Internet-of-Vehicles (IoV) connects vehicles, sensors, pedestrians, mobile devices, and the Internet with advanced communication and networking technologies, which can enhance road safety, improve road traffic management, and support immerse user experience. However, the increasing number of vehicles and other IoV devices, high vehicle mobility, and diverse service requirements render the operation and management of IoV intractable. Software-defined networking (SDN) and network function virtualization (NFV) technologies offer potential solutions to achieve flexible and automated network management, global network optimization, and efficient network resource orchestration with cost-effectiveness and are envisioned as a key enabler to future IoV. In this article, we provide an overview of SDN/NFV-enabled IoV, in which SDN/NFV technologies are leveraged to enhance the performance of IoV and enable diverse IoV scenarios and applications. In particular, the IoV and SDN/NFV technologies are first introduced. Then, the state-of-the-art research works are surveyed comprehensively, which is categorized into topics according to the role that the SDN/NFV technologies play in IoV, i.e., enhancing the performance of data communication, computing, and caching, respectively. Some open research issues are discussed for future directions.

Mobile edge computing (MEC) and cloud computing (CC) have been considered as the key technologies to improve the task processing efficiency for Internet of Vehicles (IoV). In this article, we consider a random traffic flow and dynamic network environment scenario where MEC and CC are collaborated for processing delay-sensitive and computation-intensive tasks in IoV. We study the joint optimization of computation offloading and resource allocation (CORA) with the objective of minimizing the system cost of processing tasks subject to the processing delay and transmission rate constraints. To attack the challenges brought by the dynamic environment, we use the Markov decision process model for formulating the dynamic optimization problem, and apply a deep reinforcement learning (DRL) technique to deal with high-dimensional and continuous states and action spaces. Then, we design a CORA algorithm, which is able to effectively learn the optimal scheme by adapting to the network dynamics. Extensive simulation experiments are conducted, in which we compare the CORA algorithm with both non-DRL algorithms and DRL algorithms. The experimental results show that the CORA algorithm outperforms others with excellent training convergence and performance in processing delay and processing cost.

In this paper, a two-timescale radio access network (RAN) slicing and computing task offloading problem is investigated for a cloud-enabled autonomous vehicular network (C-AVN). We aim at jointly maximizing the communication and computing resource utilization with diverse quality-of-service (QoS) guarantee for autonomous driving tasks. Specifically, to capture the small-timescale network dynamics, a computing task scheduling problem is formulated as a stochastic optimization program, for maximizing the long-term network-wide computation load balancing with minimum task offloading variations. Due to the large problem size and unavailable network state transition probabilities, we employ cooperative multi-agent deep Q -learning (MA-DQL) with fingerprint to solve the problem by learning the set of stationary task offloading policies with stabilized convergence. Given the task offloading decisions, we further study a RAN slicing problem in a large timescale, which is formulated as a convex optimization program. We focus on optimizing the radio resource slicing ratios among base stations, to maximize the aggregate network utility with statistical QoS provisioning for autonomous driving tasks. Based on the impact of radio resource slicing on computation load balancing, we propose a two-timescale hierarchical optimization framework to maximize both communication and computing resource utilization. Extensive simulation results are provided to demonstrate the effectiveness of the proposed framework in comparison with state-of-the-art schemes.

In this paper, a dynamic radio resource slicing framework is presented for a two-tier heterogeneous wireless network. Through software-defined networking-enabled wireless network function virtualization, radio spectrum resources of heterogeneous wireless networks are partitioned into different bandwidth slices for different base stations (BSs). This framework facilitates spectrum sharing among heterogeneous BSs and achieves differentiated quality-of-service (QoS) provisioning for data service and machine-to-machine service in the presence of network load dynamics. To determine the set of optimal bandwidth slicing ratios and optimal BS-device association patterns, a network utility maximization problem is formulated with the consideration of different traffic statistics and QoS requirements, location distribution for end devices, varying device locations, load conditions in each cell, and intercell interference. For tractability, the optimization problem is transformed to a biconcave maximization problem. An alternative concave search (ACS) algorithm is then designed to solve for a set of partial optimal solutions. Simulation results verify the convergence property and display low complexity of the ACS algorithm. It is demonstrated that the proposed radio resource slicing framework outperforms the two other resource slicing schemes in terms of low communication overhead, high spectrum utilization, and high aggregate network utility.

With software-defined networking (SDN) and network function virtualization (NFV) technologies, network slicing is a promising solution for resource orchestration to achieve quality-of-service (QoS) isolation in customized services in fifth-generation (5G) networks. In this article, we propose a comprehensive network slicing framework for end-to-end (E2E) QoS provisioning, with differentiated resource types in both wireless and wired network domains considered.

In this paper, we present a three-layer (i.e., device, field, and factory layers) deterministic federated learning (FL) framework, named DetFed, which accelerates collaborative learning process for ultra-reliable and low-latency industrial Internet of Things (IoT) via integrating 6G-oriented Time-sensitive Networks (TSN). Utilizing dispersive local data, industrial IoT devices distributively train a deep neural network (DNN) model, and the updated model parameters are aggregated at their associated field servers every round or at a centralized factory server every a few rounds. Aiming at optimizing the learning accuracy of FL without affecting the co-transmission of burst traffic (e.g., safety-critical traffic), an integrated TSN is considered to establish connections among the three layers, where a cyclic queuing and forwarding mechanism is deployed in each switch to support deterministic model parameter transmission with microsecond-level delay and near-zero packet loss requirements. To improve the FL performance, we formulate a multi-objective stochastic optimization problem to simultaneously maximize the scheduling success ratio and learning accuracy while satisfying the deterministic requirements of delay, jitter, and packet loss. Since the objective function is implicit and the available time slots of the considered TSN in each FL round are temporally correlated, the problem is difficult to solve in real time. Therefore, we transform the problem into a Markov decision process formulation and propose a dynamic resource scheduling algorithm, based on deep reinforcement learning, to make optimal resource scheduling decisions while adapting to device heterogeneity and network dynamics. Experimental results based on real-world dataset demonstrate that the proposed DetFed significantly accelerates FL convergence and improves learning accuracy as compared to state-of-the-art benchmarks.