  In the rapidly evolving field of serverless computing, efficient function
scheduling and resource scaling are critical for optimizing performance and
cost. This paper presents a comprehensive review of the application of Deep
Reinforcement Learning (DRL) techniques in these areas. We begin by providing
an overview of serverless computing, highlighting its benefits and challenges,
with a particular focus on function scheduling and resource scaling. We then
delve into the principles of deep reinforcement learning (DRL) and its
potential for addressing these challenges. A systematic review of recent
studies applying DRL to serverless computing is presented, covering various
algorithms, models, and performances. Our analysis reveals that DRL, with its
ability to learn and adapt from an environment, shows promising results in
improving the efficiency of function scheduling and resource scaling in
serverless computing. However, several challenges remain, including the need
for more realistic simulation environments, handling of cold starts, and the
trade-off between learning time and scheduling performance. We conclude by
discussing potential future directions for this research area, emphasizing the
need for more robust DRL models, better benchmarking methods, and the
exploration of multi-agent reinforcement learning for more complex serverless
architectures. This review serves as a valuable resource for researchers and
practitioners aiming to understand and advance the application of DRL in
serverless computing.

Distributed, Parallel, and Cluster Computing;Artificial Intelligence;Machine Learning