用Transformer预测函数冷启动延迟，显著降低云服务响应时间。
Transformer-Based Model for Cold Start Mitigation in FaaS Architecture
- 用Transformer建模函数初始化延迟，捕捉调用模式
- 在Azure数据集上冷启动时间减少79%
- 适合关注Serverless性能优化的研究者
无服务器架构，特别是函数即服务（FaaS）模型，因其简化资源管理并提升应用部署敏捷性，已成为现代云计算的核心。然而，一个关键挑战依然存在：冷启动问题。当空闲的FaaS函数被调用时，需经历完整的初始化过程，导致延迟增加，影响用户体验。现有冷启动缓解方案在调用模式泛化和实现复杂度方面受限。本研究提出一种创新方法，利用Transformer模型缓解FaaS架构中的冷启动影响。该方案能准确建模函数初始化延迟，优化无服务器系统性能。基于Azure提供的公开数据集进行实验评估，冷启动时间相比传统方法最多降低79%。
原文摘要 · Abstract (English)
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.
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