Scholarlink

<p>This research presents a serverless architecture cold start optimization solution based on resource serialization. The performance of this technology was evaluated through experiments across multiple cloud platforms, testing different function types and resource configurations. The results demonstrate that resource serialization reduces serverless function cold start time and memory consumption. In web service scenarios, the optimization achieved an 86.78% reduction, decreasing cold start time from 42266.35ms to 5585.97ms. Cross-platform testing shows optimization rates exceeding 53% across different cloud environments. Analysis indicates that this technology’s effectiveness varies by application type: I/O-intensive applications and functions with complex environment configurations showed higher optimization rates compared to compute-intensive tasks, with AI inference functions showing a 25.67% reduction. The combination of this technology with existing optimization strategies, such as Layer implementation, produced additional performance improvements. The research also addresses security considerations and implementation constraints in production environments and provides implementation guidelines. These findings contribute to the optimization of serverless applications and provide implementation reference for cloud service providers and developers.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A generic framework for minimizing cold start times in serverless applications via resource serialization

  • 期刊论文

  • 发表日期：

    2025年8月14日

查看全文
  • Yu Liu,
  • Fu Li,
  • Chenhao Kong

摘要

This research presents a serverless architecture cold start optimization solution based on resource serialization. The performance of this technology was evaluated through experiments across multiple cloud platforms, testing different function types and resource configurations. The results demonstrate that resource serialization reduces serverless function cold start time and memory consumption. In web service scenarios, the optimization achieved an 86.78% reduction, decreasing cold start time from 42266.35ms to 5585.97ms. Cross-platform testing shows optimization rates exceeding 53% across different cloud environments. Analysis indicates that this technology’s effectiveness varies by application type: I/O-intensive applications and functions with complex environment configurations showed higher optimization rates compared to compute-intensive tasks, with AI inference functions showing a 25.67% reduction. The combination of this technology with existing optimization strategies, such as Layer implementation, produced additional performance improvements. The research also addresses security considerations and implementation constraints in production environments and provides implementation guidelines. These findings contribute to the optimization of serverless applications and provide implementation reference for cloud service providers and developers.