Scheduling Method

Scheduling methods optimize the allocation of resources to tasks, aiming to minimize completion times, energy consumption, and maximize resource utilization. Current research emphasizes adapting scheduling to dynamic environments, using techniques like deep reinforcement learning (DRL) and multi-agent systems to handle uncertainty and improve efficiency in diverse applications such as 5G networks, fog computing, and robotic systems. These advancements are crucial for improving performance in resource-constrained systems and enabling real-time responsiveness in applications ranging from video streaming to complex industrial processes. Furthermore, research explores the trade-offs between fairness and efficiency in scheduling algorithms, seeking to develop methods that achieve both goals.