Design of an Improved Method for Task Scheduling Using Proximal Policy Optimization and Graph Neural Networks

Modern computational environments have been getting more and more complex and dynamic; hence, mechanisms of task scheduling are increasingly sophisticated in order to guarantee optimal resource usage and reduced latency. Traditional heuristic-based approaches, although very fast, often fail to meet the intricacies of the dependencies and real-time variability that exist in large-scale systems and hence result in far from optimal task completion timestamp and resource inefficiencies. Existing machine learning methods have improved but still remain unstable, lacking domain-specific adjustability for robust scheduling across a very broad workload spectrum. We provide an integrated scheduling framework that integrates Proximal Policy Optimization, Graph Neural Networks, hybrid rule-based and machine learning techniques, and synthetic data generation with Generative Adversarial Networks in this paper. PPO has been chosen to strike a balance between exploration and exploitation while considering efficient task scheduling in a dynamic environment. The GNNs are used to model complicated relations of tasks with resources, describing dependencies usually ignored by traditional models. This hybrid approach exploits domain knowledge via rule-based filtering and refines scheduling decisions with machine learning to bring flexibility and accuracy into the same platform. They are also applied to generate realistic synthetic workload traces. These augment the real data and, therefore, improve the robustness and generalization of the scheduling models. Our framework improves important performance metrics of the job completion timestamp by 20%-30%, resource utilization by 15%-25%, the latency of scheduling by 25%, and enhancing the accuracy of task prioritization by 30%. These results underline the potential of hybrid AI approaches to advance task scheduling mechanisms, offering much more adaptability, efficiency, and resiliency for computational systems today.