# AI-based Resource Allocation: Reinforcement Learning for Adaptive Auto-scaling in Serverless Environments

arXiv: https://arxiv.org/abs/2005.14410 · HTML: https://mlsanity.com/paper/2005.14410/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2020.

## Built on by (most cited first, 5 of 5)

- [A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless Functions](https://mlsanity.com/paper/2308.05937/) (2023)
- [SMLT: A Serverless Framework for Scalable and Adaptive Machine Learning Design and Training](https://mlsanity.com/paper/2205.01853/) (2022)
- [On-demand Cold Start Frequency Reduction with Off-Policy Reinforcement Learning in Serverless Computing](https://mlsanity.com/paper/2308.07541/) (2023)
- [A Review of Deep Reinforcement Learning in Serverless Computing: Function Scheduling and Resource Auto-Scaling](https://mlsanity.com/paper/2311.12839/) (2023)
- [An Auto-Scaling Approach for Serverless Environments Based on a Multi-Expert Consensus Mechanism](https://mlsanity.com/paper/2607.15511/) (2026)

## Abstract (arXiv)

Serverless computing has emerged as a compelling new paradigm of cloud computing models in recent years. It promises the user services at large scale and low cost while eliminating the need for infrastructure management. On cloud provider side, flexible resource management is required to meet fluctuating demand. It can be enabled through automated provisioning and deprovisioning of resources. A common approach among both commercial and open source serverless computing platforms is workload-based auto-scaling, where a designated algorithm scales instances according to the number of incoming requests. In the recently evolving serverless framework Knative a request-based policy is proposed, where the algorithm scales resources by a configured maximum number of requests that can be processed in parallel per instance, the so-called concurrency. As we show in a baseline experiment, this predefined concurrency level can strongly influence the performance of a serverless application. However, identifying the concurrency configuration that yields the highest possible quality of service is a challenging task due to various factors, e.g. varying workload and complex infrastructure characteristics, influencing throughput and latency. While there has been considerable research into intelligent techniques for optimizing auto-scaling for virtual machine provisioning, this topic has not yet been discussed in the area of serverless computing. For this reason, we investigate the applicability of a reinforcement learning approach, which has been proven on dynamic virtual machine provisioning, to request-based auto-scaling in a serverless framework. Our results show that within a limited number of iterations our proposed model learns an effective scaling policy per workload, improving the performance compared to the default auto-scaling configuration.