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Online Scheduling Technique To Handle Data Velocity Changes in Stream Workflows

Lookup NU author(s): Professor Raj Ranjan

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Abstract

IEEEMany 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 paper, 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.


Publication metadata

Author(s): Barika M, Garg S, Zomaya A, Ranjan R

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Parallel and Distributed Systems

Year: 2021

Volume: 32

Issue: 8

Pages: 2115-2130

Print publication date: 01/08/2021

Online publication date: 16/02/2021

Acceptance date: 06/02/2021

Date deposited: 17/02/2021

ISSN (print): 1045-9219

ISSN (electronic): 1558-2183

Publisher: IEEE Computer Society

URL: https://doi.org/10.1109/TPDS.2021.3059480

DOI: 10.1109/TPDS.2021.3059480


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