Joint Task Offloading and Resource Allocation in Ultra-Dense Multi-Access Edge Computing: A Mean Field Learning Approach

In ultra-dense multi-access edge computing (MEC), efficient task offloading and resource allocation are critical to meeting stringent delay and energy constraints. However, the rapid growth in terminal devices poses a significant challenge to traditional deep reinforcement learning (DRL)-based methods, which struggle to maintain efficient task offloading and resource allocation due to increased computational complexity and decision latency. To address this challenge, in this paper, we propose a mean field theory (MFT)-guided DRL approach which leverages the statistical characteristics of a large population of terminal devices to reduce the computational complexity of decision-making. Firstly, we formulate the joint task offloading and resource allocation problem as a mean field Markov decision process (MFMDP) with the objective of minimizing the overall system energy consumption while satisfying the task delay requirements and resource constraints. Secondly, by leveraging the inherent structure of the MFMDP, which represents the system dynamics using state and action distributions rather than joint action spaces, we achieve significant dimensionality reduction and improved scalability. Thirdly, we prove that the Q-function of MFMDP satisfies the fixed point theory, and considering the continuity of task offloading and resource allocation variables, we develop an MFT-guided deep deterministic policy gradient (MFT-DDPG) algorithm to solve the proposed problem. Experimental results show that MFT-DDPG significantly outperforms conventional DRL baselines in convergence speed and scalability. Specifically, for 50 terminal devices, the training time of proposed MFT-DDPG is reduced by up to 86.1%, 92.1%, and 94.6% compared to those of MADDPG, QMIX, and DDPG, respectively. In comparison with the Edge Server Computing Only (ECO) scheme and Device and Edge Server Collaborative Computing (DECC) scheme, the proposed method consistently yields lower energy consumption and delay across varying numbers of terminal devices, transmission power, and edge server computing resources compared to benchmark solutions, demonstrating its robustness and effectiveness in ultra-dense MEC scenarios.