# Cold Start Latency in Serverless Computing: A Systematic Review, Taxonomy, and Future Directions

arXiv: https://arxiv.org/abs/2310.08437 · HTML: https://mlsanity.com/paper/2310.08437/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2023.

## Built on by (most cited first, 6 of 6)

- [Enabling Efficient Serverless Inference Serving for LLM (Large Language Model) in the Cloud](https://mlsanity.com/paper/2411.15664/) (2024)
- [Transformer-Based Model for Cold Start Mitigation in FaaS Architecture](https://mlsanity.com/paper/2504.11338/) (2025)
- [High-Performance Serverless Computing: A Systematic Literature Review on Serverless for HPC, AI, and Big Data](https://mlsanity.com/paper/2601.09334/) (2026)
- [Serverless AI Security: Attack Surface Analysis and Runtime Protection Mechanisms for FaaS-Based Machine Learning](https://mlsanity.com/paper/2601.11664/) (2026)
- [Learning to Remember: Attentive Reinforcement Learning for Edge Serverless Autoscaling](https://mlsanity.com/paper/2603.28790/) (2026)
- [Breaking the Ice: Analyzing Cold Start Latency in vLLM](https://mlsanity.com/paper/2606.07362/) (2026)

## Abstract (arXiv)

Recently, academics and the corporate sector have paid attention to serverless computing, which enables dynamic scalability and an economic model. In serverless computing, users only pay for the time they actually use resources, enabling zero scaling to optimise cost and resource utilisation. However, this approach also introduces the serverless cold start problem. Researchers have developed various solutions to address the cold start problem, yet it remains an unresolved research area. In this article, we propose a systematic literature review on clod start latency in serverless computing. Furthermore, we create a detailed taxonomy of approaches to cold start latency, which we use to investigate existing techniques for reducing the cold start time and frequency. We have classified the current studies on cold start latency into several categories such as caching and application-level optimisation-based solutions, as well as Artificial Intelligence (AI)/Machine Learning (ML)-based solutions. Moreover, we have analyzed the impact of cold start latency on quality of service, explored current cold start latency mitigation methods, datasets, and implementation platforms, and classified them into categories based on their common characteristics and features. Finally, we outline the open challenges and highlight the possible future directions.