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Abstract

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.

Item Type:	Article
Authors/Creators:	Barika, MS and Garg, SK and Zomaya, AY and Ranjan, R
Keywords:	Internet of Things, IoT, stream workflow, dynamic scheduling, alpha-beta pruning, GA with random immigrants, cloud environments, cloud computing, big data
Journal or Publication Title:	IEEE Transactions on Parallel and Distributed Systems
Publisher:	Ieee Computer Soc
ISSN:	1045-9219
DOI / ID Number:	https://doi.org/10.1109/TPDS.2021.3059480
Copyright Information:	2021 IEEE.
Related URLs:	Google Scholar: Garg, SK UTAS Profile: Garg, SK
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