Scheduling Framework

Scheduling frameworks aim to optimize resource allocation across diverse systems, balancing competing objectives like performance, fairness, and predictability of task completion times. Current research emphasizes decentralized approaches using agent-based systems and deep reinforcement learning, often incorporating mechanisms to handle uncertainty and dynamic environments, such as those found in cloud, fog, and island energy systems. These advancements improve resource utilization and task success rates in complex, real-world applications, impacting fields ranging from cloud computing and IoT to healthcare and energy management.