A Review of Deep Reinforcement Learning in Serverless Computing: Function Scheduling and Resource Auto-Scaling

• URL: http://arxiv.org/abs/2311.12839v1
• Date: Thu, 5 Oct 2023 09:26:04 GMT
• Title: A Review of Deep Reinforcement Learning in Serverless Computing: Function Scheduling and Resource Auto-Scaling
• Authors: Amjad Yousef Majid, Eduard Marin
• Abstract summary: This paper presents a comprehensive review of the application of Deep Reinforcement Learning (DRL) techniques in serverless computing. 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.
• Score: 2.0722667822370386
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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