Abstract: In the fiercely competitive landscape of product-oriented operating systems, including the Internet of Things (IoT), efficiently managing a substantial stream of real-time tasks coexisting with resource-intensive user applications embedded in constrained hardware presents a significant challenge. Bridging the gap between embedded and general-purpose operating systems, we introduce XIRAC, an optimized operating system shaped by information-theory principles. XIRAC leverages Shannon’s information theory to regulate processor workloads, minimize context switches, and preempt processes by maximizing system entropy tolerance. Unlike prior approaches that apply information theory to task priority alignment, the proposed method integrates maximum entropy into the core of the real-time operating system (RTOS) and scheduling algorithms. Subsequently, we optimize numerous system parameters by shifting and integrating commonly used unlimited tasks from the application layer to the kernel. We describe the advantages of this architectural shift, including improved system performance, scalability, and adaptability. A new application-programming paradigm, termed “object-emulated programming,” has emerged from this integration. Practical implementations of XIRAC in diverse products have revealed additional benefits, including reduced learning curves, elimination of library functions and threading dependencies, optimized chip capabilities, and increased competitiveness in product development. We provide a comprehensive explanation of these benefits and explore their impact through real-world use cases and practical applications.

Key words: Task scheduling; Information theory; Embedded real-time operating system (RTOS); Maximum entropy; Load balancing

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