Stream processing is an emerging paradigm to handle data streams upon arrival, powering latency-critical application such as fraud detection, algorithmic trading, and health surveillance. Though there are a variety of Distributed Stream Processing Systems (DSPSs) that facilitate the development of streaming applications, resource management and task scheduling is not automatically handled by the DSPS middleware and requires a laborious process to tune toward specific deployment targets. As the advent of cloud computing has supported renting resources on-demand, it is of great interest to review the research progress of hosting streaming systems in clouds under certain Service Level Agreements (SLA) and cost constraints. In this article, we introduce the hierarchical structure of streaming systems, define the scope of the resource management problem, and present a comprehensive taxonomy in this context covering critical research topics such as resource provisioning, operator parallelisation, and task scheduling. The literature is then reviewed following the taxonomy structure, facilitating a deeper understanding of the research landscape through classification and comparison of existing works. Finally, we discuss the open issues and future research directions toward realising an automatic, SLA-aware resource management framework.

中文翻译：

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分布式流处理系统中的资源管理和调度

流处理是一种新兴的范式，用于处理到达时的数据流，为欺诈检测、算法交易和健康监测等延迟关键型应用程序提供动力。尽管有多种分布式流处理系统 (DSPS) 可以促进流应用程序的开发，但资源管理和任务调度并不是由 DSPS 中间件自动处理的，并且需要一个费力的过程来调整特定的部署目标。由于云计算的出现支持按需租用资源，因此回顾在特定服务水平协议 (SLA) 和成本限制下在云中托管流媒体系统的研究进展非常有意义。在本文中，我们介绍了流系统的层次结构，定义资源管理问题的范围，并在此背景下提出一个全面的分类法，涵盖关键研究主题，如资源供应、操作员并行化和任务调度。然后按照分类结构对文献进行回顾，通过对现有作品的分类和比较，促进对研究前景的更深入了解。最后，我们讨论了实现自动、SLA 感知资源管理框架的开放问题和未来研究方向。通过对现有作品的分类和比较，促进对研究领域的更深入了解。最后，我们讨论了实现自动、SLA 感知资源管理框架的开放问题和未来研究方向。通过对现有作品的分类和比较，促进对研究领域的更深入了解。最后，我们讨论了实现自动、SLA 感知资源管理框架的开放问题和未来研究方向。