Load balancing in data center networks is crucial to effectively utilize network resources and enhance Quality of Service (QoS). Especially, the flowlet-level load balancing has been proven efficient in reducing latency and increasing throughput simultaneously. However, most existing work relying on empirical static timeout encounters performance degradation in dynamic network scenarios, due to a mismatch between the static timeout and changing traffic conditions. To address this problem, we propose a Deep Reinforcement Learning-Based Traffic-Aware Load Balancing scheme (DRL-TAL), which uses deep reinforcement learning (DRL) to update the flowlet timeout adaptively. The agent using a deep deterministic policy gradient (DDPG) algorithm continuously senses network throughput and generates the timeout threshold dynamically for the next time slot. The flowlet granularity is deployed for elephant flows to achieve a balance between throughput and disorder, where the timeout value relies on the threshold generated by the agent. Furthermore, the mice flow gets forwarded under packet granularity by selecting the port with the smallest queue length to ensure a shorter flow completion time. The results demonstrate that DRL-TAL performs impressively well in the symmetric topology, with no packet loss and minimal disorder under high load compared to the state-of-the-art schemes. Moreover, it significantly reduces flow completion time by up to 45% compared to Conga in the asymmetric topology.

In elastic optical inter-datacenter networks, multiple software-defined networking (SDN) controllers have been used to improve scalability and reliability. The static controller deployment faces the problem of controller load imbalance under dynamic changes in network traffic. In response to this problem, existing research works proposed to use dynamic switch migration to achieve the controller load balancing. However, the efficiency of the existing switch migration is low, because the load balancing performance of controllers does not improve significantly after the switch migration and the migration activities can also cause additional migration costs. Thus the efficiency of switch migration has not yet been well resolved. In this work, we propose a highly-efficient switch migration (HESM) for controller load balancing. The proposed HESM method defines multiple load metrics to measure the load of controllers, and selects the optimal target controller with the largest remaining resource, which improves the load balancing performance. HESM selects switches based on minimizing migration costs, reducing additional migration costs. In addition, HESM can handle the load of multiple controllers in parallel during a switch migration activity, which improves the efficiency of migration. The simulation results show that HESM significantly improves load balancing performance of controllers and reduces the migration cost compared to existing solutions.

Software defined networking (SDN) can bring considerable flexibility to the management of inter-DC elastic optical networks. However, in the large-scale network, unreasonable placement of multiple SDN controllers may cause the unbalanced distribution of controller loads. To address this issue, we propose a two-sided matching method (TSMM) and design its corresponding algorithm to implement the optimal multi-controller placement. We solve the controller placement problem as the two-sided matching problem and consider optimizing multiple metrics which affect the controller placement. Different from previous research ideas, TSMM implements placement choice from the bilateral perspective of switch and controller. Additionally, TSMM algorithm can achieve the optimal controller placement by maximizing mutual satisfaction between controller and switch. The simulation results show that TSMM can achieve the optimal multi-controller placement and the balanced distribution of controller loads when compared with the existing schemes.