A Context-Aware Intelligent Scheduling Framework for Modern Operating Systems Using Hybrid Machine Learning Models - Savarimalai, Pandikumar; Chellappan, Menaka; Nidhya, Murugaiyan Swaminathan; Thomas, Margaret Mary; Nallathambi, Sevugapandi; Selvaraj, Manjula (Journal of Computational and Cognitive Engineering, 2026)
In modern computing environments, the operating systems are required to handle dynamic and complex applications with heterogeneous workloads. These workloads include interactive, data-centric, and high-computing applications. Interactive tasks require low latency and high response, while data-centric transactions demand high resource utilization and input/output. Likewise, high-computing processes including machine learning tasks require sustained throughput. Traditional CPU scheduling, like round-robin and priority scheduling, highly relies on static and rule-based systems. Generally, these give equal weightage to all processes. They are not paying attention to the contextual variations and working patterns that impact the responsiveness, efficiency, and overall performance. There is an evident need for a context-aware and adaptive CPU scheduling mechanism that will handle applications and processes based on context. To address this gap, this paper introduces an application-aware intelligent scheduling mechanism that integrates a random forest (RF) classifier and deep Q-network (DQN) reinforcement learning. The RF classifies the processes based on their working and behavioral patterns, and then DQN has system state and queue dynamics to make adaptive scheduling decisions. It can be continuously learned and adjusted based on the user's needs; it is responsive and achieves high throughput. Experimental evaluations are conducted on generative datasets and show remarkable performance improvements. The proposed framework achieves up to 42.2% responsive time reduction on interactive application workloads, 29.2% throughput gain against the machine learning training workloads, and 22.8% for database transactions. The architecture is computationally sparse and uses the existing Linux scheduling system. This renders it a convenient and scalable method of CPU scheduling in contemporary multicore systems.Received: 2 July 2025 | Revised: 17 December 2025 | Accepted: 6 January 2026Conflicts of InterestThe authors declare that they have no conflicts of interest to this work.Data Availability StatementData are available from the corresponding author upon reasonable request.Author Contribution StatementPandikumar Savarimalai: Conceptualization, Validation, Project administration. Menaka Chellappan: Conceptualization, Formal analysis, Writing – review & editing, Supervision. Murugaiyan Swaminathan Nidhya: Methodology, Investigation, Resources. Margaret Mary Thomas: Validation, Data curation. Sevugapandi Nallathambi: Software, Writing – review & editing. Manjula Selvaraj: Investigation, Writing – original draft, Visualization.
Keywords: intelligence CPU scheduling; context-aware scheduling; reinforcement learning; random forest classification; deep Q-network