SC-PPO: Spectral clustering-guided Proximal Policy Optimization for distributed workflow scheduling in cloud–fog computing

In fog–cloud computing, efficient task scheduling is crucial to meet the performance requirements of modern applications such as smart healthcare, intelligent transportation, industrial automation. It needs to process large-scale, latency-sensitive,dependency-rich tasks, which can be modeled as workflow-directed acyclic graphs (DAGs). Existing task scheduling mechanisms face difficulties in managing conflicting objectives such as makespan, energy, fault tolerance. To overcome these difficulties, we introduce a novel hybrid task scheduling framework called SC-PPO, which integrates spectral clustering with Proximal Policy Optimization (PPO), a deep reinforcement learning algorithm. The spectral clustering technique is first used to cluster structurally similar tasks, thereby simplifying task scheduling problem and obtaining a higher-level abstraction for decision-making. A PPO agent is then employed to schedule the clustered tasks based on task characteristics, virtual machine (VM) status, reliability values, resource availability. The PPO agent is trained using a multi-objective reward function that balances makespan, energy, task reliability, and trust-aware VM selection. Simulation experiments were conducted in a diverse fog–cloud simulation environment on the Google Cloud Jobs (GoCJ) dataset. The proposed SC-PPO approach was compared with three representative baselines: the Reliability-Improved Whale Optimization Algorithm (RIWOA), Deep Q-Network (DQN), and Advantage Actor–Critic (A2C) algorithm. The results obtained indicate that the proposed SC-PPO approach outperforms the baselines with more than a 20% improvement in makespan, lower energy consumption, higher reliability scores, and improved scalability for handling large-scale workloads.