A market-oriented hierarchical scheduling strategy in cloud workflow systems

Wu, Zhangjun, Liu, Xiao, Ni, Zhiwei, Yuan, Dong and Yang, Yun 2013, A market-oriented hierarchical scheduling strategy in cloud workflow systems, Journal of supercomputing, vol. 63, no. 1, pp. 256-293, doi: 10.1007/s11227-011-0578-4.

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Title A market-oriented hierarchical scheduling strategy in cloud workflow systems
Author(s) Wu, Zhangjun
Liu, XiaoORCID iD for Liu, Xiao orcid.org/0000-0001-8400-5754
Ni, Zhiwei
Yuan, Dong
Yang, Yun
Journal name Journal of supercomputing
Volume number 63
Issue number 1
Start page 256
End page 293
Total pages 38
Publisher Springer
Place of publication New York, N.Y.
Publication date 2013-01
ISSN 0920-8542
1573-0484
Keyword(s) Cloud workflow system
Cloud computing
Workflow scheduling
Hierarchical scheduling
Metaheuristics
Science & Technology
Technology
Computer Science, Hardware & Architecture
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Computer Science
Engineering
OPTIMIZATION APPROACH
GENETIC-ALGORITHM
CONSTRAINTS
SWINDEW
GRIDS
Summary A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. One of the most important aspects which differentiate a cloud workflow system from its other counterparts is the market-oriented business model. This is a significant innovation which brings many challenges to conventional workflow scheduling strategies. To investigate such an issue, this paper proposes a market-oriented hierarchical scheduling strategy in cloud workflow systems. Specifically, the service-level scheduling deals with the Task-to-Service assignment where tasks of individual workflow instances are mapped to cloud services in the global cloud markets based on their functional and non-functional QoS requirements; the task-level scheduling deals with the optimisation of the Task-to-VM (virtual machine) assignment in local cloud data centres where the overall running cost of cloud workflow systems will be minimised given the satisfaction of QoS constraints for individual tasks. Based on our hierarchical scheduling strategy, a package based random scheduling algorithm is presented as the candidate service-level scheduling algorithm and three representative metaheuristic based scheduling algorithms including genetic algorithm (GA), ant colony optimisation (ACO), and particle swarm optimisation (PSO) are adapted, implemented and analysed as the candidate task-level scheduling algorithms. The hierarchical scheduling strategy is being implemented in our SwinDeW-C cloud workflow system and demonstrating satisfactory performance. Meanwhile, the experimental results show that the overall performance of ACO based scheduling algorithm is better than others on three basic measurements: the optimisation rate on makespan, the optimisation rate on cost and the CPU time.
Language eng
DOI 10.1007/s11227-011-0578-4
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2011, Springer Science+Business Media, LLC
Persistent URL http://hdl.handle.net/10536/DRO/DU:30087720

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