Deep reinforcement learning-based scheduling in distributed systems: a critical review

Many fields of research use parallelized and distributed computing environments, including astronomy, earth science, and bioinformatics. Due to an increase in client requests, service providers face various challenges, such as task scheduling, security, resource management, and virtual machine migration. NP-hard scheduling problems require a long time to implement an optimal or suboptimal solution due to their large solution space. With recent advances in artificial intelligence, deep reinforcement learning (DRL) can be used to solve scheduling problems. The DRL approach combines the strength of deep learning and neural networks with reinforcement learning's feedback-based learning. This paper provides a comprehensive overview of DRL-based scheduling algorithms in distributed systems by categorizing algorithms and applications. As a result, several articles are assessed based on their main objectives, quality of service and scheduling parameters, as well as evaluation environments (i.e., simulation tools, real-world environment). The literature review indicates that algorithms based on RL, such as Q-learning, are effective for learning scaling and scheduling policies in a cloud environment. Additionally, the challenges and directions for further research on deep reinforcement learning to address scheduling problems were summarized (e.g., edge intelligence, ideal dynamic task scheduling framework, human-machine interaction, resource-hungry artificial intelligence (AI) and sustainability).