A Dynamic Deep-Learning-Based Virtual Edge Node Placement Scheme for Edge Cloud Systems in Mobile Environment

Edge node placement is a key topic to edge cloud systems for that it affects their service performances significantly. Previous solutions based on the existing information are not suitable for the mobile environment due to the mobility and random Internet access of end users. In this article, we propose a dynamic virtual edge node placement scheme, in which the edge node placement strategy is generated based on the prediction information. Our placement scheme applies the pay-as-you-go and Spot Instance model of cloud computing, which may allocate the service resources with low cost conveniently and flexibly. What’s more, Long Short-Term Memory (LSTM) is implemented to predict the information of end users’ requests and the resources’ prices, endowing the generated placement strategy with the adaptability to the change of end users. At last, a set of hierarchical-clustering-based placement algorithms are proposed, which not only locate virtual edge nodes and allocate their corresponding service resources actively, but also guarantee the service quality of end users with low time complexity. The simulation with trace data shows that compared with K-means-clustering-based placement schemes, our virtual edge node placement scheme can provide users with high-quality service in terms of network delay with relatively low placement cost time-efficiently.