Arun Kumar
Independent Researcher
India
Abstract
Real‐time systems demand predictable and timely task completion under stringent deadlines. Over the past four decades, various scheduling algorithms—most notably Rate Monotonic Scheduling (RMS), Earliest Deadline First (EDF), and Deadline Monotonic (DM)—have been proposed to balance processor utilization, deadline adherence, and robustness against overload. This manuscript presents a comparative analysis of these algorithms, focusing on their theoretical foundations, implementation considerations, and performance in representative engineering case studies from avionics, automotive control, and industrial automation. Through simulation experiments using established benchmarks, we evaluate schedulability, average response time, and processor utilization. We identify critical research gaps, including transient overload management, mixed‐criticality support, and energy‐aware scheduling. The methodology employs time‐driven simulation with standardized task sets drawn from Liu and Layland’s seminal model and subsequent extensions by Audsley et al. Results indicate that EDF achieves optimal processor utilization but suffers from unpredictability under overload, whereas RMS offers simplicity at the cost of lower utilization. Deadline Monotonic provides a middle ground but requires careful tuning. We conclude with recommendations for hybrid approaches and outline future directions in adaptive and mixed‐criticality scheduling.
Keywords
Rate Monotonic, Earliest Deadline First, Deadline Monotonic, real‐time scheduling, processor utilization, overload behavior
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