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.

Network caching in Vehicular Named Data Networks (VNDN) has the potential to support latency-sensitive services, though given the massive amount of content generated by vehicles in a VNDN, it is challenging to cache sufficiently diverse content across the network for an acceptable hit ratio. Redundant interest transmissions can negatively impact network overhead, wasting network resources when retrieving duplicate content. In the literature, a caching decision is typically based on ongoing popularity, computed from a vehicle’s experience with requests received for individual content. This leads to each vehicle caching the most popular content. Instead, we group content into different categories. All content in a popular category of vehicular service (e.g., tourist information category) is cached equally, not just the most popular content within a category (e.g., information relating to a particular museum). This increases content diversity in the cache. Further, we posit that a service requester from a particular location at a specific time would be interested in the same category of service/application as other users from the same location/time. Leveraging this observation, we consider both forthcoming spatial/temporal and ongoing popularity of vehicular services when ranking their popularity. We also address network overhead with a mobility model that captures the communication duration between vehicles. This parameter, which has been overlooked in existing literature, enables the selection of an appropriate forwarder for content requests. We use ndnSIM VNDN with a mobility trace of Luxembourg city for simulation. Compared to an approach where individual content-wise popularity is computed, our scheme shows more than 100% improvement in cache hit ratio where the number of contents in the network varied between 104 and 105. Simultaneously, we also enhanced the diversity of content caching, while considering the trade-off between cache hit ratio and network overhead.

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 present Joint Unicast-Multicat Panoramic Streaming (JUMPS) over the cellular network. JUMPS optimizes the resource allocation for a group of eMBMS (evolved Multicast Broadcast Multimedia Systems) users to enhance their experience while leveraging the inherent diversity in both users' network conditions and field of view (FoV). The key intuition is combining unicast and multicast, for tiled panoramic content, would enable facilitate using the right amount of resources for every tile considering the tile popularity and receiving user link quality. We compare JUMPS performance to state-of-the-art solutions and show that it significantly improves users' received FoV bitrate and reduces their battery by reducing the number of resource blocks that users have to listen to. These results are consistent across various scenarios that vary across user group link conditions, FoV diversity, and available network resources.

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.

We propose DENA as a deep reinforcement learning (DRL) based energy-efficient 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 pre-given. 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%.

In this paper, we propose a prediction algorithm for dynamic channel allocation amongst coexisting Wireless body area networks (WBANs). Variations in channel assignment due to mobility scenarios within each WBAN as well as the movement of WBANs towards each other is investigated. The proposed scheme is further optimized to allocate the optimum transmission time with synchronous and parallel transmissions such that interference is fully avoided. This reduces the number of interfering nodes and leads to better usage of the scarce limitation of resources in these networks, larger network lifetime, higher energy savings and higher throughput. In fact, the aim of this protocol is to mitigate interference along with maintaining minimum power consumption in order to maximize network lifetime and increase the spatial reuse and throughput of each WBAN. Simulation results show that our approach achieves a much higher spatial reuse using the smart spectrum allocation scheme for interference mitigation in collocated WBANs. We conduct extensive simulations for coexistence prediction in different mobility scenarios using the NS-2 simulator. Consequently, we demonstrate the efficiency of the proposed protocol in providing interference-free channel assignments and higher energy savings.

This paper presents a novel primitive for self-organization amongst multiple coexisting Wireless Body Area Networks (WBANs). It follows a biologically inspired approach based on the theory of pulse-coupled oscillators. Our proposal allows for coexisting WBANs to use delayed information from previous transmissions to adjust to a collision-free TDMA schedule amongst each other for future communications. Most importantly, it does not require a global coordinator as all nodes achieve synchronization in a completely self-organized manner. Simulation results show that our protocol achieves a significantly fast convergence time despite little information from its coexisting networks. Moreover, the proposed approach is shown to be robust to variations in channel conditions, density of sensor nodes within each network and the number of coexisting WBANs. We conduct extensive simulations to evaluate the efficiency of the proposed protocol using the NS-2 simulator.