A Hierarchical Deep Reinforcement Learning Approach for Joint Dependent Task Offloading and Service Placement in MEC

In large-scale IoT environments, two major challenges—limited edge storage resources and complex task dependencies—make efficient management of service placement and task offloading particularly difficult. Existing approaches often optimize these two aspects independently while overlooking their tight interrelationship, resulting in poor performance in dynamic settings. To address this co-optimization challenge, we propose a Hierarchical Deep Q-Network (HDQN) framework that simultaneously manages service placement and task offloading in task-dependent MEC systems. HDQN divides the decision process into two levels: a meta-controller for long-term service placement and resource planning, and a subcontroller that makes real-time task offloading decisions based on the latest system state. This two-layer structure enables the framework to efficiently adapt to changing conditions while meeting both dependency and resource constraints. Evaluation across diverse experimental conditions—including varying numbers of users, MEC servers, communication rates, and service types—demonstrates that our proposed HDQN framework achieves a significant enhancement in task latency optimization compared to mainstream advanced algorithms like DDPG and DQN, underscoring its superior performance.