Real Time Scheduling

Real-time scheduling focuses on efficiently allocating computing resources to tasks with strict deadlines, ensuring timely completion despite unpredictable workloads and resource constraints. Current research emphasizes developing algorithms and frameworks that improve scheduling performance in diverse applications, including robotics, cloud computing, and power systems, often leveraging reinforcement learning, deep learning, and advanced optimization techniques like simulated annealing to dynamically adapt to changing conditions. These advancements are crucial for enabling the reliable operation of complex systems requiring immediate responses, impacting fields ranging from autonomous vehicles and industrial automation to sustainable energy management.