Online Scheduling Technique To Handle Data Velocity Changes in Stream Workflows
Online Scheduling Technique To Handle Data Velocity Changes in Stream Workflows
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DOI：
10.1109/tpds.2021.3059480
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发表时间：
2021-08
影响因子：
5.3
通讯作者：
M. Barika;S. Garg;Albert Y. Zomaya;R. Ranjan
中科院分区：
文献类型：
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作者：
M. Barika;S. Garg;Albert Y. Zomaya;R. Ranjan
许多物联网应用和服务，如智能停车和智能交通控制，都包含一个由不同分析组件组成的网络，这些组件以工作流的形式组成，以做出更好的决策。这些工作流也称为流工作流。现有的研究工作主要集中在流操作图上，它不同于流工作流应用，因为它涉及异构性、多数据源和多输出。考虑到流工作流的复杂性和动态性，在部署时满足实时数据分析需求并不是全部，因为数据的速度会随着时间的推移而变化。此更改是在此应用程序执行期间频繁发生的流工作流的最动态形式。在这篇文章中，我们提出了一种新的动态调度技术，随着时间的推移管理云资源，以处理流工作流中的数据速度变化，同时保持用户定义的实时数据分析要求，并最大限度地减少执行成本。该技术的效率进行了评估，实验结果表明，该技术优于其竞争对手，是接近下限。
Many IoT applications and services such as smart parking and smart traffic control contain a network of different analytical components, which are composed in the form of a workflow to make better decisions. These workflows are also known as stream workflows. The focus of existing research works is on the streaming operator graph, which differs from stream workflow application as it involves heterogeneity, multiple data sources and multiple outputs. Considering the complexity and dynamism of stream workflow, meeting real-time data analysis requirements at deployment time is not the whole story as the velocity of data changes over time. This change is the most dynamic form of stream workflow that occurs frequently during the execution of this application. In this article, we propose a new dynamic scheduling technique that manages cloud resources over time to handle data velocity changes in stream workflow while maintaining user-defined real-time data analysis requirements and minimising execution cost. The efficiency of the proposed technique is evaluated, and experimental results showed that this technique outperformed its competitors and is close to the lower bound.