Joint optimization of edge computing data offloading and energy awareness using user mobility

With the progress of wireless communication technology and software technology, a large number of new intelligent applications and services are emerging, which puts higher demands on the latency and processing performance of mobile user devices. Traditional centralized cloud computing has bottlenecks in dealing with real-time and highly interactive services. As a result, edge computing has emerged to offload computing tasks to the network edge near users to reduce latency and enhance service experiences. This work proposes an Optimization Algorithm of Data Unloading and Energy Awareness Based on User Mobility (UM-OADU&EA) for mobile users' data offloading and energy-aware optimization issues. By considering the time-varying channel characteristics of users and their quality of service requirements, the model achieves effective offloading of task data and dynamic adjustment of system energy consumption. Simulation results indicate a significant improvement in user utility and a reduction in data offloading failure rates compared to traditional models. Specifically, as the average data and computation load increase, the UM-OADU&EA model improves user utility by at least 45%, with a maximum data offloading failure rate not exceeding 20%. The research on the joint optimization model of data offloading and energy awareness for user mobility has practical significance in the field of communication and computing fusion.