A Predictive and Trajectory-Aware Edge Service Allocation Approach in a Mobile Computing Environment

The mobile edge computing (MEC) model is featured by the ability to provision elastic computing resources close to user requests at the edge of the internet. This paradigm moves traditional digital infrastructure close to mobile networks and extensively reduces application latency for mobile computing tasks like online gaming and video streaming. Nevertheless, it remains a difficulty to provide a effective and performance-guaranteed edge service offloading and migration in the MEC environment. Most existing contributions in this area consider task offloading as a offline decision making process by exploiting transient positions of mobile requesters as model inputs. In this work instead, we develop a predictive-trajectory-aware and online MEC task offloading strategy. Simulations based on real-world MEC deployment datasets and a campus mobile trajectory datasets clearly illustrate that our approach outperforms state-of-the-art ones in terms of effective service rate and migration overhead.