Explainable AI-Enabled Framework for Predictive Resource Allocation and Intelligent Process Optimization in Advanced Operating Systems

Muhammad Hasnain Sultan; Hafiza Aysha Asghar; Muhammad Safwan Ashraf; Ghulam Muhy Ud Deen Raee; Muhammad Azam; Mubasher Hussain Malik

doi:10.5281/zenodo.20487561

Authors

Muhammad Hasnain Sultan Department of Computer Science & Information Technology, University of South Punjab (USP), Multan, Punjab, Pakistan Author
Hafiza Aysha Asghar Department of Computer Science & Information Technology, University of South Punjab (USP), Multan, Punjab, Pakistan Author
Muhammad Safwan Ashraf Department of Computer Science & Information Technology, University of South Punjab (USP), Multan, Punjab, Pakistan Author
Ghulam Muhy Ud Deen Raee Department of Computer Science & Information Technology, University of South Punjab (USP), Multan, Punjab, Pakistan Author
Muhammad Azam Department of Computer Science & Information Technology, University of South Punjab (USP), Multan, Punjab, Pakistan Author
Mubasher Hussain Malik Department of Computer Science & Information Technology, University of South Punjab (USP), Multan, Punjab, Pakistan Author

DOI:

https://doi.org/10.5281/zenodo.20487561

Keywords:

Explainable Artificial Intelligence (XAI), Advanced Operating Systems, Predictive Resource Allocation, Intelligent Process Optimization, Deep Learning, Reinforcement Learning, Bayesian Inference, Resource Scheduling, Uncertainty-Aware Decision Making, System Optimization

Abstract

The increasing complexity of modern computing environments has created significant challenges in resource management, process scheduling, and system optimization within advanced operating systems. Traditional operating system management approaches often rely on static policies and deterministic scheduling mechanisms, which struggle to adapt to dynamic workloads and heterogeneous resource demands. To address these limitations, this study proposes an Explainable AI-enabled framework for predictive resource allocation and intelligent process optimization in advanced operating systems. The proposed framework integrates deep learning, reinforcement learning, Bayesian decision modeling, multi-modal data fusion, and Explainable Artificial Intelligence (XAI) to enable adaptive, transparent, and uncertainty-aware decision-making. Deep learning models are employed for workload forecasting and resource demand prediction, while reinforcement learning supports intelligent process scheduling and adaptive optimization. Bayesian inference enhances uncertainty handling, and XAI mechanisms provide feature importance analysis, confidence estimation, and interpretable decision rationales. Experimental evaluation demonstrates significant improvements over conventional operating system management approaches. The proposed framework achieved a decision accuracy of 91.6%, reduced latency from 67.9 ms to 38.2 ms, improved fault tolerance from 55.1% to 85.3%, enhanced uncertainty handling from 42.7% to 88.1%, and increased explainability scores from 30.5% to 79.4%. These results confirm that the integration of predictive analytics, intelligent process optimization, and explainable AI can significantly improve resource utilization, system responsiveness, reliability, and transparency. The proposed framework provides a scalable and trustworthy solution for next-generation intelligent operating systems and autonomous computing environments.

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