Mix-flow scheduling using deep reinforcement learning for software-defined data-center networks

For a mix-flow scenario in software-defined data-center networks, how to simultaneously achieve the different performance requirements of the different types of flows is a considerable challenge. This paper proposes a mix-flow scheduling scheme based on deep reinforcement learning (DRL). This paper establishes three private link sets for three types of flows. Then, DRL is employed to adaptively and intelligently allocate bandwidth for each type of flow according to the traffic variations across time and space. A novel metric is designed as a function of DRL's reward to guide the process of simultaneously maximizing the deadline meet rate for mice flows (MF) and minimizing the flow completion time for elephant flows. Within these three private link sets, three flow-scheduling strategies (ie, priority-based allocation for MF, stable matching-based allocation for elephant flows with unknown sizes, and proportion-based allocation for elephant flows with known sizes) are employed. A simulation demonstrates the effectiveness of the proposed scheme compared with previous methods (Fincher and pFabric). DRL-Flow's overhead also is minimal to satisfy the scalability well and is deployable in a large-scale network.