Network Resource Optimization for Mobile Edge Computing: A Deep Learning Approach

With the proliferation of mobile devices, the demand for efficient network resource management and low-latency computing has become increasingly critical. Mobile edge computing (MEC) emerges as a promising solution by enabling task offloading to network edges, thereby reducing latency and energy consumption while improving user experience. However, the limited storage, computing, and communication capabilities of MEC systems pose significant challenges for resource optimization. This paper addresses these challenges by proposing a deep learning-based approach to optimize task offloading decisions in MEC networks. We formulate a task offloading model that balances local computation and edge offloading, leveraging deep learning algorithms to minimize long-term expected costs associated with task processing delays and failures. Through extensive simulations, we evaluate the performance of our approach under varying hyperparameters, including learning rates, batch sizes, and optimizers.