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Early-Stage Resource-Bound Prediction for Threads Using Real-Time Kernel Event Analysis | ||
| Journal of Electrical and Computer Engineering Innovations (JECEI) | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 24 خرداد 1405 | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22061/jecei.2026.12900.914 | ||
| نویسندگان | ||
| Morteza Noferesti* ؛ Farzad Amiri Delouei* ؛ Sarah Aryan | ||
| Department of Engineering, Bozorgmehr University of Qaenat, Qaen, South Khorasan, Iran. | ||
| تاریخ دریافت: 30 دی 1404، تاریخ بازنگری: 07 خرداد 1405، تاریخ پذیرش: 21 خرداد 1405 | ||
| چکیده | ||
| Background and Objectives: 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. Methods: 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. Results: 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. Conclusion: 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. | ||
| کلیدواژهها | ||
| Performance Analysis؛ Proactive Scheduling, Kernel Events؛ Behavioral Profiling؛ Applied AI | ||
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