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Data-centric task scheduling algorithm for hybrid tasks in cloud data centers
conference contribution
posted on 2018-01-01, 00:00 authored by X Li, L Wang, Jemal AbawajyJemal Abawajy, X QinWith the development of big data, a demand for data analysis keeps increasing. This requirement has prompted a need for data-aware task scheduling approach that can simultaneously schedule various tasks such as batched tasks and real-time tasks in a data center efficiently. To this end, we propose a hybrid task scheduling strategy coupled with data migration in data center. Firstly, we translate the task scheduling problem into task selection problem, and give methods of selecting batched tasks and real-time tasks respectively. Then the method for scheduling both batched tasks and real-time tasks is introduced in detail. Finally, we integrate data migration into the hybrid scheduling strategy. Experimental results show that, compared to the traditional FIFO algorithm, the proposed task scheduling strategy greatly improves the data locality and data migration performs very well on reducing the job execution time. Our algorithm also guarantees an acceptable fairness for tasks.
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Event
Algorithms and Architectures for Parallel Processing. Conference (2018 : Guangzhou, China)Volume
11335Series
Algorithms and Architectures for Parallel Processing ConferencePagination
630 - 644Publisher
SpringerLocation
Guangzhou, ChinaPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2018-11-15End date
2018-11-17ISSN
0302-9743eISSN
1611-3349ISBN-13
9783030050535Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2018, Springer Nature Switzerland AGEditor/Contributor(s)
J Vaidya, J LiTitle of proceedings
ICA3PP 2018 : Proceedings of the International Conference on Algorithms and Architectures for Parallel ProcessingUsage metrics
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