Skip to main content
SpringerLink
Log in

Trust-driven and QoS demand clustering analysis based cloud workflow scheduling strategies

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Due to the restrictions that most traditional scheduling strategies only cared about users’ quality of service (QoS) time or cost requirements, lacked the effective analysis of users’ real service demand and could not guarantee scheduling security, this paper added trust into workflow’s QoS target and proposed a novel customizable cloud workflow scheduling model. In order to better analyze different user’s service requirements and provide customizable services, the new model divided workflow scheduling into two stages: the macro multi-workflow scheduling as the unit of cloud user and the micro single workflow scheduling. It introduced trust mechanism into multi-workflow scheduling level. And in single workflow scheduling level, it classified workflows into time-sensitive, cost-sensitive and balance three types according to different workflow’s QoS demand parameters using fuzzy clustering method. Based on it, it customized different service strategies for different type. The simulation experiments show that the new schema has some advantages in shortening workflow’s final completion time, achieving relatively high execution success rate and user satisfaction compared to other kindred solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

References

  1. Liu, P.: Cloud Computing, 2nd edn. Publishing House of Electronics Industry, Beijing (2011)

    Google Scholar 

  2. Foster, I., Kesselman, C.: “The Grid: Blueprint for a New Computing Infrastructure”. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  3. Chinese Cloud Computing Forum. http://bbs.chinacloud.cn

  4. Vouk, M.A.: Cloud computing-issues research and implementations, Proceedings ITI 2008 30th International Conference on Information Technology Interfaces, 2008. IEEE, (2008)

  5. Zhu, F., He, Y.: Parallel Scheduling Algorithm in Distributed Computing Theory and Design. Wuhang University Press, Wuhang (2003)

    Google Scholar 

  6. Ullman, J.D.: Complexity of sequencing problems. In: Coffman, E.G. (ed.) Computer and Job Scheuling Theory. Wiley, New York (1976)

    Google Scholar 

  7. Li, X., Gui, X.: Cognitive model of dynamic trust forecasting. J. Softw. 21(1), 163–176 (2010)

    Article  Google Scholar 

  8. Li, X., Gui, X.: Research on dynamic trust model for large scale distributed environment. J. Softw. 18(6), 1510–1521 (2007)

    Article  MathSciNet  Google Scholar 

  9. Pan, J., Xu, F., Lv, J.: Reputation-based recommender discovery approach for service selection. J. Softw. 21(2), 388–400 (2010)

    Article  Google Scholar 

  10. Hu, J., Wu, Q., Zhou, B., et al.: Robust feedback credibility-based distributed P2P trust model. J. Softw. 20(10), 2885–2898 (2009)

    Article  Google Scholar 

  11. Wang, S., Zhang, L., Li, H.: Evaluation approach of subjective trust based on cloud model. J. Softw. 21(6), 1341–1352 (2010)

    Article  Google Scholar 

  12. Hu, C., Chang, B., Xu, H., Zhao, Q.: Approach of service combination based on deepness trust reasoning in complex cross-organizational collaboration. J. Central South University (Science and Technology) 43(2), 567–575 (2012)

    Google Scholar 

  13. Wang, Y., Xiao, X., Jia, L.: Dynamic and comprehensive evaluation method for interoperability trust based on fuzzy variable weighting. J. Comput. Res. Dev. 49(6), 1235–1242 (2012)

    Google Scholar 

  14. Xie, X., Liu, L., Zhao, P.: Trust model based on double incentive and deception detection for cloud computing. J. Electron. Inform. Technol. 34(4), 812–817 (2012)

    Google Scholar 

  15. Wei, Z., Zhou, W., Ren, X., Wei, Q., et al.: A strategy-proof trust based decision mechanism for pervasive computing environments. Chin. J. Comput. 35(5), 871–882 (2012)

    Article  Google Scholar 

  16. Liu, W., Yin, L., Fang, B., Zhang, H.: A hierarchical trust model for the internet of things. Chin. J. Comput. 35(5), 846–855 (2012)

