DENA: An Intelligent Dynamic Flow Scheduling for Rate Adjustment in Green DCNs We propose DENA as a deep reinforcement learning (DRL) based energyefficient algorithm in the data center. To achieve the goal, DENA adjusts the transmission rate for each flow with deadlines and reduce the flow completion time (FCT) when the paths of flows are pregiven. Moreover, DENA includes a neural network classifier and a DRL system (DS). Accordingly, classifier separates the mice and elephant flows, then directly transfers the mice flows to avoid latency, while DS detects the interval of the most significant energy consumption density as a critical interval to schedule elephant flows. Besides, we apply deep deterministic policy gradient to DS with the advantage of an optimal base solution to enhance the accuracy of exploration. To the best of our knowledge, we are the first to use DRL for the rate adjustment. Our results show that besides saving energy, DENA reduces FCT for elephant flows workload 4.12%, data mining 1.04% and Hadoop 3.8%.

Intelligent Routing based on Deep Reinforcement Learning in SoftwareDefined DataCenter Networks In softwaredefined datacenter networks, Elephant flowMice flowCoflow coexist and multiple resources (bandwidth, cache and computing) coexist. However, the conventional routing methods cannot overcome the large gap between different performance requirements of flow and efficient resource allocation. Therefore, this paper proposes DRLR (Deep Reinforcement Learningbased Routing) to bridge this gap. First, we recombine multiple resources (nodexe2x80x99s cache, linkxe2x80x99s bandwidth) by quantifying the contribution score of them reducing the delay. This actually converts the performance requirements of flow into resource requirements of this flow, hence, the routing problem can be converted into a jobscheduling problem in resource management. Second, a DRL agent deployed on an SDN controller continually interacts with the network for adaptively performing reasonable routing according to the network state, and optimally allocating network resources for traffic. We employ Deep QNetwork (DQN) and Deep Deterministic Policy Gradient (DDPG) to build the DRLR. Finally, we demonstrate the effectiveness of DRLR through extensive simulations. Benefitted from continually learning with a global view, DRLR can improve throughput highest up to 40% and flow completion time highest up to 47% over OSPF. DRLR can improve the load balance of link highest up to 18.8% over OSPF. Additionally, DDPG has better performance than DQN.