Resource-intensive applications on smart vehicles is posing difficulties to the use of traditional cloud computing for computation offloading in vehicular networks. In particular, the long transmission distance between the vehicles and the cloud center can cause high latency and poor reliability which may degrade application performance and quality of service. As an integration of mobile edge computing and vehicular networks, vehicular edge computing is a promising paradigm that aims to improve vehicular services by performing computation offloading in close proximity to vehicles. In this paper, the task offloading algorithm that efficiently optimizes task delay and computing resource consumption in multi-user, multi-server vehicular edge computing scenarios is studied. The offloading algorithm not only determines where the tasks are performed, but also indicates the execution order of the tasks on the server. In order to reduce the time complexity, this paper proposes a hybrid intelligent optimization algorithm based on partheno genetic algorithm and heuristic rules. Extensive simulations are conducted, and the results show that compared with the baseline algorithms, the proposed algorithm effectively improves the offloading utility of the VEC system and is suitable for task offloading in various situations.

Low Earth Orbit (LEO) satellite networks have recently been regarded as a promising solution to provide ubiquitous user access and flexible content delivery. To meet the increasing demands of satellite services, multipath routing is leveraged to transmit each flow via multiple transmission paths concurrently. However, dynamically changed satellite topologies have brought challenges to multipath route planning and traffic splitting among different paths. Thus, in the paper, we propose a GNN-enabled Multipath Routing (GMR) scheme to maximize the network efficiency. Particularly, we firstly formulate the mutipath routing issue as a stochastic optimization problem under the bandwidth limitation and flow conservation constraint, and further present a Link Disjoint Multipath Routing (LDMR) scheme for the central route calculation, where available transmission paths and uneven traffic density are jointly considered to maximize the link utilization. Then, to balance the load distribution of different paths, a GNN-based MultiPath Traffic Engineering (GNN-MPTE) algorithm is designed for dynamic flow splitting based on estimated path quality. Finally, we implement the proposed GMR scheme and corresponding algorithms in Network Simulator NS-3 and make comparisons with other benchmarks. Simulation results demonstrate that the proposed GNN-MPTE can be extended to arbitrary inclined/polar orbit constellations without repetitive training, and significantly prompt the transmission quality including average delay, throughput, and flow completion ratio.

Recently, the Internet of Things (IoT) is attracting significant attention from both academia and industry. To connect the huge amount of IoT devices effectively, software-defined networking (SDN) is considered as a promising way because of its centralized network management and programmable routing logic. However, due to the limited resources in both the data plane and the control plane, SDN is vulnerable to the new-flow attack, which can disable the SDN-based IoT by exhausting the switches or the controller. Therefore, in this paper, we propose a smart security mechanism (SSM) to defend against the new-flow attack. The SSM uses the standard southbound and northbound interfaces of SDN, and it includes a low-cost method that monitors the new-flow attack by reusing the asynchronous messages on the control link. The monitor method can differentiate the new-flow attack from the normal flow burst by checking the hit rate of the flow entries. Based on the monitoring result, the SSM uses a dynamic access control method to mitigate the new-flow attack by perceiving the behavior of the security middleware in the IoT. The dynamic access control method can intercept the attack flows at their access switch. Extensive simulations and testbed-based experiments are conducted and the corresponding results verify the feasibility of our claims.

Multi-Layer Satellite Networks (MLSNs) based on Medium and Low Earth Orbit (MEO and LEO) satellites have gained wide attention for a sake of their complementary advantages, offering improved flexibility and transmission efficiency. However, there are still several key issues to be addressed in terms of efficient routing, as related network topologies are dynamically changed, and Quality of Service (QoS) guarantee is impeded by the changing link quality and uneven traffic distribution. Thus, in this paper, we mainly focus on the topology management and route planning for MLSNs, and propose a multi-objective-based Inter-layer Link Allocation (ILA) scheme for MEO and LEO satellites, along with a QoS-based Routing (QoSR) algorithm for on-demand traffic delivery. The aims are to significantly enhance the topology stability for transmission efficiency and improve user experience for different types of applications. Then, we implement the proposed scheme in Network Simulator NS-3 and evaluate its performance with other benchmarks. Extensive simulations were performed and related results indicate that the proposed scheme can sharply reduce the frequency of Inter-Layer Link (ILL) handovers and transmission power consumption, and significantly prompt the link utilization and transmission quality including end-to-end delay, packet loss rate, and flow finish rate.

Software-defined networking (SDN) is a promising network paradigm for future Internet. The centralized controller and simplified switches replace the traditional complex forwarding devices, and make network management convenient. However, the switches in SDN currently have limited ternary content addressable memory to store specific routing rules from the controller. This bottleneck provokes cyber attacks to overload the switches. Despite existing some countermeasures for such attacks, they are proposed based on simplified attack patterns. In this paper, we review the table-overflow attack using a sophisticated attack pattern. In the attack pattern, attack flows are targeted at their middle hops instead of endpoints. We first define potential targets in the network topology, then we propose three specific traffic features and a monitoring mechanism to detect and locate the attackers. Further, we propose a mitigation mechanism to limit the attack rate using the token bucket model. With the control of token add rate and bucket capacity, it avoids the table overflow on the victim switch. Extensive simulations in different types of topologies and experiments in our testbed are provided to show the performance of our proposal.

Network function virtualization (NFV) is a ground-breaking technology that decouples network functions (NFs) from customized hardware to support more flexible network services and network resource allocation. However, these improvements also lead to an increase in the possibility of service function chain (SFC) failure due to hardware failures, software bugs, or resource contention. This could lead to minor problems or even serious consequences. Unfortunately, the existing failure detection methods have multiple issues, such as small detection range, single detection function, heavy overhead, and low accuracy. Consequently, we propose FullSight, a feasible framework based on deep learning (DL) models that can efficiently integrate both the control plane and programmable data plane for fault detection and classification. This framework obtains the status and indicators of components and network that cause service quality performance degradation through two planes. These indicators are ultimately sent to the knowledge plane for preprocessing, dimensionality reduction, and fault analysis. In addition, we propose two algorithms based on text convolutional neural network (textCNN) and bidirectional encoder representations from transformers (BERT) to classify SFC faults. We implement and evaluate the proposed FullSight prototype extensively on a prototype with thirteen programmable switches and twenty end-hosts. Our experimental results show that FullSight can rapidly and accurately detect and identify eight categories of fine-grained SFC failures, compared with other state-of-the-art methods. Besides, compared with SFC Path Tracer and Pingmesh, our framework can reduce the average bandwidth overhead of the data plane by 57% and 84%, respectively, and achieve detection accuracy of more than 98%.