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Multiple types of flows with contradictory service requirements, namely mixed flows, coexist in the data center network. Similar flows will be aggregated into the same queue after flow classification and are scheduled in switches by using fair queueing and its extension schemes which are capable of flow isolation. These schemes allocate different bandwidths by adjusting weights to realize differentiated services. However, existing solutions only focus on the requirements of some flows, which leads to the failure to satisfy the requirements of other flows. Therefore, it is necessary to make a trade-off between the service requirements of different flows when allocating bandwidth. In this paper, a max-min fairness based scheduling optimization (MMFSO) mechanism is proposed to schedule mixed flows. First, the bandwidth requirements of each queue are calculated by statistics of the switch. To reduce the influence of sampled statistics while forecasting bandwidth requirements, we introduce the exponentially weighted moving average for bandwidth requirements computation. Second, the bandwidth is allocated to each queue according to the max-mix fairness. The queue weight is determined by the allocated bandwidth of the queue. Finally, the performance of the proposed mechanism is evaluated on the hardware testbed in which workloads include coarse-grained flows and fine-grained flows. The results show that MMFSO can effectively schedule mixed flows.

No abstract