一个基于预测资源需求和应对预测失败的自动缩放系统
1Department of Computer Science and Engineering, Dongguk University, Seoul 04620, Republic of Korea.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
本研究介绍了实时数据处理的动态资源配置框架,提高了99%的自动扩展性能,同时最大限度地降低了边缘计算应用的计算开销.
科学领域:
- 计算机科学 计算机科学
- 云计算 云计算 云计算 云计算
- 边缘计算 边缘计算
背景情况:
- 边缘计算和传感器技术正在彻底改变实时数据处理.
- 数据采集包括收集感官信息 (图像,视频) 并将其传输到云端进行分析.
- 积极的资源配置对于处理不同数据量和实时请求频率至关重要.
研究的目的:
- 建议在云基础设施中为实时数据处理提供动态资源配置框架.
- 为了应对与纯粹预测性资源配置相关的系统故障风险.
- 在边缘计算环境中提高自动扩展算法的性能和效率.
主要方法:
- 开发了一个框架,用于定期监测资源需求的算法.
- 实现资源配置的动态调整,使其与实际需求相匹配.
- 使用Bitbrains数据集进行实验,具有特定的网络吞吐量和值设置.
主要成果:
- 拟议的系统在自动扩展中实现了99%的性能改进.
- 与预测模型相比,该系统仅增加了0.43毫秒的额外计算开销.
- 在实验条件下进行实时数据处理的有效资源管理.
结论:
- 动态资源配置为实时数据处理提供了一个比纯粹预测模型更强大的解决方案.
- 拟议的框架提高了边缘计算的自动扩展性能和效率.
- 这种方法通过适应实际的资源需求来减轻系统故障的风险.
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