火脸:利用内部功能功能在无服务器边缘平台上的功能配置
Ming Li1,2,3, Jianshan Zhang4, Jingfeng Lin1,2,3
1College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
通过预测函数执行时间和使用APSO-GA来选择具有成本效益的资源配置,FireFace优化了无服务器边缘计算,降低了高达44.8%的开支. 这种适应性方法尽量减少财务开销,同时实现服务水平目标 (SLO).
科学领域:
- 计算机科学 计算机科学
- 分布式计算 (Distributed Computing) 是一种分布式计算.
- 云计算 云计算 云计算
背景情况:
- 无服务器计算是一种流行的云应用程序部署模型,抽象基础设施管理.
- 现有的无服务器资源配置方法依赖于历史数据或插值,这对于边缘平台来说是低效的.
- 无服务器边缘平台面临的挑战是资源异质性和更高的开销,增加了开发人员的成本.
研究的目的:
- 提出一种自适应和高效的方法,FireFace,用于优化无服务器边缘计算资源配置.
- 尽量减少开发人员的财务开销,同时确保服务水平目标 (SLO) 得到满足.
- 解决无服务器边缘平台中资源异质性和动态环境的挑战.
主要方法:
- 开发了一个预测模块,根据内部功能特征和配置方案预测功能执行时间.
- 实现了一个使用自适应粒子集群优化和遗传算法操作员 (APSO-GA) 算法的决策模块.
- 决策模块分析环境信息,为CPU,内存和边缘平台选择最佳配置.
主要成果:
- 预测模型在所有指标上取得了最佳结果,实际无服务器应用的预测错误率为4.25%9.51%.
- 与经典算法相比,FireFace通过找到最佳的资源配置,实现了7.2%44.8%的平均成本节约.
- 该方法表现出快速的适应性,有效地调整动态环境中的资源分配.
结论:
- 在满足SLO的同时,FireFace有效地将无服务器边缘计算的财务开销降到最低.
- 拟议的方法比现有解决方案提供了显著的成本节约和提高效率.
- 对于无服务器边缘资源管理的复杂性,FireFace提供了一个强大的,可适应的解决方案.
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