# Transformer-Based Model for Cold Start Mitigation in FaaS Architecture

arXiv: https://arxiv.org/abs/2504.11338 · HTML: https://mlsanity.com/paper/2504.11338/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider](https://mlsanity.com/paper/2003.03423/) (2020)
- [Cold Start Latency in Serverless Computing: A Systematic Review, Taxonomy, and Future Directions](https://mlsanity.com/paper/2310.08437/) (2023)
- [A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless Functions](https://mlsanity.com/paper/2308.05937/) (2023)
- [FaaSLight: General Application-Level Cold-Start Latency Optimization for Function-as-a-Service in Serverless Computing](https://mlsanity.com/paper/2207.08175/) (2022)
- [A Time Series Forecasting Approach to Minimize Cold Start Time in Cloud-Serverless Platform](https://mlsanity.com/paper/2206.15176/) (2022)
- [A Deep Reinforcement Learning based Algorithm for Time and Cost Optimized Scaling of Serverless Applications](https://mlsanity.com/paper/2308.11209/) (2023)
- [Benchmarking Parallelism in FaaS Platforms](https://mlsanity.com/paper/2010.15032/) (2020)
- [Managing Cold-start in The Serverless Cloud with Temporal Convolutional Networks](https://mlsanity.com/paper/2304.00396/) (2023)

## Abstract (arXiv)

Serverless architectures, particularly the Function as a Service (FaaS) model, have become a cornerstone of modern cloud computing due to their ability to simplify resource management and enhance application deployment agility. However, a significant challenge remains: the cold start problem. This phenomenon occurs when an idle FaaS function is invoked, requiring a full initialization process, which increases latency and degrades user experience. Existing solutions for cold start mitigation are limited in terms of invocation pattern generalization and implementation complexity. In this study, we propose an innovative approach leveraging Transformer models to mitigate the impact of cold starts in FaaS architectures. Our solution excels in accurately modeling function initialization delays and optimizing serverless system performance. Experimental evaluation using a public dataset provided by Azure demonstrates a significant reduction in cold start times, reaching up to 79% compared to conventional methods.