Similar articles

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.

XIRAC：一种优化的具有无限制任务及创新编程范式的基于最大熵且面向产品的近实时操作系统

[1]Abohamama AS, El-Ghamry A, Hamouda E, 2022. Real-time task scheduling algorithm for IoT-based applications in the cloud–fog environment. J Netw Syst Manag, 30(4):54.

[2]Avan A, Azim A, Mahmoud QH, 2023. A state-of-the-art review of task scheduling for edge computing: a delay-sensitive application perspective. Electronics, 12(12):2599.

[3]Fampa M, Lee J, 2024. An outer-approximation algorithm for maximum-entropy sampling. Discrete Appl Math, 347:271-284.

[4]Fang JH, Zhang R, Zhou AY, 2020. Load balance for distributed real-time computing systems. In: Zheng WA, Wang SP, Fang JH, et al. (Eds.), East China Normal University Scientific Reports. World Scientific, Singapore, p.83-107.

[5]Gaikwad JS, 2024. Multi-core system classification algorithms for scheduling in real-time systems. In: Kaiser MS, Xie JY, Rathore VS (Eds.), ICT: Smart Systems and Technologies. Springer, Singapore, p.229-237.

[6]He S, Li SZ, Chen Y, et al., 2015. Uncertainty analysis of race conditions in real-time systems. Proc IEEE Int Conf on Software Quality, Reliability and Security, p.227-232.

[7]Inc. ICP DAS USA, 2024. Zigbee Wireless Modbus RTU Data Acquisition. https://www.icpdas-usa.com/zigbee_alliance_wireless_data_acquisition [Accessed on Feb. 10, 2024].

[8]Khezerloo D, Nedaie HA, Farhood B, et al., 2017. Optical computed tomography in PRESAGE^® three-dimensional dosimetry: challenges and prospective. J Cancer Res Ther, 13(3):419-424.

[9]Khosravi Tanak A, Najafi M, Mohtashami Borzadaran GR, 2024. A new lifetime distribution by maximizing entropy: properties and applications. Prob Eng Inform Sci, 38(1):189-206.

[10]Kiyani A, Abdollahzadeh M, Sadat Kiai SM, et al., 2011. Designing of a quadrupole Paul ion trap. J Fusion Energy, 30(4):291-293.

[11]Kondaveeti HK, Kumaravelu NK, Vanambathina SD, et al., 2021. A systematic literature review on prototyping with Arduino: applications, challenges, advantages, and limitations. Comput Sci Rev, 40:100364.

[12]Li SZ, Zhang YZ, Luo HY, et al., 2018. Race-condition-aware and hardware-oriented task partitioning and scheduling using entropy maximization. IEEE Trans Parall Distrib Syst, 29(7):1589-1604.

[13]Lisitsin DV, Gavrilov KV, 2024. The use of maximum entropy principle to construct robust estimators under point Bayesian contamination. Part I. Appl Math Contr Sci, (1):‍‍55-72 (in Russian).

[14]Microchip Technology, 2023. Microchip Studio for AVR^® and SAM Devices. https://www.‍microchip.‍com/en-us/tools-resources/develop/microchip-studio [Accessed on Feb. 5, 2024].

[15]Min-Allah N, Khan SU, Wang YJ, 2012. Optimal task execution times for periodic tasks using nonlinear constrained optimization. J Supercomput, 59(3):1120-1138.

[16]Najmi AH, Moon T, 2020. Advanced Signal Processing: a Concise Guide. McGraw-Hill, New York, USA.

[17]Rincón CAC, Cheng AMK, 2017. Using information theory principles to schedule real-time tasks. Proc 51^st Annual Conf on Information Sciences and Systems, p.1-6.

[18]Rincón CAC, Cheng AMK, 2018. SITSA-RT: an information theory inspired real-time multiprocessor scheduler. Proc IEEE 21^st Int Symp on Real-Time Distributed Computing, p.156-163.

[19]Schäffler S, 2024. Mathematics of Information: Theory and Applications of Shannon‍–‍Wiener Information. Springer, Berlin, Germany.

[20]Scharfenaker E, Yang J, 2020. Maximum entropy economics. Eur Phys J Spec Top, 229(9):1577-1590.

[21]Shannon CE, 1948. A mathematical theory of communication. Bell Syst Tech J, 27(3):379-423.

[22]Sharma R, Nitin, 2013. Visualization of information theoretic maximum entropy model in real time distributed system. Proc 3^rd Int Conf on Advances in Computing and Communications, p.282-286.

[23]Ünsalan C, Gürhan HD, Yücel ME, 2022. Embedded System Design with ARM Cortex-M Microcontrollers: Applications with C, C++ and MicroPython. Springer, Cham, Switzerland.

[24]Wang KC, 2017. Embedded and Real-Time Operating Systems. Springer, Cham, Switzerland.

[25]Zirak A, 2023. XIRAC-Q: a near-real-time quantum operating system scheduling structure based on Shannon information theorem. Quantum Inform Process, 22(11):403.

[26]Zirak A, Roshani S, 2016. A reduced switch voltage stress class E power amplifier using harmonic control network. Int J Adv Comput Sci Appl, 7(5):38-42.

[27]Zirak A, Beygi P, Mirzakhah S, 2017. Dynamic estimation of the modeling error statistics in diffuse optical tomography. Inverse Probl Sci Eng, 25(4):492-505.