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.
Patro et al. (Fri,) studied this question.
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