    Article  Google Scholar 

  17. Long, J., Liu, X., Yuan, X., Zhang, Z., et al.: A web services composition strategy based on trust reasoning and evolution. Chin. J. Comput. 35(2), 298–314 (2012)

    Article  Google Scholar 

  18. Qi, x, Jiyi, W., Guilin, W., Wenhao, L., et al.: Provably secure authentication protocol based on convertible proxy signcryption in cloud computing. Sci. China 42(3), 303–313 (2012)

    Google Scholar 

  19. Jiyi, W., Ping, L., Pan, X., Zhuo, L.: Cloud computing: concept and platform. Telecommun. Sci. 33(3), 59–66 (2012)

    Google Scholar 

  20. Li, Wenjuan, Ping, L., Li, J., Qiu, Q.: Cloud service discovery algorithm based on trust fuzzy comprehensive evaluation. ICIC Express Lett. Part B 3(2), 1–6 (2012)

    Google Scholar 

  21. Li, W., Ping, L., Pan, X.: Trust model to enhance security and interoperability of cloud environment. In: Proceedings of the 1st International Conference on Cloud Computing (CloudCom’09), pp. 69–79. Springer, Berlin (2009)

  22. Li, W., Ping, L., Qiu, Q., Zhang, Q.: Research on trust management strategies in cloud computing environment. J. Comput. Inform. Syst. 8(4), 1757–1763 (2012)

    Google Scholar 

  23. Zhao, G.: Research on adaptive genetic algorithm based service workflow scheduling problem. Sun yat-sen university, Guangzhou (2010)

  24. Yu, J., Tian, G., Cao, Y., et al.: A resource allocating algorithm in reliability. J. Comput. Res. Dev. 46(2), 1821–1829 (2009)

    Google Scholar 

  25. Yuan, Y., Li, X., Wang, Q., et al.: Time optimization heuristics for scheduling budget\_constrained grid workflows. J. Comput. Res. Dev. 46(2), 194–201 (2009)

    Google Scholar 

  26. Li, J.: QoS Optimal, Grid Workflow Scheduling Algorithm. Guangxi University, Guilin (2009)

    Google Scholar 

  27. Wang, Y., Hu, C., Du, Z.: QoS-awared grid workflow schedule. J. Softw. 17(11), 2341–2351 (2006)

    Article  MATH  Google Scholar 

  28. Azzedin, F., Maheswaran, M.: Integrating trust into grid resource management systems. Proceedings of the 2002 International Conference on Parallel Processing, pp. 47–54. Vancouver, British Columbia (2002)

  29. He, X., Sun, X., Gregor, V.L.: QoS guided Min-rain heuristic for grid task scheduling. J. Comput. Sci. Technol. 18(4), 442–451 (2003)

    Article  MATH  Google Scholar 

  30. Weng, C., Lu, X.: Heuristic scheduling for bag -of-tasks applications in combination with QoS in the computational grid. Future Gener. Comput. Syst. 21(2), 271–280 (2005)

    Article  Google Scholar 

  31. Buyya, R., Abramson, D., Giddy, J., et al.: Economic models for resource management and scheduling in grid computing. J. Concurr. Comput. 14(13–15), 1507–1542 (2002)

    Google Scholar 

  32. Hum, P.M., Thompson, M.R.: Security implications of typical grid computing usage scenario, pp. 95–103. Proceedings of the IEEE HPDC, San Francisco, CA (2001)

  33. Li K., He Y., Liu X.: Security-driven scheduling algorithms based on eigentrust in grid. In: Proceedings of the 6th International Conference of Parallel and Distributed Computing Applications and Technologies, Denver, USA, pp. 1068–1072 (2005)

