Task scheduling and load balancing in cloud, fog, and edge computing systems: a survey
A structured taxonomy is presented that classifies algorithms into traditional, heuristic, meta-heuristic, and modern learning-based approaches, with a particular emphasis on the increasing adoption of Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) for dynamic and adaptive scheduling.