A Formally Verified AI Framework for Scheduling OptimiSation in Slurm-Based Distributed HPC Systems
1 Friedrich-Alexander-Universität Erlangen-Nurenberg (FAU), Germany Department of Artificial Intelligence in Biomedical Engineering.
2 Uganda Martyrs University, Nkozi Department of Computer and Information Systems.
3 Kyambogo University, Uganda Department of Electrical and Electronics Engineering.
* Corresponding Author
ORCID Details
Robert W. Bakyayita: 0009-0001-8580-9196
Brian J Kasozi: 0000-0001-6569-3969
Denise Joanita Birabwa: 0000-0002-9477-1556
Research Article
Open Access Research Journal of Science and Technology, 2026, 17(02), 099–108
Article DOI: 10.53022/oarjst.2026.17.2.0076
Publication history:
Received on 04 July 2026; revised on 11 August 2026; accepted on 13 August 2026
Abstract:
This paper presents an artificial intelligence (AI)-enabled scheduling framework for Slurm that integrates formal verification techniques with data-driven optimization methods. The proposed architecture combines concepts from queueing theory, Markov chain modeling, graph theory, mathematical optimization, reinforcement learning, and swarm intelligence to support adaptive scheduling decisions while preserving system correctness and reliability. By unifying these complementary approaches, the framework enhances scheduling efficiency, improves resource utilization, and increases operational robustness in dynamic HPC environments.
Experimental evaluation demonstrates that the proposed framework consistently outperforms conventional scheduling ap-proaches across multiple performance metrics. Specifically, it achieves higher job throughput, shorter queue waiting times, improved resource utilization, and stronger fault tolerance, highlighting its effectiveness for next-generation HPC workload management.
Keywords:
High-Performance Computing (HPC), Slurm Workload Manager, Distributed Computing, Parallel Computing, Cluster Com-putting, Artificial Intelligence, Formal Verification, Queueing Theory, Markov Chain Modeling, Graph Theory, Reinforcement Learning, Swarm Intelligence, Resource Scheduling, Fault-Tolerant Systems, Scheduling Optimisation.
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Copyright information:
Copyright © 2026 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0