LiMPO: lightweight mobility prediction and offloading framework using machine learning for mobile edge computing

Several applications have emerged with the proliferation of mobile devices to provide communication, learning, social networking, entertainment, and community computing services. Such applications include augmented reality, online gaming, and other real-time applications that need higher computational resources. These applications, executing on mobile devices, often need to access external computing resources and offload the application tasks to the cloud or mobile edge computing (MEC) servers. However, delivering task offloading results to the users in the MEC environment is a challenge, certainly when user mobility is high. Sub-optimal server selection at the offloading stage increases latency, energy consumption and deteriorates both quality of experience and quality of service. Existing techniques proposed in the literature handle computation offloading and mobility management separately. Without considering the real-time mobility factors, the solutions produced are sub-optimal. Some solutions exist to manage mobility, but they involve higher time complexity. We consider the user mobility in offloading decisions and present a lightweight mobility prediction and offloading (LiMPO) framework that offloads the compute-intensive tasks to the predicted user location using artificial neural networks with less complexity. In addition, we propose a multi-objective genetic algorithm based server selection technique that jointly optimizes latency and energy consumption while improving the resource utilization of MEC servers. The performance of the proposed framework is compared with two other techniques task-assignment with optimized mobility and dynamic mobility-aware offloading algorithm for edge computing. The simulation results show that LiMPO outperforms the others by latency reduction, energy efficiency, and enhanced resource utilization.