Taking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.
Intelligent vehicles and their applications increasingly demand high computing power and low task delays, which poses significant challenges for providing reliable and efficient vehicle services. Mobile edge computing (MEC) is a new model that reduces the completion time of tasks and improves vehicle service by performing computation offloading near the moving vehicles. Considering the high-speed mobility of the vehicles and the unstable connection of the wireless cellular network, symmetric and geographically distributed edge servers are regarded as peers in a peer-to-peer (P2P) network, and a P2P-based vehicle edge offloading model is proposed in this paper to determine the optimal offloading server for the vehicle and the offloading ratio of tasks to achieve the goal of minimizing execution time. Because the edge computing infrastructure is deployed at the edge of the network, the data in the edge nodes are easily damaged or lost. Therefore, a P2P-based edge node fault tolerance mechanism is proposed to improve the reliability and fault tolerance of the system. The feasibility and effectiveness of our proposed system have been verified through simulation experiments, which greatly reduces the task completion delay.
The rapid development of the Internet of Things and 5G networks have generated a large amount of data. By offloading computing tasks from mobile devices to edge servers with sufficient computing resources, network congestion and data transmission delays can be effectively reduced. The placement of edge server is the core of task offloading and is a multi-objective optimization problem with multiple resource constraints. Efficient placement approach can effectively meet the needs of mobile users to access services with low latency and high bandwidth. To this end, an optimization model of edge server placement has been established in this paper through minimizing both communication delay and load difference as the optimization goal. Then, an Edge Server placement based on meta-Heuristic alGorithM (ESH-GM) has been proposed to achieve multi-objective optimization. Firstly, the K-means algorithm is combined with the ant colony algorithm, and the pheromone feedback mechanism is introduced into the placement of edge servers by emulating the mechanism of ant colony sharing pheromone in the foraging process, and the ant colony algorithm is improved by setting the taboo table to improve the convergence speed of the algorithm. Then, the improved heuristic algorithm is used to solve the optimal placement of edge servers. Experimental results using Shanghai Telecom’s real datasets show that the proposed ESH-GM achieves an optimal balance between low latency and load balancing, while guaranteeing quality of service, which outperforms several existing representative approaches.
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