Scheduling algorithms for real-time problem-solving in smart technologies over the cloud environment

The increasing rate of development of smart technologies has highlighted effective scheduling algorithms as a solution to the real-time problem-solving aspect of the cloud. This chapter discusses the spheres scheduling algorithms cut across, including performance improvement, latency reduction, and scalability of autonomous cars, healthcare systems, and industrial systems. This has been achieved by discussing the different scheduling models including static, dynamic, and hybrid, and their scope of application and challenges in highly dynamic and resource-limited conditions. Other methodologies such as those based on reinforcement learning (RL), which encompasses adaptive scheduling, are also discussed for their robustness in task completion, energy consumption, and system throughput. Furthermore, AI– and ML–powered scheduling systems have also been incorporated into the explanation with regard to how they can change the industry in real time. Studies on the application of RL in scheduling autonomous vehicles and smart grids are illustrative of the impact of these algorithms in real-life applications. Scheduling algorithms are becoming more important in keeping pace with the requirements of intelligent, connected systems and best practices in advanced smart technologies.