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Multiple DAGs Workflow Scheduling Algorithm Based on Reinforcement Learning in Cloud Computing

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Book cover Computational Intelligence and Intelligent Systems (ISICA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 575))

Abstract

To the problem of scheduling multiple DAG workflow applications with multiple priorities submitted at different times in cloud computing environment, a novel workflow scheduling algorithm based on reinforcement learning is proposed in this paper. In the workflow scheduling scheme, the number of VMs in resources pool is defined as state space; the runtime of user task is defined as immediate reward, and then interactive with cloud computing environment to obtain the optimization policy. We use real cloud workflow to test the proposed scheme. Experiment results show the proposed scheme not only can solve the fairness of scheduling multiple DAGs with the same priority level submitted at different times, but also can ensure that the execution of the DAGs with higher priorities cannot be influenced by the DAGs with lower priorities. More importantly, the proposed scheme can reasonably schedule multiple DAGs with multiple priorities and improve utilization rate of resources better.

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References

  1. Workflow Management Coalition. http://www.wfmc.org/

  2. Wikipedia. https://en.wikipedia.org

  3. Tim, D., Ernst, J., Bernd, F.: On-demand resource provisioning for BPEL workflows using amazon’s elastic compute cloud. In: 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, pp. 140–147. IEEE Press, New York (2009)

    Google Scholar 

  4. Eun, K.B., Yang, S.K.: Cost optimized provisioning of elastic resources for application workflows. Future Gener. Comput. Syst. 27(8), 1011–1026 (2011)

    Article  Google Scholar 

  5. Guozhong, T., Yu, J., He, J.S.: Towards critical region reliability support for grid workflows. J. Parallel Distrib. Comput. 69(12), 989–995 (2009)

    Article  Google Scholar 

  6. Guozhong, T., Yu, J., He, J.S.: Scheduling and fair cost-optimizing methods for concurrent multiple DAGs with deadline sharing resources. Chin. J. Comput. 37(7), 1607–1619 (2014)

    Google Scholar 

  7. Topcuoglu, H., Hariri, S., Min, W.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002)

    Article  Google Scholar 

  8. Ming, T., Shoubin, D., Liping, Z.: A multi-strategy collaborative prediction model for the runtime of online tasks in computing cluster/grid. Cluster Comput. 14(2), 199–210 (2011)

    Article  Google Scholar 

  9. Ming, T., Shoubin, D., Kejing, H.: A new replication scheduling strategy for grid workflow applications. In: 6th ChinaGrid Annual Conference (ChinaGrid 2011), pp. 74–80 (2011)

    Google Scholar 

  10. Jin, L., Qian, W., Cong, W., Ning, C., Kui, R., Wenjing, L.: Fuzzy keyword search over encrypted data in cloud computing. In: 29th IEEE International Conference on Computer Communications (INFOCOM 2010), pp. 441–445. IEEE Press (2010)

    Google Scholar 

  11. Li, J., Chen, X., Li, J., Jia, C., Ma, J., Lou, W.: Fine-grained access control system based on outsourced attribute-based encryption. In: Crampton, J., Jajodia, S., Mayes, K. (eds.) ESORICS 2013. LNCS, vol. 8134, pp. 592–609. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  12. Ullman, J.D.: NP-complete scheduling problems. J. Comput. Syst. Sci. 10(3), 384–393 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  13. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)

    Google Scholar 

  14. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Experience 41(1), 23–50 (2011)

    Article  Google Scholar 

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Acknowledgments

The work presented in this paper was supported by National Natural Science Foundation of China (No. 61272382, 61402183). Key project of Guangdong Province in the research center of cloud robot (petrochemical) Engineering Technology (No. 650007). Guangdong Provincial Science and Technology Program (No. 2014A020208139); Distinguished Young Talents in Higher Education of Guangdong (No. 2013LYM-0057). 2014 Guangdong Provincial Technological Innovation Program (No. 650019). Wende Ke is corresponding author.

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Authors and Affiliations

  1. College of Computer and Electronic Information, Guangdong University of Petrochemical Technology, Maoming, China

    Delong Cui, Wende Ke, Zhiping Peng & Jinglong Zuo

Authors
  1. Delong Cui
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  2. Wende Ke
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  3. Zhiping Peng
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  4. Jinglong Zuo
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Corresponding author

Correspondence to Wende Ke .

Editor information

Editors and Affiliations

  1. College of Mathematics and Informatics, The South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. School of Computer Science, Guangzhou University, Guangzhou, China

    Jin Li

  3. School of Computer Science and Engineeri, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

  4. Dept. of Informatics, University of Salerno, Fisciano, Italy

    Aniello Castiglione

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Cui, D., Ke, W., Peng, Z., Zuo, J. (2016). Multiple DAGs Workflow Scheduling Algorithm Based on Reinforcement Learning in Cloud Computing. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_31

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  • DOI: https://doi.org/10.1007/978-981-10-0356-1_31

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0355-4

  • Online ISBN: 978-981-10-0356-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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