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SPSC:流处理框架Atop无服务器计算用于工业大数据
IEEE transactions on cybernetics
|June 21, 2024
概括
我们介绍SPSC,一个新的无服务器流计算框架. 通过使用无状态Lambda函数,SPSC提高了大数据处理效率,优于Apache Flink等传统系统.
科学领域:
- 计算机科学 计算机科学
- 数据工程数据工程
- 分布式系统 分布式系统
背景情况:
- 来自智能制造和信息技术的数据量不断增加,需要高效的处理.
- 现有的分布式流处理系统 (例如,Apache Flink,Spark) 面临着资源利用不足和高延迟的挑战.
- 大数据应用需要可扩展和高性能解决方案.
研究的目的:
- 提出SPSC,一个基于无服务器的流计算框架,旨在解决当前大数据处理系统的局限性.
- 在流处理中实现高效的任务和数据并行性.
- 评估SPSC框架的表现.
主要方法:
- 开发了SPSC,这是一个无服务器框架,使用原子流和无状态Lambda函数作为操作符.
- 在使用AWS Lambda,AWS简单队列服务 (SQS) 和AWS DynamoDB.DB的Amazon Web Services (AWS) 上实现了一个原型.
- 将事件分离为原子流,并使用与上下文无关的Lambda函数进行并行处理.
主要成果:
- 与阿里巴巴的实时计算Flink版本相比,SPSC框架表现出更高的性能.
- 在相似的开销中,SPSC实现了10.12%的业绩改善.
- 无服务器方法促进了固有的数据并行性和任务并行性.
结论:
- 在智能制造和相关领域,SPSC为大数据流处理提供了高效和高性能的替代方案.
- 无服务器架构可以有效地解决大数据场景中资源利用不足和延迟的挑战.
- 拟议的框架显示了实时数据处理应用的巨大潜力.
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