A Trajectory Prediction-Based and Dependency-Aware Container Migration for Mobile Edge Computing
Weiwen Zhang, Jinzhou Luo, Lei Chen, Jianqi Liu
Abstract
Edge computing and container technologies offer more possibilities for the development of Internet of Vehicles (IOV). However, many studies have neglected the dependencies among containers and the mobility of users. In this article, we propose a container migration strategy based on trajectory prediction, with the consideration of dependencies among containers. Given a set of containers with dependencies, we aim to reduce the service latency while distributing the containers as evenly as possible for load balance. Specifically, we leverage Recurrent Neural Network (RNN) to train a model for trajectory prediction. Based on the prediction, we can identify the location of the vehicles, after which we develop a mathematical model for service delay. We formulate the problem as a 0-1 program and solve the problem by Hunger Games Search (HGS). The proposed algorithm is validated with a real vehicle dataset and an Alibaba cluster dataset. Experiment results demonstrate that we can predict the trajectory of vehicle accurately and the container migration strategy can effectively reduce service latency and improve server load balancing, compared to alternative algorithms. In addition, we characterize the impact of error correction mechanism and also the effect of bandwidth on the optimization.