Graph Neural Network Aided Deep Reinforcement Learning for Microservice Deployment in Cooperative Edge Computing

Abstract

Deploying microservices on the cooperative edge computing system greatly shortens the interaction delay between users and service and alleviates the traffic burden on the backbones, which has emerged as a new paradigm for service provision. However, it is challenging to embed microservices, having diverse resource demands and heterogeneous invocation relationships, into a distributed edge computing system with irregular network topology. In order to characterize the invocation relationship, we conceive a graph attention network based model to capture the structural features of microservices. Similarly, we propose a multi-channel directed graph convolutional network model to capture the spatial dynamic of edge resources distribution, which jointly considers the heterogeneity of the edge nodes and the links between them. Then, we develop a sequence-to-sequence based multi-step decision model, which maps the feature sequence of the current state to a sequence of deployment actions. Using this model, we further propose a microservice deployment algorithm based on graph neural network aided deep reinforcement learning, where a parallel asynchronous training process is used to accelerate convergence. The performance evaluation shows that the proposed algorithm can improve the deployment success ratio and resource utilization, while ensuring the load balance of edge nodes.