By deploying computing, storage, and bandwidth resources at the user side, vehicular edge computing (VEC) provides low-delay services for vehicle users. However, due to the limited resources of edge servers, how to efficiently meet the Quality-of-Service (QoS) requirements of multiple tasks and save the total energy consumption in a dynamic environment is an important issue in VEC. In this article, we first propose an energy consumption and QoS-aware co-offloading model. Unlike most previous studies, our goal is to minimize the total energy consumption while guaranteeing the QoS constraints of tasks, thus avoiding the overallocation of resources and high energy consumption caused by the one-sided pursuit of delay minimization. Then, without the requirements for domain experts, we propose Bayesian optimization-based computation offloading (BOCO) method to find the optimal offloading decision. To the best of our knowledge, this work is the first to apply Bayesian optimization to computation offloading in VEC. Furthermore, we conduct a series of experiments and comparisons with other offloading methods to analyze the effectiveness and performance of the proposed algorithm. Experimental results verify that our proposed BOCO outperforms counterparts.

The microservice deployment strategy is promising in reducing the overall service response time in the microservice-oriented edge computing platform. However, existing works ignore the effect of different interaction frequencies among microservices and the decrease in service execution performance caused by the increased node loads. In this article, we first model the invocation relationships among microservices as an undirected and weighted interaction graph to characterize the communication overhead. Then, we propose a multi-objective microservice deployment problem (MMDP) in edge computing. MMDP aims to minimize the communication overhead while achieving load balance between edge nodes. Without the requirement for domain experts, we propose Reward Sharing Deep Q Learning (RSDQL), a learning-based algorithm, to solve MMDP and obtain the optimal deployment strategy. In addition, to improve the scalability of the services, we propose an Elastic Scaling algorithm (ES) based on heuristics to deal with the dynamic pressure of requests. Finally, we conduct a series of experiments in Kubernetes to evaluate the performance of our approach. Experimental results indicate that, compared with interaction-aware strategy and Kubernetes default strategy, RSDQL has shorter response times, more balanced resource loads, and makes services scale elastically according to the request pressure.