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.

Microservice architecture is a design philosophy that achieves decoupling by decomposing a monolithic application into multiple lightweight microservices. Meanwhile, edge computing can significantly reduce service latency and network congestion by extending computation and storage resources to the network edge. Therefore, in the microservice-oriented edge computing platform, a fundamental problem is how to efficiently deploy microservices with complex dependencies on the resource-constrained edge servers to satisfy the Quality of Service (QoS) constraints of users. Most of the existing studies ignore multiple call graphs with differentiated dependencies for an application, which often result in the violation of QoS. To address this issue, in this article, we first model the request response time of multiple instances and multiple call graphs scenario with service conflicts. Then, different from the existing heuristic or approximation algorithms which rely heavily on expert knowledge, we propose a graph-reinforcement-learning-based deployment (GRLD) framework. GRLD uses a graph convolutional network (GCN) to extract the graph data required for multiple call graphs with messages passing and aggregation, and the generated feature is fed into the underlying network of deep-reinforcement-learning (DRL). Experimental results show that GRLD outperforms counterparts in reducing service deployment overhead while satisfying QoS constraints of multiple call graphs.

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 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 over-allocation 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 BO 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.

Benefiting from advanced satellite payload technologies, edge computing servers can be deployed on satellites to achieve orbital computing and reduce the mission processing delay. However, geostationary Earth orbit (GEO) satellites are hindered by long-distance communication, whereas low Earth orbit (LEO) satellites are restricted by time windows. Relying solely on GEO or LEO satellites cannot meet the strict quality of service (QoS) requirements of on-board missions while conserving energy consumption. In this paper, we propose a computation offloading strategy for GEO/LEO hybrid satellite networks that minimizes total energy consumption while guaranteeing the QoS requirements of multiple missions. We first innovatively transform the on-board partial computation offloading problem, which is a mixed-integer nonlinear programming (MINLP) problem, into a minimum cost maximum flow (MCMF) problem. Then, the successive shortest path-based computation offloading (SSPCO) method is introduced to obtain the offloading decision in polynomial time. To evaluate the effectiveness and performance of SSPCO, we conduct a series of numerical experiments and compare SSPCO with other offloading methods. The experimental results demonstrate that our proposed SSPCO outperforms the reference methods in terms of total energy consumption, QoS violation degree, and algorithm running time.