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Profit-aware distributed online scheduling for data-oriented tasks in cloud datacenters

Lu, Wei, Lu, Ping, Sun, Quanying, Yu, Shui and Zhu, Zuqing 2018, Profit-aware distributed online scheduling for data-oriented tasks in cloud datacenters, IEEE Access, vol. 6, pp. 15629-15642, doi: 10.1109/ACCESS.2018.2808481.

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Title Profit-aware distributed online scheduling for data-oriented tasks in cloud datacenters
Author(s) Lu, Wei
Lu, Ping
Sun, Quanying
Yu, ShuiORCID iD for Yu, Shui orcid.org/0000-0003-4485-6743
Zhu, Zuqing
Journal name IEEE Access
Volume number 6
Start page 15629
End page 15642
Total pages 14
Publisher IEEE Access
Place of publication Piscataway, N.J.
Publication date 2018-02-21
ISSN 2169-3536
Keyword(s) Science & Technology
Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
Computer Science
Engineering
Datacenter networks
Lyapunov optimization
distributed online scheduling
data-transfer acceleration
ELASTIC OPTICAL NETWORKS
MANAGEMENT
MULTICAST
EONS
Summary As there is an increasing trend to deploy geographically distributed (geo-distributed) cloud datacenters (DCs), the scheduling of data-oriented tasks in such cloud DC systems becomes an appealing research topic. Specifically, it is challenging to achieve the distributed online scheduling that can handle the tasks' acceptance, data-transfers, and processing jointly and efficiently. In this paper, by considering the store-and-forward and anycast schemes, we formulate an optimization problem to maximize the time-average profit from serving data-oriented tasks in a cloud DC system and then leverage the Lyapunov optimization techniques to propose an efficient scheduling algorithm, i.e., GlobalAny. We also extend the proposed algorithm by designing a data-transfer acceleration scheme to reduce the data-transfer latency. Extensive simulations verify that our algorithms can maximize the time-average profit in a distributed online manner. The results also indicate that GlobalAny and GlobalAnyExt (i.e., GlobalAny with data-transfer acceleration) outperform several existing algorithms in terms of both time-average profit and computation time.
Language eng
DOI 10.1109/ACCESS.2018.2808481
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2018, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30106993

Document type: Journal Article
Collections: School of Information Technology
Open Access Collection
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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.