Advertisement

Trust-Aware Resource Provisioning for Meteorological Workflow in Cloud

  • Ruichao Mo
  • Lianyong Qi
  • Zhanyang Xu
  • Xiaolong XuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11910)

Abstract

Cloud computing centers are becoming the predominant platform of offering high-performance computing services based on high-performance computers. However, enabling meteorological workflow that requires real-time response is still challenging due to uncertainty in the cloud, once the computing nodes in the cloud are down, the tasks deployed on the cloud will not be completed in time. To address this problem, an optimal cloud resource for the downtime tasks provisioning method (ODPM) is proposed by formulating a programming model. The ODPM method can select the appropriate migration strategy for the tasks on the compute node to achieve the shortest workflow completion time and load balancing of the compute center compute nodes. A large number of experimental are conducted to verify the benefits brought by ODPM.

Keywords

Cloud computing Trust-aware Meteorological workflow NSGA-II 

Notes

Acknowledgment

This research is also supported by the National Natural Science Foundation of China under grant no. 61702277, no. 61702442, no. 61672276. Besides, this work was supported by the National Key Research and Development Program of China (No. 2017YFB1400600).

References

  1. 1.
    Maenhaut, P.-J., Moens, H., Volckaert, B., Ongenae, V., De Turck, F.: Resource allocation in the cloud: from simulation to experimental validation. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), pp. 701–704. IEEE (2017)Google Scholar
  2. 2.
    Xie, X., Yuan, T., Zhou, X., Cheng, X.: Research on trust model in container-based cloud service. Comput. Mater. Continua 56(2), 273–283 (2018)Google Scholar
  3. 3.
    Botta, A., De Donato, W., Persico, V., Pescapé, A.: Integration of cloud computing and internet of things: a survey. Future Gener. Comput. Syst. 56, 684–700 (2016)CrossRefGoogle Scholar
  4. 4.
    Zhang, J., Xie, N., Zhang, X., Yue, K., Li, W., Kumar, D.: Machine learning based resource allocation of cloud computing in auction. Comput. Mater. Continua 56(1), 123–135 (2018) Google Scholar
  5. 5.
    Wu, Q., Ishikawa, F., Zhu, Q., Xia, Y., Wen, J.: Deadline-constrained cost optimization approaches for workflow scheduling in clouds. IEEE Trans. Parallel Distrib. Syst. 28(12), 3401–3412 (2017)CrossRefGoogle Scholar
  6. 6.
    Xu, X., Dou, W., Zhang, X., Chen, J.: EnReal: an energy-aware resource allocation method for scientific workflow executions in cloud environment. IEEE Trans. Cloud Comput. 4(2), 166–179 (2015)CrossRefGoogle Scholar
  7. 7.
    Duan, R., Prodan, R., Li, X.: Multi-objective game theoretic schedulingof bag-of-tasks workflows on hybrid clouds. IEEE Trans. Cloud Comput. 2(1), 29–42 (2014)CrossRefGoogle Scholar
  8. 8.
    Qi, L., et al.: Structural balance theory-based e-commerce recommendation over big rating data. IEEE Trans. Big Data 4(3), 301–312 (2016)CrossRefGoogle Scholar
  9. 9.
    Qi, L., Chen, Y., Yuan, Y., Fu, S., Zhang, X., Xu, X.: A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems. World Wide Web 4(3), 1–23 (2019)Google Scholar
  10. 10.
    Li, Z., Ge, J., Hu, H., Song, W., Hu, H., Luo, B.: Cost and energy aware scheduling algorithm for scientific workflows with deadline constraint in clouds. IEEE Trans. Serv. Comput. 11(4), 713–726 (2015) CrossRefGoogle Scholar
  11. 11.
    Chaisiri, S., Lee, B.-S., Niyato, D.: Optimization of resource provisioning cost in cloud computing. IEEE Trans. Serv. Comput. 5(2), 164–177 (2011)CrossRefGoogle Scholar
  12. 12.
    Greenberg, A., et al.: Vl2: a scalable and flexible data center network. In: ACM SIGCOMM Computer Communication Review, Vol. 39, pp. 51–62. ACM (2009)CrossRefGoogle Scholar
  13. 13.
    Rankothge, W., Le, F., Russo, A., Lobo, J.: Optimizing resource allocation for virtualized network functions in a cloud center using genetic algorithms. IEEE Trans. Netw. Serv. Manage. 14(2), 343–356 (2017)CrossRefGoogle Scholar
  14. 14.
    Xia, Z., Wang, X., Sun, X., Wang, Q.: A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. IEEE Trans. Parallel Distrib. Syst. 27(2), 340–352 (2015)CrossRefGoogle Scholar
  15. 15.
    Xu, X., Liu, Q., Zhang, X., Zhang, J., Qi, L., Dou, W.: A blockchain-powered crowdsourcing method with privacy preservation in mobile environment. IEEE Trans. Comput. Soc. Syst. 340–352 (2019)Google Scholar
  16. 16.
    Sadooghi, I., et al.: Understanding the performance and potential of cloud computing for scientific applications. IEEE Trans. Cloud Comput. 5(2), 358–371 (2015) CrossRefGoogle Scholar
  17. 17.
    Liu, J., Pacitti, E., Valduriez, P., Mattoso, M.: A survey of data-intensive scientific workflow management. J. Grid Comput. 13(4), 457–493 (2015)CrossRefGoogle Scholar
  18. 18.
    Asvija, B., Shamjith, K., Sridharan, R., Chattopadhyay, S.: Provisioning the MM5 meteorological model as grid scientific workflow. In: 2010 International Conference on Intelligent Networking and Collaborative Systems, pp. 310–314. IEEE (2010)Google Scholar
  19. 19.
    Chen, X., Wei, M., Sun, J.: Workflow-based platform design and implementation for numerical weather prediction models and meteorological data service. Atmos. Clim. Sci. 7(03), 337 (2017)Google Scholar
  20. 20.
    Qi, L., et al.: Finding all you need: web APIs recommendation in web of things through keywords search. IEEE Trans. Comput. Soc. Syst. 337–351 (2019)Google Scholar
  21. 21.
    Ostermann, S., Prodan, R., Schüller, F., Mayr, G.J.: Meteorological applications utilizing grid and cloud computing. In: 2014 IEEE 3rd International Conference on Cloud Networking (CloudNet), pp. 33–39. IEEE (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ruichao Mo
    • 1
  • Lianyong Qi
    • 2
  • Zhanyang Xu
    • 1
    • 3
  • Xiaolong Xu
    • 1
    • 3
    Email author
  1. 1.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina
  2. 2.School of Information Science and EngineeringQufu Normal UniversityQufuChina
  3. 3.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET)Nanjing University of Information Science and TechnologyNanjingChina

Personalised recommendations