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Time-Wavelength Division Multiplexing Passive Optical Network (TWDM-PON) is considered as a promising solution of next generation PON (NG-PON). The integration of Edge Computing (EC) and TWDM-PON can satisfy the QoS requirements of delay-sensitive applications by providing storage, processing and caching capabilities at the network edge. However, with limited resource capacity, edge nodes in TWDM-PON are vulnerable to network attacks, e.g., Distributed Denial of Service (DDoS) attacks. Resource exhaustion in the attacked nodes easily leads to QoS degradation and even service blocking. This paper investigates how to effectively schedule traffic to mitigate the DDoS attack in EC-enabled TWDM-PON. Based on the collaboration of edge nodes, an adaptive traffic scheduling algorithm is designed to minimize the impact of DDoS attacks on delay sensitive services. The performance of the proposed algorithm is evaluated in simulation, where direct and indirect DDoS attacks are simulated. Besides, the attack duration and the number of attacked nodes are considered in the evaluation. Simulation results show that the proposed algorithm can effectively mitigate DDoS attacks in terms of reducing the QoS degradation rate and blocking rate of delay-sensitive services.

Low-power wide-area (LPWA) communication has gained increasing attention in recent years with the rapid growth of fifth generation evolution (5G), the Internet of Things (IoT), and mobile computing. Narrowband Internet of Things (NB-IoT) is one kind of LPWA technology based on cellular IoT, which supports massive connections, wide area coverage, ultra-low power consumption, and ultra-low cost. Research on NB-IoT communication is increasingly attractive. Network calculus theory facilitates the performance analysis and network optimization of the NB-IoT system. We aim to analyze and optimize the NB-IoT networks. In this paper, we construct a random access traffic model including the NB-IoT user equipment (UE) arrival process and eNB service process. Then, we utilize the stochastic network calculus (SNC) to analyze the network delay in NB-IoT traffic model. Random latency bounds in different arrival processes are derived. Simulations show that SNC can evaluate the system delay under different distributions effectively. For the condition that numerous UEs access simultaneously following the Beta distribution, we first propose an improved K-means algorithm to cluster the NB-IoT terminals. Then, we raise the scheduling strategy on the basis of priority. It consists of the priority generation algorithm IPGNTQ and the NB-IoT task scheduling algorithm SANTQ. The extensive experiment results verify that our proposed optimized strategy can alleviate the network congestion effectually. Moreover, we compare our proposed optimized scheme with four existing uplink traffic scheduling schemes, showing that ours outperforms all of them.

With the ubiquitous use of networking technologies in the military domain, network-centric warfare, which aims at improving the system of systems (SoS) capability, is a no-way-back trend of military development. Recently, it has become increasingly important to construct an airborne backbone network (ABN) to maintain coverage and provide reach-back to military units because the terrestrial networks cannot be flexibly deployed and have poor survivability. Given the advantages of software-defined networking (SDN), a software-defined airborne backbone network architecture (SD-ABN) is proposed for the ABN to ensure network flexibility, openness, interoperability, and evolvability. To meet the challenges of traffic management in SD-ABN, segment routing (SR) is applied with some practical modifications for the SD-ABN. Moreover, a network traffic scheduling algorithm termed MRP-TS is designed based on SD-ABN to improve the transmission reliability and bandwidth utilization by balancing network traffic to multiple reliable transmission paths. Relevant theories have been proposed and proved to illustrate the correctness of the algorithm. The network simulation experiments have been conducted using the network simulator EXata 5.1, and the simulation results validate the effectiveness of our proposals.

The emergence of smart grid poses technical challenges to the power distribution network because of the increasing data traffic resulting from diverse data applications. Traffic scheduling algorithms manage these heterogeneous applications by applying different priorities to each traffic type based on its quality of service (QoS). However, QoS alone cannot accurately capture complex situations wherein packets with low priority occasionally need to be served first based on their context, resulting in a suboptimal solution. This paper proposes a context aware traffic scheduling (CATSchA) algorithm to schedule the traffic such that it could adapt to varying power network conditions. The power distribution network traffic is characterized based on heterogeneous traffic demands, and then mapped into weighted quality classes. The CATSchA algorithm is implemented in a packet switched network using NS-3 simulator, and the traffic demand is fulfilled based on the algorithm's context awareness. Compared with traditional traffic scheduling algorithms, the proposed algorithm lowers the delay while maintaining the throughput and link efficiency.

With the rapid evolution of high-speed mobile communications, cloud computing, and other high-bitrate datacenter-supported services, efficient and flexible traffic scheduling has become one of the fundamental tasks of inter-datacenter optical networks (IDCONs). Traffic scheduling algorithms based on long-term traffic prediction, which have intelligent and global resource allocation ability, have been proved to perform well in IDCONs. However, the low accuracy of existing long-term traffic prediction methods, which is caused by the accumulated errors produced in the recursive multi-step prediction process, directly restricts the efficiency of traffic scheduling. In this paper, we consider the problem of highly efficient traffic scheduling in IDCONs by leveraging one step long-term traffic prediction to reduce the prediction errors. We first design a multiple time interval feature-learning network (MTIFLN) to handle the challenging task of one step long-term traffic prediction. By integrating five bidirectional RNNs (B-RNNs) to one single framework, the MTIFLN has a strong ability to extract the long-term traffic features at different time intervals. Moreover, the stacked architecture of MTIFLN helps to reduce the prediction errors through multi-resampling process. A traffic prediction-based resource allocation (TP-RA) algorithm is proposed together with a global factor to evaluate the efficiency of traffic prediction and achieve effective traffic scheduling based on both traffic prediction results and network resource utilization. Simulation results indicate that with our proposal, the MTIFLN can accurately predict the traffic for more than 24 hours in one step, and the TP-RA algorithm enables IDCONs to make more efficient use of network resources.

Network function virtualization (NFV) has already been a new paradigm for network architectures. By migrating NFs from dedicated hardware to virtualization platform, NFV can effectively improve the flexibility to deploy and manage service function chains (SFCs). However, resource allocation for requested SFC in NFV-based infrastructures is not trivial as it mainly consists of three phases: virtual network functions (VNFs) chain composition, VNFs forwarding graph embedding, and VNFs scheduling. The decision of these three phases can be mutually dependent, which also makes it a tough task. Therefore, a coordinated approach is studied in this paper to jointly optimize NFV resource allocation in these three phases. We apply a general cost model to consider both network costs and service performance. The coordinate NFV-RA is formulated as a mixed-integer linear programming, and a heuristic-based algorithm (JoraNFV) is proposed to get the near optimal solution. To make the coordinated NFV-RA more tractable, JoraNFV is divided into two sub-algorithms, one-hop optimal traffic scheduling and a multi-path greedy algorithm for VNF chain composition and VNF forwarding graph embedding. Last, extensive simulations are performed to evaluate the performance of JoraNFV, and results have shown that JoraNFV can get a solution within 1.25 times of the optimal solution with reasonable execution time, which indicates that JoraNFV can be used for online NFV planning.