Scheduling First (FPSF) algorithm, and then coflows with different priorities are scheduled and adjusted, which greatly reduce the average coflow completion time (CCT). Simulation results show that compared with the classical Aalo and MCS mechanisms, our CAPN mechanism can reduce the completion time of coflow by by 31.32% and 25.72%, respectively.