Task scheduling methods in real-time systems

Abstract

Real-time systems are an integral part of modern technologies, covering the areas of production automation, medical devices, transport systems and other critical areas. These systems require ensuring timely and reliable execution of tasks, which makes the issue of effective dispatching particularly relevant. This article presents an overview and analysis of modern methods of dispatching tasks in real-time systems, including static, dynamic and combined approaches. The paper highlights the importance of balancing predictability and flexibility in dispatching methods to improve system efficiency and reliability. Special attention is paid to key challenges such as load balancing between processors, ensuring reliability and fault tolerance, efficient use of resources, dynamic adaptation to changing conditions and consideration of power consumption. The development of new methods and the improvement of existing approaches must take these challenges into account to achieve optimal performance. Special attention is paid to the prospects of using machine learning for load forecasting and dynamic optimization of dispatching algorithms. The use of such technologies allows to significantly increase the efficiency and reliability of real-time systems, providing adaptive management of tasks in real time. The mathematical modelling carried out in the article allows to evaluate the effectiveness of different dispatching methods according to key criteria such as response time, throughput, scalability, resource utilization, reliability and flexibility. The results show that no method is universally best, and the choice of the optimal approach depends on the specific requirements of a particular system. The paper outlines directions for further research, including the integration of machine learning techniques to improve the adaptability and performance of real-time systems. This is an important step in overcoming the challenges of optimizing the use of resources and meeting deadlines in a changing environment, which will contribute to further progress in this field.