在无服务器计算中,异质节点之间的延迟敏感函数配置
Urooba Shahid1,2, Ghufran Ahmed1, Shahbaz Siddiqui1
1Department of Computer Science, National University of Computer and Emerging Sciences, Karachi 75030, Pakistan.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
功能即服务 (FaaS) 提供了可适应的智能城市解决方案. 本研究介绍了一种自适应机器学习模型,以优化 FaaS 的配置,提高资源利用率和最后期限的遵守.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 智慧城市技术 智慧城市技术
背景情况:
- 功能即服务 (FaaS) 提供无服务器计算,简化了开发人员的基础设施管理.
- 与物联网 (IoT) 集成的FaaS可以实现事件驱动的行动和实时计算,这对智能城市至关重要.
- 在FaaS中优化功能配置对于满足性能要求至关重要,例如截止日期和高效的资源使用.
研究的目的:
- 开发和评估基于概率的自适应机器学习模型,以实现最佳的 FaaS 功能配置.
- 为了提高资源利用率,并确保在智慧城市背景下在 FaaS 部署中遵守截止日期.
- 解决分布式 FaaS 环境中网络延迟和计算需求的挑战.
主要方法:
- 采用了自适应机器学习模型,使用XGBoost回归器来估计执行时间,并使用决策树回归器来预测网络延迟.
- 将网络延迟,到达计算和资源重视等因素纳入机器学习模型,用于放置决策.
- 利用Docker容器进行复制,专注于无服务器节点类型,函数位置,截止日期和边缘云拓.
主要成果:
- 拟议的机器学习模型有效地帮助选择FaaS函数的最佳配置.
- 有效的资源利用被证明与加强的截止日期遵守直接相关.
- 该研究证实了FaaS在智能城市基础设施中的好处,通过优化放置策略.
结论:
- 适应性机器学习模型为优化智能城市中的 FaaS 功能配置提供了有效的解决方案.
- 通过智能 FaaS 部署策略,可以实现高效的资源管理和遵守严格的截止日期.
- 这项研究有助于无服务器计算的进步,用于可扩展和响应的智能城市应用程序.
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