Consistent routing updates through Software-Defined Networking (SDN) can be difficult due to the asynchronous and distributed nature of the data plane. Recent studies have achieved consistent unicast routing updates. However, achieving consistent updates with drop-freeness and duplicate-freeness remains a challenge for multicast with fewer known results. This paper proposes a Novel Ordered Update Scheme called Nous, a novel approach that offers a comprehensive solution for consistently updating multicast routing based on SDN. To avoid duplicate entries, Nous configures the inport match field in the forwarding rules. Nous implements a dependency graph to schedule update operations dynamically. It also solves the Replace Operation Tree Migration Problem (ROTMP) using a greedy solution. To compare the greedy solution with the optimal solution, we employ the state-of-the-art mathematical programming solver Gurobi Optimizer 7.5 (for solving the optimization problem), Mininet 2.0, and Floodlight 1.2 (for simulation and comparison) to obtain a near-optimal solution. Simulation results show that using the greedy solution, Nous can usually achieve near-optimal solutions to the ROTMP with an average of fewer than 1.2 rounds and within 10 ms in different scenarios. This makes Nous the first ordered update scheme to guarantee two consistent states simultaneously.

A controller needs to solve the multi-commodity flow problem and globally update the network under tight time constraints to maintain optimal network configurations. This centralized optimization in data centers involves many variables and constraints, which has a slow convergence speed and little scalability. In this paper, we propose MiFi, which aims to Minimize Flow cost or intuitively average transmission delay (delay or latency of flows), under reconfiguration budget constraints in data centers. Thus, we formulate this optimization problem as a constrained Markov Decision Process and propose a set of algorithms to solve it in a scalable manner. We first develop a propagation algorithm to identify the flows mostly affected in terms of latency and configuration in the next update. Then, we set a limitation range (the subset of switches requiring network updates) for updating them to improve adaptability and scalability by updating a less number of flows each time to achieve fast operations. Further, based on the Drift-Plus-Penalty method in Lyapunov theory, we propose a heuristic policy without prior information of flow demand and a renewal policy with a performance guarantee to minimize the additive optimality gap. To the best of our knowledge, MiFi is the first paper that studies the range and frequency of flow reconfigurations, which has both theoretical and practical significance in the area. Emulations and numerical simulations, which are much better than the estimated theoretical bound, show that MiFi outperforms the state of the art algorithms in terms of latency by over 45% while making improvements in adaptability and scalability.

In this paper, we propose Deep-RL-a marking decision with Deep Reinforcement Learning (DRL) via per-port for solving the erroneously marking problems in multi-queue multi service scenarios of Data Center Networks (DCNs). We formulate the statement as a DRL problem and use Deep Neural Network (DNN) to achieve the best possible policy for the agent. In this way we can model the complex DCNs in order to obtain the optimal threshold in the output port when the marked packets from queue buffers are not a concern. Unlike prior research that focused on mathematical models or used machine learning in DCN, Deep-RL is a novel DRL based method, which optimizes the real value of threshold with continues action space. Thus, this fact makes our work incomparable with previous research. To the best of our knowledge, we are the first to discuss the problem with DRL and DNN. Simulation results demonstrate that Deep-RL utilizes the buffer capacity at exactly 30% and achieves near optimal flow completion time.

Data center networks have become heavily reliant on software-defined network to orchestrate data transmission. To maintain optimal network configurations, a controller needs to solve the multi-commodity flow problem and globally update the network under tight time constraints. In this paper, we aim to minimize flow cost or intuitively average transmission delay, under reconfiguration budget constraints in data centers. Thus, we formulate this optimization problem as a constrained Markov Decision Process and propose a set of algorithms to solve it in a scalable manner. We first develop a propagation algorithm to identify the flows which are mostly affected in terms of latency and will be configured in the next network update. Then, we set a limitation range for updating them to improve adaptability and scalability by updating a less number of flows each time to achieve fast operations as well. Further, based on the Drift-Plus-Penalty method in Lyapunov theory, we propose a heuristic policy without prior information of flow demand with a performance guarantee to minimize the additive optimality gap. To the best of our knowledge, this is the first paper that studies the range and frequency of flow reconfigurations, which has both theoretical and practical significance in the related area. Extensive emulations and numerical simulations, which are much better than the estimated theoretical bound, show that our proposed policy outperform the state of the art algorithms in terms of latency by over 45% while making improvements in adaptability and scalability.