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A Review of Dynamic Scalability and Dynamic Scheduling in Cloud-Native Distributed Stream Processing Systems

机译:云原生分布式流处理系统中的动态可伸缩性和动态调度的综述

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Scalability is one of the common goals addressed by distributed stream processing systems. Distributed stream processing systems execute streaming applications that are segmented and distributed among several nodes across clusters, in order to cater heavy and growing stream processing use cases. Due to the tremendous benefits provided by cloud infrastructures, distributed stream processing systems are often adapted to be deployed on cloud-native environments. Container orchestrators tend to provide modularity and ease of management for containerised applications. Dynamic scalability and dynamic scheduling of streaming applications become the key points, in addressing the efficiency of cloud-native distributed stream processing systems. In this paper, the author attempts to summarise the researches that have been conducted within the domain of dynamic scaling and dynamic scheduling of stream processing systems, related to cloud-nativeness.
机译:可伸缩性是分布式流处理系统解决的常见目标之一。分布式流处理系统执行流应用程序,这些流应用程序在集群中的多个节点之间进行了分段和分布,以适应繁重且不断增长的流处理用例。由于云基础架构所提供的巨大利益,分布式流处理系统通常适合于部署在云原生环境中。容器编排器倾向于为容器化应用程序提供模块化和易于管理的功能。流应用程序的动态可伸缩性和动态调度成为解决云原生分布式流处理系统效率的关键点。在本文中,作者试图总结在流处理系统的动态缩放和动态调度领域中与云原生相关的研究。

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