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The expansion of megaconstellation networks (MCNs) represents a promising solution for achieving global Internet coverage. To meet the growing demand for satellite services, multipath routing allows the simultaneous establishment of multiple transmission paths, enabling the transmission of flows in parallel. Nevertheless, the mobility of satellites and time-varying link states presents a challenge for the discovery of optimal paths and traffic scheduling in multipath routing. Given the inflexibility of traditional static deep reinforcement learning (DRL)-based routing algorithms in dealing with time-varying constellation topologies, DRL-based multipath routing (DMR) enabled by a graph neural network (GNN) is proposed as a means of enhancing the transmission performance of MCNs. DMR decouples the stochastic optimization problem of multipath routing under traffic and bandwidth constraints into two subproblems: multipath routing discovery and multipath traffic scheduling. Firstly, the minimum hop count-based multipath route discovery algorithm (MHMRD) is proposed for the computation of multiple available paths between all source and destination nodes. Secondly, the GNN-based multipath traffic scheduling scheme (GMTS) is proposed as a means of dynamically scheduling the traffic on each available path for each data stream, based on the state information of ISLs and traffic demand. Simulation results demonstrate that the proposed scheme can be scaled to constellations with different configurations without the necessity for repeated training and enhance the throughput, completion ratio, and delay by 42.64%, 17.39%, and 3.66% in comparison with the shortest path first algorithm (SPF), respectively. [ABSTRACT FROM AUTHOR]

PFC (Priority-based Flow Control), which is used to give full play to the performance advantages of RDMA, can cause problems such as head-of-line (HoL) blocking. PFC-based method cannot eliminate the congestion fundamentally. At the same time, another approach to deal with congestion in the data center networks, commonly known as "injection throttling" mechanism, may result in slower effective response due to the delay between congestion detection and feedback to the sources. In this article, we have optimized the two types of algorithms (DCI and NACC) and used them in combination (DCI-NACC). We propose a novel scheduling strategy of combining PFC-based algorithm and "injection throttling"-based algorithm using programmable data plane device for lossless data center networks. Specifically, we design a dynamic congestion isolation (DCI) method to optimize PFC. DCI ensures lossless transmission in the data center networks and eliminates the HoL blocking problem caused by PFC. We also propose a network-assisted congestion control (NACC) algorithm in which the feedback information is directly sent from the switch where congestion occurs in the network. NACC increases the efficiency of network congestion perception and reduces the delay that feedback take effect. Experimental results show that, compared with PFC and ECN algorithm, DCI-NACC algorithm has significant improvement in network performance (normalized throughput, network latency, and flow completion time). Especially, simulation experiments show that NACC can greatly reduce the number of times that PFC PAUSE is triggered. The simulation experiments confirm that DCI-NACC algorithm can effectively improve the performance of the data center networks on the basis of lossless transmission. [ABSTRACT FROM AUTHOR]

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Due to the complicated structure, power communication network is difficult to guarantee the quality of service (QoS) of power services. A two-level scheduling algorithm based on software defined network (SDN) is proposed in this paper. Firstly, the priority-based scheduling method is used to meet the latency-sensitive of power service. Then, in order to alleviate congestion, queue bandwidth is adjusted according to network state information, which can be collected by the centralized control of SDN. Finally, the Mininet and Ryu controller are made use of building simulation environment. The test results show that the algorithm proposed in this paper reduce delay and packet loss rate significantly, which achieves QoS. [ABSTRACT FROM AUTHOR]

The next generation electrical power grid, known as smart grid (SG), requires a communication infrastructure to gather generated data by smart sensors and household appliances. Depending on the quality of service (QoS) requirements, this data is classified into event-driven (ED) and fixed-scheduling (FS) traffics and is buffered in separated queues in smart meters. Due to the operational importance of ED traffic, it is time sensitive in which the packets should be transmitted within a given maximum latency. In this paper, considering QoS requirements of ED and FS traffics, we propose a two-stage wireless SG traffic scheduling model, which results in developing a SG traffic scheduling algorithm. In the first stage, delay requirements of ED traffic is satisfied by allocating the SG bandwidth to ED queues in smart meters. Then, in the second stage, the SG rest bandwidth is going to the FS traffic in smart meters considering maximizing a weighted utility measure. Numerical results demonstrate the effectiveness of the proposed model in terms of satisfying latency requirement and efficient bandwidth allocation. [ABSTRACT FROM AUTHOR]

