Transformer-Based Model for Cold Start Mitigation in FaaS Architecture
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908320643678208 |
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| author | Mouen, Alexandre Savi Fayam Mbala Zeutouo, Jerry Lacmou Tchendji, Vianney Kengne |
| author_facet | Mouen, Alexandre Savi Fayam Mbala Zeutouo, Jerry Lacmou Tchendji, Vianney Kengne |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11338 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transformer-Based Model for Cold Start Mitigation in FaaS Architecture Mouen, Alexandre Savi Fayam Mbala Zeutouo, Jerry Lacmou Tchendji, Vianney Kengne Distributed, Parallel, and Cluster Computing Artificial Intelligence 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. |
| title | Transformer-Based Model for Cold Start Mitigation in FaaS Architecture |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2504.11338 |