Abstract: The research on task offloading and migration strategies in optimization model-driven Mobile Edge Computing(MEC) networks is conducted against the backdrop of the surge in Internet of Things(IoT) devices and the widespread adoption of 5G technology.MEC significantly reduces data transmission latency and cloud-side pressure by migrating computing resources to the network edge.This study proposes a series of task offloading and migration strategies and validates their effectiveness through performance evaluations.Experimental results demonstrate that the proposed strategies optimize key performance indicators in typical application scenarios: latency is reduced by approximately 25%,energy consumption is decreased by 30% and task throughput is increased by 20%.Specific optimizations include dynamic resource scheduling for load balancing and improved offloading efficiency;a Quality of Service(QoS)-guaranteed migration mechanism to ensure service stability; and cross-layer optimization design to enhance multi-task collaboration capabilities.Additionally,machine learning-based prediction techniques are employed to dynamically adapt to network fluctuations,thereby improving system flexibility.The research conclusions indicate that the optimization model offers significant advantages in ensuring efficient resource allocation and task real-time performance,thereby enhancing the service quality and user experience of MEC networks.The strategies can be widely applied in heterogeneous networks and dynamic environments,demonstrating potential for further expansion.

Key words: Mobile edge computing, Task offloading, Optimization model, Genetic algorithm, Migration strategy