Optimized dependency-aware task offloading and resource allocation via multi-stage Imitation Learning in mobile edge computing

In the realm of mobile edge computing, the energy consumption and stability of offloading can be significantly influenced by random task arrivals and constraints imposed by task dependencies. To address this challenge, this study proposes a Multi-stage Generative Adversarial Imitation Learning (Multi-stage GAIL) algorithm. This algorithm engages in multi-stage continuous optimization to handle the offloading and allocation processes of dependent tasks. By integrating a simulation system, the algorithm effectively disentangles dependency relationships and generates a queue of tasks ready for execution. It obtains the optimal decision in multiple stages, effectively addressing the impact of the random task arrivals and dependency constraints on task offloading and allocation. Consequently, it leads to enhancements in system performance compared to the baseline algorithm.