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Towards operational cost minimization for cloud bursting with deadline constraints in hybrid clouds

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Abstract

In hybrid clouds, there is a technique named cloud bursting which can allow companies to expand their capacity to meet the demands of peak workloads in a low-priced manner. In this work, a cost-aware job scheduling approach based on queueing theory in hybrid clouds is proposed. The job scheduling problem in the private cloud is modeled as a queueing model. A genetic algorithm is applied to achieve optimal queues for jobs to improve the utilization rate of the private cloud. Then, the task execution time is predicted by back propagation neural network. The max–min strategy is applied to schedule tasks according to the prediction results in hybrid clouds. Experiments show that our cost-aware job scheduling algorithm can reduce the average job waiting time and average job response time in the private cloud. In additional, our proposed job scheduling algorithm can improve the system throughput of the private cloud. It also can reduce the average task waiting time, average task response time and total costs in hybrid clouds.

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References

  1. Ro, C.: Modeling and analysis of memory virtualization in cloud computing. Clust. Comput. 18(1), 177–185 (2015)

    Article  Google Scholar 

  2. Kaur, T., Chana, I.: Energy aware scheduling of deadline-constrained tasks in cloud computing. Clust. Comput. 19(2), 679–698 (2016)

    Article  Google Scholar 

  3. Kailasam, S., Gnanasambandam, N., Dharanipragada, J., et al.: Optimizing ordered throughput using autonomic cloud bursting schedulers. IEEE Trans. Softw. Eng. 39(11), 1564–1581 (2013)

    Article  Google Scholar 

  4. Guo, T., Sharma, U., Shenoy, P., et al.: Cost-aware cloud bursting for enterprise applications. ACM Trans. Internet Technol. 13(3), 1–24 (2014)

    Article  Google Scholar 

  5. Chopra, N., Singh, S.: Deadline and cost based workflow scheduling in hybrid cloud. In: 2013 2nd International Conference on Advances in Computing, Communications and Informatics, pp. 840–846. IEEE (2013)

  6. TaoBao website. https://www.taobao.com/

  7. Amazon website. https://www.amazon.com/

  8. Toosi, A.N., Sinnott, R.O., Buyya, R.: Resource provisioning for data-intensive applications with deadline constraints on hybrid clouds using Aneka. Future Gener. Comput. Syst. 79, 765–775 (2018)

    Article  Google Scholar 

  9. Loreti, D., Ciampolini, A.: A hybrid cloud infrastructure for big data applications. In: 2015 IEEE 17th International Conference on High Performance Computing and Communications, pp. 1713–1718. IEEE (2015)

  10. Acs, S., Kozlovszky, M., Kacsuk, P.: A novel cloud bursting technique. In: 2014 9th IEEE International Symposium on Applied Computational Intelligence and Informatics, pp. 135–138. IEEE (2014)

  11. Farahabady, M.R.H., Lee, Y.C., Zomaya, A.Y.: Pareto-optimal cloud bursting. IEEE Trans. Parallel Distrib. Syst. 25(10), 2670–2682 (2014)

    Article  Google Scholar 

  12. Farokhi, S., Jamshidi, P., Lakew, E.B., et al.: A hybrid cloud controller for vertical memory elasticity: a control-theoretic approach. Future Gener. Comput. Syst. 65, 57–72 (2016)

    Article  Google Scholar 

  13. Clemente-Castelló, F.J., Nicolae, B., Rafique, M.M., et al.: Evaluation of data locality strategies for hybrid cloud bursting of iterative MapReduce. In: 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 181–185. IEEE (2017)

  14. Lee, Y.C., Lian, B.: Cloud bursting scheduler for cost efficiency. In: 2017 IEEE 10th IEEE International Conference on Cloud Computing, pp. 774–777. IEEE (2017)

  15. Charrada, F.B., Tata, S.: An efficient algorithm for the bursting of service-based applications in hybrid Clouds. IEEE Trans. Serv. Comput. 9(3), 357–367 (2016)

    Article  Google Scholar 

  16. Zhang, Y., Sun, J., Wu, Z.: An heuristic for Bag-of-Tasks scheduling problems with resource demands and budget constraints to minimize makespan on hybrid clouds. In: 2017 5th International Conference on Advanced Cloud and Big Data, pp. 39–44. IEEE (2017)

  17. Daniel, D., Raviraj, P.: Distributed hybrid cloud for profit driven content provisioning using user requirements and content popularity. Clust. Comput. 20(1), 525–538 (2017)

    Article  Google Scholar 

  18. Li, C., Tang, J., Luo, Y.: Distributed QoS-aware scheduling optimization for resource-intensive mobile application in hybrid cloud. Clust. Comput. (2017). https://doi.org/10.1007/s10586-017-1171-2

    Article  Google Scholar 

  19. Zhu, J., Li, X., Ruiz, R., et al.: Scheduling stochastic multi-stage jobs to elastic hybrid cloud resources. IEEE Trans. Parallel Distrib. Syst. (2018). https://doi.org/10.1109/TPDS.2018.2793254

    Article  Google Scholar 

  20. Zuo, L., Shu, L., Dong, S., et al.: A multi-objective hybrid cloud resource scheduling method based on deadline and cost constraints. IEEE Access 5, 22067–22080 (2017)

    Article  Google Scholar 

  21. Champati, J.P., Liang, B.: One-restart algorithm for scheduling and offloading in a hybrid cloud. In: 2015 23rd IEEE International Symposium on Quality of Service, pp. 31–40. IEEE (2015)

  22. http://snap.stanford.edu/data/index.html

  23. Zhang, S., Pan, L., Liu, S., et al.: Profit based two-step job scheduling in clouds. Lect. Notes Comput. Sci. 9659, 481–492 (2016)

    Article  Google Scholar 

  24. Hung, C.C., Golubchik, L., Yu, M.: Scheduling jobs across geo-distributed datacenters. In: 2015 6th ACM Symposium on Cloud Computing, pp. 111–124. ACM (2015)

  25. Wang, W.J., Chang, Y.S., Lo, W.T., et al.: Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments. J. Supercomput. 66(2), 783–811 (2013)

    Article  Google Scholar 

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Acknowledgements

The authors thank the editors and the anonymous reviewers for their helpful comments and suggestions. The work was supported by the National Natural Science Foundation (NSF) under Grants (Nos. 61672397, 61873341, 61472294), Application Foundation Frontier Project of WuHan (No. 2018010401011290), the Fundamental Research Funds for the Central Universities (WUT No. 2017-YB-029). Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology (No. KDXS1804). Any opinions, findings, and conclusions are those of the author and do not necessarily reflect the views of the above agencies.

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430063, China

    Chunlin Li, Jianhang Tang & Youlong Luo

  2. Key Lab of Industrial Internet of Things and Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Chunlin Li

  3. Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Chunlin Li

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  1. Chunlin Li
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  2. Jianhang Tang
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Correspondence to Chunlin Li.

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Li, C., Tang, J. & Luo, Y. Towards operational cost minimization for cloud bursting with deadline constraints in hybrid clouds. Cluster Comput 21, 2013–2029 (2018). https://doi.org/10.1007/s10586-018-2841-4

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