Urban traffic congestion imposes significant economic and environmental costs worldwide. Conventional adaptive signal controllers-including actuated systems and deep reinforcement-learning (DRL) approaches-either collapse under saturation or lack formal verifiability, making them unsuitable for safety-critical infrastructure. This paper presents the Traffic Light Management Operating Framework (TLMOF), a signal control architecture that maps classical operating-system (OS) scheduling theory directly onto intersection management. Vehicles are modeled as processes, approach lanes as ready queues, and the intersection controller as a CPU scheduler. The TLMOF Hybrid Kernel selects signal phases by maximising a modified pressure function combining Max-Pressure (MP) throughput control with Weighted Fair Queuing (WFQ) anti-starvation. Gridlock is formally characterised as a Coffman deadlock, detected via Wait-for-Graph (WFG) depth-first search in O(V+E) time, and resolved through a formally specified Flush Phase. With fairness weight β > 0, the kernel provably guarantees bounded delay for all movements (AntiStarvation Theorem). SUMO 1.26 validation across three demand scenarios demonstrates a 377× average-delay reduction over fixed-time under saturation and a 25% Jain's Fairness Index improvement over pure Max-Pressure (0.909 vs. 0.727) under asymmetric demand while reducing average delay by 56% (3.62 s vs. 8.15 s). A sensitivity analysis of (α, β) confirms that the recommended default (0.7, 0.3) achieves a robust balance between throughput and fairness.
Kumaran K, Richika Rani, Mokshdaa Gupta et al.· 2026 7th International Confe...· 0 citations
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.
Durgesh Kumar, Aviskha Talukdar, Nandani et al.· 2026 7th International Confe...· 0 citations