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Data-intensive task scheduling for heterogeneous big data analytics in IoT system

Li, Xin, Wang, Liangyuan, Abawajy, Jemal H, Qin, Xiaolin, Pau, Giovanni and You, Ilsun 2020, Data-intensive task scheduling for heterogeneous big data analytics in IoT system, Energies, vol. 13, no. 17, pp. 1-14, doi: 10.3390/en13174508.

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Title Data-intensive task scheduling for heterogeneous big data analytics in IoT system
Author(s) Li, Xin
Wang, Liangyuan
Abawajy, Jemal HORCID iD for Abawajy, Jemal H orcid.org/0000-0001-8962-1222
Qin, Xiaolin
Pau, Giovanni
You, Ilsun
Journal name Energies
Volume number 13
Issue number 17
Article ID 4508
Start page 1
End page 14
Total pages 14
Publisher Molecular Diversity Preservation International
Place of publication Basel, Switzerland
Publication date 2020-09-01
ISSN 1996-1073
Keyword(s) big data analysis
heterogeneous data-intensive task
IoT system
service response delay
task scheduling
Summary Efficient big data analysis is critical to support applications or services in Internet of Things (IoT) system, especially for the time-intensive services. Hence, the data center may host heterogeneous big data analysis tasks for multiple IoT systems. It is a challenging problem since the data centers usually need to schedule a large number of periodic or online tasks in a short time. In this paper, we investigate the heterogeneous task scheduling problem to reduce the global task execution time, which is also an efficient method to reduce energy consumption for data centers. We establish the task execution for heterogeneous tasks respectively based on the data locality feature, which also indicate the relationship among the tasks, data blocks and servers. We propose a heterogeneous task scheduling algorithm with data migration. The core idea of the algorithm is to maximize the efficiency by comparing the cost between remote task execution and data migration, which could improve the data locality and reduce task execution time. We conduct extensive simulations and the experimental results show that our algorithm has better performance than the traditional methods, and data migration actually works to reduce th overall task execution time. The algorithm also shows acceptable fairness for the heterogeneous tasks.
Language eng
DOI 10.3390/en13174508
Indigenous content off
Field of Research 02 Physical Sciences
09 Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30142363

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.