  34. Zhang, W., Fang, B., et al.: A trust-QoS enhanced grid service scheduling. chin. J. Comput. 29(7), 1157–1166 (2006)

    Google Scholar 

  35. Yuan, L., Zeng, G., et al.: Dynamic level scheduling based on trust model in grid computing. Chin. J. Comput. 29(7), 1217–1224 (2006)

    Google Scholar 

  36. Hu, Z., Juan, H.: Grid workflow scheduling algorithm under constraints of trust. Appl. Res. Comput. 27(8), 2936–2938 (2010)

    Google Scholar 

  37. Hu, C., Wu, M., Liu, G.: QoS scheduling based on trust relationship in web service workflow. Chin. J. Comput. 32(1), 42–53 (2009)

    Google Scholar 

  38. Hu, C., Wu, M., Liu, G., et al.: An approach to constructing web service workflow based on business spanning graph. J. Softw. 18(8), 1870–1882 (2007)

    Google Scholar 

  39. Han, J.: Research on grid workflow scheduling algorithm under the constraints of trust. Zhongnan University, Changsha (2010)

    Google Scholar 

  40. Wang, X.: On the policy description and quantification model for trust management. National University of Defense Technology, Changsha (2009)

  41. Li, W., Zhang, Q., Ping, L., Pan, X.: Cloud scheduling algorithm based on fuzzy clustering. J. Commun. 33(3), 146–154 (2012)

    Google Scholar 

  42. Li, W., Pan, X., Zhang, Q., Ping, L.: A novel job scheduling model to enhance efficiency and overall user fairness of cloud computing environment. In: Proceedings of the 1st International Conference on Cloud Computing and Services Science (CLOSER2011), pp. 152–157. SCITePress, Noordwijkerhout (2011)

  43. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)

    Book  MATH  Google Scholar 

  44. NetLogo home page. http://ccl.northwestern.edu/netlogo

  45. Calheiros, R. N., Ranjan1, R., De Rose, C. A. F., and Buyya, R.: “CloudSim: a novel framework for modeling and simulation of cloud computing infrastructures and services”. http://www.chinacloud.cn/show.aspx?id=1316&cid=28

  46. Buyya, R., Ranjan, R., and Calheiros, R.N.: “Modeling and simulation of scalable cloud computing environments and the CloudSim toolkit: challenges and opportunities”. In: Proceedings of the 7th High Performance Computing and Simulation Conference (HPCS09), IEEE Computer Society, June 2009

  47. Chung, J.Y., Shih, W.K., Jane, W.S., et al.: Scheduling periods jobs that allow imprecise results. IEEE Trans. Comput. 39(9), 1156–1174 (1990)

    Article  Google Scholar 

  48. Yu, A.C., Lin, K.J.: Scheduling parallelizable imprecise computations on multiprocessor. In: Proceedings of the 5th International Symposium on Parallel Processing. Anaheim, CA, USA, pp. 531–536 April 30–May 2 1991

  49. Zhang, W., Liu, X., Yun, X., Zhang, H., et al.: Trust-driven job scheduling heuristics for computing grid. J. Commun. 27(2), 73–79 (2006)

    Google Scholar 

  50. Zhao, C.: Research and Realization of Job Scheduling Algorithm in Cloud Environment. Beijing Jiaotong University, Beijing (2009)

    Google Scholar 

Download references

Acknowledgments

This work is supported by the Natural Science Foundation Research Project of Zhejiang Province (LQ12G02016). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers.

Author information

Authors and Affiliations

  1. Hangzhou Normal University, Hangzhou, China

    Wenjuan Li, Jiyi Wu, Keyong Hu & Jing Li

  2. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Jiyi Wu & Qifei Zhang

Authors
  1. Wenjuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiyi Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qifei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Keyong Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenjuan Li.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, W., Wu, J., Zhang, Q. et al. Trust-driven and QoS demand clustering analysis based cloud workflow scheduling strategies. Cluster Comput 17, 1013–1030 (2014). https://doi.org/10.1007/s10586-013-0340-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-013-0340-1

Keywords

Search

Navigation