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Cost-efficient reactive scheduling for real-time workflows in clouds

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Abstract

Workflow comprising of many tasks and data dependencies among tasks is an attractive programming paradigm for processing big data in clouds, and workflow scheduling plays essential roles in improving the cost and resource efficiency for cloud platforms. Up to now, large numbers of scheduling approaches have been proposed and improved. However, the majority of them focused on scheduling a single workflow and have not adequately exploited the idle time slots on resources to reduce the cost for executing workflow applications. To cover the above issue, we suggest to schedule tasks from different workflows in a hybrid way to take full advantage of idle time slots to improve the cost and resource efficiency, while guaranteeing the deadlines of workflows. To achieve the above idea, we first introduce a reactive scheduling architecture for real-time workflows. Then, a novel cost-efficient reactive scheduling algorithm (CERSA) is proposed to deploy multiple workflows with deadlines to cloud platforms. Finally, on the basis of real-world workflow traces, extensive experiments are conducted to compare CERSA with five existing algorithms. The experimental results demonstrate that CERSA is better than those algorithms with respect to monetary cost and resource efficiency.

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Notes

  1. https://github.com/HuangkeChen/Uncertainty-Aware-Workflow-Scheduling.

  2. https://confluence.pegasus.isi.edu/display/pegasus/WorkflowGenerator.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China under Grants (No. 61572511 and 61603404) and the Scientific Research Project of National University of Defense Technology under Grants (No. ZK16-03-09 and ZK16-03-30).

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Authors and Affiliations

  1. College of Systems Engineering, National University of Defense Technology, Changsha, 410073, Hunan, People’s Republic of China

    Huangke Chen, Jianghan Zhu & Lisu Huo

  2. School of Traffic and Transportation Engineering, Central South University, Changsha, 410073, Hunan, People’s Republic of China

    Guohua Wu

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  1. Huangke Chen
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  2. Jianghan Zhu
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  3. Guohua Wu
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  4. Lisu Huo
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Correspondence to Guohua Wu.

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Chen, H., Zhu, J., Wu, G. et al. Cost-efficient reactive scheduling for real-time workflows in clouds. J Supercomput 74, 6291–6309 (2018). https://doi.org/10.1007/s11227-018-2561-9

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