APPO: Task Scheduling With Deep Reinforcement Learning in Cloud–Fog Collaborative Computing for Industrial Cyber-Physical Systems

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Abstract
With the large-scale deployment of industrial cyber-physical system (ICPS) terminals such as drones and robots, edge computing often falls short in multidevice collaboration scenarios. Fog computing overcomes this issue by integrating different resources across a distributed node network, thereby enabling more efficient system-wide coordination. However, the limited resources of fog nodes make independently addressing the efficient scheduling demands of massive devices and complex tasks difficult. To address this problem, an attention-enhanced proximal policy optimization (APPO) algorithm is proposed in this paper. First, a dual-branch attention-based architecture is designed to model the complex dependencies between task requirements and multilayer resource states, thereby effectively capturing the coupling relationships in a hierarchical environment. Next, the PPO framework is leveraged with a centralized critic that integrates global system views, which enables coordinated scheduling decisions that are optimized across a latency-sensitive fog layer and a resource-rich cloud layer. Finally, a fault tolerance strategy is constructed to address the higher failure probabilities of distributed fog nodes through real-time monitoring and dynamic rescheduling, which significantly increases the robustness of the system. The experimental results demonstrate that the proposed APPO algorithm outperforms the existing baseline approaches and achieves a 28.15% higher task success rate while reducing virtual machine (VM) costs, energy consumption, and average delays by 20.94%, 21.33%, and 56.12%, respectively.
Funding Information
  • National Natural Science Foundation of China (92367103)

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