Load-Aware Adaptive Scheduling (LAAS) System: A Real-Time CPU Scheduler for Minimal Operating System Kernels

Abstract

Adaptive CPU scheduling that responds to dynamic workload characteristics remains an open challenge for minimal operating systems. Traditional schedulers employ static parameters unsuitable for heterogeneous workloads, while recent machine learning approaches incur overhead inappropriate for resource-constrained environments. This paper presents Load-Aware Adaptive Scheduling (LAAS), a novel approach that integrates real-time system load classification with entropy-inspired process scoring to enable dynamic scheduling decisions. The core innovation—a load-dependent scoring formula where weight values adapt based on system load class—represents the first application of entropy principles to CPU process selection logic in a minimal kernel. LAAS achieves adaptation without machine learning runtime overhead (sub-0.5% CPU vs. 1% for ML-based approaches) and is suitable for minimal monolithic kernels (approximately 1,500 LOC total implementation). Experimental evaluation on synthetic workloads demonstrates 31% reduction in average waiting time, 28% reduction in context switches, and 18% improvement in CPU utilization compared to standard Round-Robin scheduling, while achieving a Jain fairness index of 0.94 in our test scenarios. These results indicate that load-aware, entropy-inspired adaptive scheduling is practically feasible in severely resource-constrained kernel environments.