Joint AI Service Placement, Task Scheduling, and Resource Allocation for IoT in 6G Networks
Zhenyu Zhang, Lu Lu, Qin Li, Yuhao Chai, Di Wu, Yong Zhang · IEEE Internet of Things Journal · 2025
As Internet of Things (IoT)-based artificial intelligence (AI) applications grow, the surge in computational and communication demands has raised concerns about energy consumption, making it critical for 6G networks to address this challenge. This paper examines the joint optimization of AI service placement, task scheduling, and computing resource allocation in an edge-network-cloud system to minimize long-term energy consumption. These problems are interdependent: AI service placement determines service locations, influencing task scheduling, which in turn dictates computing resource allocation. The key challenge lies in the coupling of these variables and the two time-scale nature of the problem, involving long-term (AI service placement) and short-term (task scheduling and computing resource allocation) strategies. To address this, a Hierarchical Markov Decision Process (HMDP) framework is proposed for efficient and coordinated optimization across time scales. A Hierarchical Mean-Field Dueling Double Deep Q-Network (HMFD3QN) algorithm is developed within this framework, where the upper layer optimizes AI service placement, and the lower layer manages task scheduling and computing resource allocation. By integrating mean-field theory, the algorithm reduces the complexity of multi-agent interactions. The computing resource allocation problem is shown to be convex when other variables are fixed, and an optimal strategy is derived using Karush-Kuhn-Tucker (KKT) conditions to simplify the action space for reinforcement learning. Experimental results demonstrate that the proposed method can reduce energy consumption by up to 34% compared to baseline methods, significantly improve queue stability, and increase the proportion of tasks meeting QoS requirements.