Data Flow Dependent Component Placement of Data Processing Cloud Applications

Michael Zimmermann, Uwe Breitenbücher, Kálmán Képes, F. Leymann, Benjamin Weder
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引用次数: 2

Abstract

With the ongoing advances in the area of cloud computing, Internet of Things, Industry 4.0, and the increasing prevalence of cyber-physical systems and devices equipped with sensors, the amount of data generated every second is rising steadily. Thereby, the gathering of data and the creation of added value from this data is getting easier and easier. However, the increasing volume of data stored in the cloud leads to new challenges. Analytics software and scalable platforms are required to evaluate the data distributed all over the internet. But with distributed applications and large data sets to be handled, the network becomes a bottleneck. Therefore, in this work, we present an approach to automatically improve the deployment of such applications regarding the placement of data processing components dependent on the data flow of the application. To show the practical feasibility of our approach, we implemented a prototype based on the open-source ecosystem OpenTOSCA. Moreover, we evaluated our prototype using various scenarios.
数据处理云应用程序的数据流相关组件放置
随着云计算、物联网、工业4.0领域的不断发展,以及配备传感器的网络物理系统和设备的日益普及,每秒产生的数据量正在稳步上升。因此,数据的收集和从这些数据中创造附加值变得越来越容易。然而,存储在云中的数据量的增加带来了新的挑战。需要分析软件和可扩展的平台来评估分布在互联网上的数据。但是由于要处理分布式应用程序和大型数据集,网络成为瓶颈。因此,在这项工作中,我们提出了一种方法,可以根据应用程序的数据流来自动改进这些应用程序的部署,并放置数据处理组件。为了展示我们方法的实际可行性,我们实现了一个基于开源生态系统OpenTOSCA的原型。此外,我们使用各种场景评估了我们的原型。
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