The evolution of Internet of Things (IoT) has increased the appetite for the energy efficient wireless infrastructures. Most of the IoT devices are inherently resource constrained and heterogeneous in respect of their traffic demand. Moreover, these devices need to be made adaptable to the varying environmental conditions. However, existing traffic scheduling and duty cycling algorithms lack the capability to meet the dynamic service quality requirements of IoT applications with variable context information. Specifically, they are infeasible for IoT use-cases where multi-hop communication is required. This paper strives to realize efficient resource allocation to the Wi-Fi based IoT devices in multi-hop IoT infrastructures. Firstly, IoT applications are characterized as per their heterogeneous traffic demand and mapped to the distinct weighted quality classes. Then, context awareness is introduced for IoT devices and an optimization model constrained by their service quality requirements and context priorities is presented. Further, an energy efficient context aware traffic scheduling (EE-CATS) algorithm is proposed wherein the convergence of model is specified by a sub-gradient projection method. The EE-CATS algorithm efficiently allocates resources to multi-hop IoT devices and reduce their total awake time by employing adaptive duty cycling. The performance evaluation is done for various IoT use-cases through simulations in Network Simulator (ns-2) and testbed implementation in Contiki operating system. The results suggest substantial improvements in the energy efficiency of IoT devices while ensuring their dynamic service requirements. In addition, the proposed algorithm minimizes the end-to-end delays in overall IoT networks. [ABSTRACT FROM AUTHOR]

In this paper, medium access control (MAC) sublayer data traffic scheduling in distributed networked control systems (DNCSs) with networked induced packet losses and delays is investigated for stabilization and control of system dynamics. An effective-information-directed distributed sensor selection algorithm is proposed to select sensors to participate in delay-tolerant information filtering for state estimation. The distributed scheduling algorithm is based on the total amount of effective information for all measurements stored in the sensors. We consider packet loss in wireless networks as a special type of delay, so the impact of packet losses on effective information amount can also be studied for the formulation of distributed scheduling strategy in the presence of packet losses. The distributed data traffic scheduling considering packet losses and delays is carried out in a networked system with data packet dropout governed by a Markov process for voltage regulation and stabilization with distributed energy resources (DERs). Experimental results demonstrate that the scheduling algorithm with awareness of the system dynamic state can well control the power system dynamics. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Core-stateless scheduling algorithms have been pro- posed in the literature to overcome the scalability problem of the stateful approach. Instead of maintaining per-flow information or performing per-packet flow classification at core routers, packets are scheduled according to the information (time stamps) carried in their headers. They can hence provision quality of service (QoS) and achieve high scalability. In this paper, which came from our observation that it is more convenient to evaluate a packet's delay in a core-stateless network with reference to its time stamp than to the real time, we propose a new traffic model and derive its properties. Based on this model, a novel time-stamp encoding scheme, which is theoretically proven to be able to minimize the end-to-end worst case delay in a core-stateless network, is presented. With our proposed traffic model, performance analysis in core-stateless net- works becomes straightforward. [ABSTRACT FROM AUTHOR]

This paper proposes a modified architecture of the Long-Term Evolution (LTE) mobile network to provide services for the Internet of Things (IoT). This is achieved by allocating a narrow bandwidth and transferring the scheduling functions from the eNodeB base station to an NB-IoT controller. A method for allocating uplink and downlink resources of the LTE/NB-IoT hybrid technology is applied to ensure the Quality of Service (QoS) from end-to-end. This method considers scheduling traffic/resources on the NB-IoT controller, which allows eNodeB planning to remain unchanged. This paper also proposes a prioritization approach within the IoT traffic to provide End-to-End (E2E) QoS in the integrated LTE/NB-IoT network. Further, we develop "smart queue" management algorithms for the IoT traffic prioritization. To demonstrate the feasibility of our approach, we performed a number of experiments using simulations. We concluded that our proposed approach ensures high end-to-end QoS of the real-time traffic by reducing the average end-to-end transmission delay. [ABSTRACT FROM AUTHOR]

Deep packet inspection (DPI) is important for network security. In this paper, we consider a software-defined network where several DPI proxy nodes are available for serving flows from ingress switches. These DPI proxy nodes can be implemented in either software or hardware. We study an integrated proxy allocation and routing determining problem with the objective of minimizing the total delay of flows from ingress switches to DPI proxies. This problem is formulated as an integer linear programming problem that is NP-hard in general. To solve this problem, we design a 2-phase algorithm that can quickly select proxy and find routing paths for incoming flows. Finally, extensive simulations are conducted to evaluate the performance of our proposed algorithm. Some useful parameter setting insights are obtained. [ABSTRACT FROM AUTHOR]

In this paper, we present a multi-gate mesh network architecture that has been developed to ensure high performance and reliability under emergency conditions when a system expects to receive power outage notifications and exchanges. In order to handle the metering traffic, under time varying outage conditions we introduce a multi-gate and single-class back-pressure based scheduling algorithm, which takes into account both the hop-count, as well as the queue length of each mesh node. An important requirement of this algorithm is that all the meter nodes should always maintain a separate path to each gateway. We first quantify the stability region of the network when our novel algorithm is applied to schedule the packets. We then present a numeric analysis to prove that the overall network delay is reduced as a result of employing the proposed scheduling algorithm. Moreover, we also theoretically prove that the network is always able to remain stable as long as the arrival rate vector lies inside the stability region provided by our scheduling algorithm. Finally, we derive a distributed objective function that is adopted by the practical implementation of the packet-scheduling scheme. The simulation results indicate that under the context of the multi-gate network, our packet-scheduling scheme can indeed significantly improve the network's reliability and delay performance, which are important factors under outage conditions. [ABSTRACT FROM PUBLISHER]