Novel Predictive Approach to Trajectory-aware Online Edge Service Allocation in Edge Environment

Abstract

The rapid development of mobile communication technology promotes the emergence of mobile edge computing(MEC).As the key technology of the fifth generation(5G) wireless network,MEC can use the wireless access network to provide the services and cloud computing functions required by telecom users nearby,so as to create a service environment with high performance,low delay and high bandwidth and accelerate various contents,services and applications in the network.However,it remains a great challenge to provide an effective and performance guaranteed strategies for services offloading and migration in the MEC environment.To solve this problem,most existing solutions tend to consider task offloading as an offline decision making process by employing transient positions of users as model inputs.In this paper instead,we consider a predictive-trajectory-aware online MEC task offloading strategy called PreMig.The strategy first predicts the future trajectory of edge users to whom the edge service belongs by a polynomial sliding window model,then calculates the dwell time of users within the signal coverage of the edge server,and finally performs the edge service assignment with a greedy strategy.To verify the effectiveness of the designed approach,we conduct simulation experiments based on real-world MEC deployment dataset and campus mobile trajectory dataset,and experimental results clearly demonstrate that the proposed strategy outperforms the traditional strategy in two key performance metrics,namely,the average service rate and the number of user service migrations.

Keywords

• edge computing|mobility|moving trajectory prediction|online service distribution|service migration