Computational Urgency: Real-Time Computing Models and Architectures

Abstract

In response to the increasing demand for real-time performance across domains such as autonomous systems, industrial control, and critical healthcare, this paper introduces the concept of Computational Urgency (CU)—a novel paradigm that reorients computing systems around task prioritization driven by temporal and contextual criticality. Unlike traditional models that emphasize throughput or fairness, CU focuses on minimizing response time for high-urgency tasks through dynamic scheduling, intelligent resource allocation, and architecture-level adaptability. The study formalizes CU, outlines its foundational technologies—including AI-enhanced scheduling and edge-aware architectures—and validates its efficacy through diverse case studies and empirical simulations. Significant improvements in responsiveness and system robustness are demonstrated in urgency-sensitive scenarios. Furthermore, the paper highlights pressing challenges such as fairness under prioritization, urgency manipulation risks, and the need for interoperable urgency standards. The findings suggest that CU offers a foundational framework for future real-time systems, representing a paradigm shift in computing where urgency becomes the central organizing principle.