Shahid Rajaee Teacher Training University Journal of Electrical and Computer Engineering Innovations (JECEI) 2322-3952 14 2 2026 01 01 Early-Stage Resource-Bound Prediction for Threads Using Real-Time Kernel Event Analysis 583 597 12573 10.22061/jecei.2026.12900.914 EN Morteza Noferesti Department of Engineering, Bozorgmehr University of Qaenat, Qaen, South Khorasan, Iran. Farzad Amiri Delouei Department of Engineering, Bozorgmehr University of Qaenat, Qaen, South Khorasan, Iran. Sarah Aryan Department of Engineering, Bozorgmehr University of Qaenat, Qaen, South Khorasan, Iran. Journal Article 2026 01 20 <strong>Background and Objectives:</strong> Modern operating systems struggle to manage threads with dynamic resource demands, as traditional schedulers rely on reactive heuristics that often misclassify thread behavior. This paper introduces a proactive thread classification methodology that predicts resource-bound categories by analyzing kernel event streams in real time. <br /><strong>Methods: </strong>Our proposed five-step pipeline includes: (1) kernel event collection using LTTng, (2) system call categorization into a seven-category taxonomy covering 57 system calls, (3) PID/TID labeling based on resource usage, (4) feature extraction from the first five events, and (5) predictive modeling with multiple machine learning classifiers. <br /><strong>Results:</strong> Our evaluation of six machine learning models, including Random Forest, LightGBM, Stacked Ensemble, MLP, CNN-BiLSTM, and BERT demonstrates that Random Forest delivers the optimal balance of high predictive performance (93.4% precision, 92.5% recall) and low inference latency (178 µs), outperforming both other ensemble methods and computationally expensive deep learning architectures. When applied to a real-world dataset [30], this optimized methodology achieves 89% precision in thread classification, which directly translates to significant system-level improvements: a 41% reduction in tail latency for interactive applications and sustained 93% CPU utilization for cpu-bound tasks.<br /><strong>Conclusion:</strong> This paper demonstrates the efficacy of a novel, proactive thread classification methodology that accurately predicts a thread's future resource-bound category within a critically short 100 µs window from its execution start. By instrumenting a five-step pipeline, the approach successfully translates fine-grained system call sequences into predictive signatures for resource constraints, such as identifying I/O-bound threads from read/write patterns. This early detection capability provides a timely and actionable foundation for operating system schedulers to preemptively optimize thread prioritization and resource allocation, thereby enhancing overall system performance and responsiveness. https://jecei.sru.ac.ir/article_12573_f00b1feeac89bd4737a3329ff92c07c0.pdf