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To meet the execution requirements of delay-sensitive services in vehicular edge computing (VEC) networks, vehicular services need to be offloaded to edge computing nodes. For complex, large-scale services, the services need to be migrated if the services are not completed before the vehicles leave the coverage of edge computing nodes. Trust and resource matching between areas thus become major problems. This paper studies the decision model of vehicular service offloading and migration. First, software-defined network (SDN) technology is introduced into the traditional network architecture, and a two-layer distributed SDN-controlled VEC network architecture is designed, which is divided into a domain control layer and an area control layer. In this framework, we use the consortium blockchain as a carrier to share network topology information between SDN controllers to prevent information leakage. We then established a service offloading and migration optimization problem model to minimize service execution delay, reduce energy consumption and maximize the throughput of the blockchain system. We describe the problem model as a Markov Decision Process (MDP), introduce a deep reinforcement learning (DRL) algorithm named asynchronous advantage actor-critic (A3C) and design a dynamic service offloading and migration algorithm (DSOMA) based on A3C to solve the problem. Simulation results show that DSOMA can increase the throughput of the blockchain system, and DSOMA is superior to the deep Q-learning (DQN) algorithm and greedy offloading algorithm in reducing service execution delay and system energy consumption. [ABSTRACT FROM AUTHOR]

Low latency/delay is one of the most critical requirements for the application of vehicular networks. However, frequent real-time information update caused by vehicles high mobility is liable to aggravate the delay. Meanwhile, the task migration between different vehicular edge computing (VEC) servers results in an amount of delay if the computing cannot be completed before the vehicle moves out of the coverage of the current VEC server. In this paper, the problem is concluded as when and to whom to offload the task for VEC, which is formulated as a finite horizon Markov decision process (MDP) to minimize the delay with respect to the communication, computing, handover and migration. Through characterizing the time-space correlation of vehicles mobility, the curse of dimensionality problem in MDP is resolved. Meanwhile, a general expression of the transition probabilities is derived. On this basis, the specific results of highway, 2-D street and real-data scenarios are provided as well. For practical implementation considerations, the transition probabilities are commonly uncertain primarily due to random driver behavior, inaccurate sample data and complex path environment. Under this uncertain environment,a robust time-aware MDP-based task offloading algorithm (RTMDP) is proposed, which has been proved to perform well even under the high uncertain transition probabilities by simulation results. [ABSTRACT FROM AUTHOR]

Currently, the Internet of Things (IoT) solutions are playing an important role in numerous areas, especially in smart homes and buildings, health-care, vehicles, and energy. It will continue to expand in various fields in the future. However, some issues limit the further development of IoT technologies. First, the battery-powered feature increases the maintenance cost of replacing batteries for IoT devices. Second, existing Cloud-IoT frameworks are not able to cope with emerging delay-constrained applications in the IoT system due to its centralized mode of operation and the considerable communication delay. Existing studies neither satisfy the demand for the quick response in time-constraint IoT applications nor fundamentally solving the problem of energy sustainability. Therefore, this paper studies the problem of energy sustainability and timeliness in IoT system. Based on Energy Harvesting Technologies (EHT), the Green and Sustainable Mobile Edge Computing (GS-MEC) framework is proposed to make IoT devices self-powered by utilizing the green energy in the IoT environment. In this framework, we formulate the problem of minimizing response time and packet losses of tasks under the limitation of energy queue stability to improve the timeliness and reliability of task processing. Additionally, the dynamic parallel computing offloading and energy management (DPCOEM) algorithm is designed to solve the problem based on the Lyapunov optimization technology. Finally, theoretical analysis demonstrates the effectiveness of the proposed algorithm, and the numerical result of simulation shows that the average performance of the proposed algorithm is an order of magnitude better than state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]