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A Hybrid Genetic Algorithm for Privacy and Cost Aware Scheduling of Data Intensive Workflow in Cloud

  • Congyang ChenEmail author
  • Jianxun Liu
  • Yiping Wen
  • Jinjun Chen
  • Dong Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9528)

Abstract

In the context of cloud computing and big data, the data of all walks of life has been obtained conveniently. Some information of users in the business process is in need of protection with the popularity of workflow applications, which will greatly affect the scheduling of workflow. Meanwhile, the amount of data is usually very large in workflow, the data privacy protection in workflow has also become an important research problem. In this paper, in order to satisfy the requirement of data privacy protection from user and minimize the total scheduling cost in workflow scheduling, we proposed a privacy and cost aware method based on genetic algorithm for data intensive workflow applications which takes into account computation cost, data transmission cost and data storage cost in cloud to solve this problem on finding the best scheduling solution. The proposed algorithm uses the summation of upward and downward rank values for prioritizing workflow tasks, then merges it to make an optimal initial population to obtain a good solution quickly. Besides, a series of operations like selection, crossover and mutation have been used to optimize the scheduling. In the workflow task scheduling, we assign the datacenter for tasks needing privacy protection, which data of these tasks cannot be moved or copied to other datacenter. Finally, we demonstrate the potential of proposed algorithm for optimizing economic cost with user privacy protection requirement. The experimental results show that proposed algorithm can help improve the scheduling and save the time and cost by an average of 3.6 % and 15.6 % respectively.

Keywords

Privacy protection Cloud computing Workflow scheduling Genetic 

Notes

Acknowledgments

This paper was supported by Nature Science Fund of China, under grant number 61272063, 61402167, 61202111, 61402168, 61300129, the Planned Science and Technology Project of Hunan Province under grant number 13FJ4048, 2014GK3004, and Scientific Research Fund of Hunan Provincial Education Department under grant number 13C160.

References

  1. 1.
    Pearson, S.: Taking account of privacy when designing cloud computing services In: Proceeding s of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing, pp. 44–52. IEEE Computer Society, Washington DC (2009)Google Scholar
  2. 2.
    Liu, Y., Zhang, T., Jin, X., Cheng, X.: Personal privacy protection in the era of big data. J. Comput. Res. Dev. 52(1), 229–247 (2015)Google Scholar
  3. 3.
    Fabian, B., Ermakova, T., Junghanns, P.: Collaborative and secure sharing of healthcare data in multi-clouds. Inf. Syst. 48, 132–150 (2015)CrossRefGoogle Scholar
  4. 4.
    Kamara, S., Lauter, K.: Cryptographic cloud storage. In: Sion, R., Curtmola, R., Dietrich, S., Kiayias, A., Miret, J.M., Sako, K., Sebé, F. (eds.) RLCPS, WECSR, and WLC 2010. LNCS, vol. 6054, pp. 136–149. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Yang, J., Li, J., Niu, Y.: A hybrid solution for privacy preserving medical data sharing in the cloud environment. Future Gener. Comput. Syst. 43–44, 74–86 (2015)CrossRefGoogle Scholar
  6. 6.
    Chen, C., Liu, J., Wen, Y., Chen, J.: Research on workflow scheduling algorithms in the cloud. In: Cao, J., Wen, L., Liu, X. (eds.) PAS 2014. CCIS, vol. 495, pp. 35–48. Springer, Heidelberg (2015)Google Scholar
  7. 7.
    Zheng, M., Cao, J., Yao, Y.: Cloud workflow scheduling algorithm oriented to dynamic pric changes. Comput. Integr. Manuf. Syst. 19(8), 1849–1858 (2013)Google Scholar
  8. 8.
    Mukute, S., Hapanyengwi, G., Mapako, B., et al.: Scheduling in instance-intensive cost-constrained workflows in a cloud. Int. J. Sci. Eng. Res. 4, 755–760 (2013)Google Scholar
  9. 9.
    Durillo, J.J., Prodan, R.: Multi-objective workflow scheduling in Amazon EC2. Cluster Comput. 17(2), 169–189 (2014)CrossRefGoogle Scholar
  10. 10.
    Liu, K., Yang, Y., Chen, J., et al.: A compromised-cost scheduling algorithm in SwinDeW-C for instance-intensive cost-constrained workflows on cloud computing platform. Int. J. High Perform. Comput. Appl. 24(4), 445–456 (2010)CrossRefGoogle Scholar
  11. 11.
    Chen, W., Zhang, J.: An ant colony optimization approach to a grid workflow scheduling problem with various QoS requirements. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 39(1), 29–43 (2009)CrossRefGoogle Scholar
  12. 12.
    Pandey, S., Wu, L., Guru, S.M., Buyya, R.: A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications (AINA), pp. 400–407 (2010)Google Scholar
  13. 13.
    Wen, Y., Liu, J., Chen, Z.: Instance aspect handling-oriented scheduling optimization in workflows. Ruan Jian Xue Bao J. Softw. 26(3), 574–583 (2015)MathSciNetGoogle Scholar
  14. 14.
    Lu, G., Tan, W., Sun, Y., Zhang, Z., Tang, A.: QoS constraint based workflow scheduling for cloud computing services. J. Softw. 9(4), 926–930 (2014)Google Scholar
  15. 15.
    Rahman, M., Hassan, M.R., Ranjan, R., Buyya, R.: Adaptive workflow scheduling for dynamic grid and cloud computing environment. Concurrency Comput. Pract. Experience 25(13), 1816–1842 (2013)CrossRefGoogle Scholar
  16. 16.
    Delavar, A.G., Aryan, Y.: HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems. Cluster comput. 17(1), 129–137 (2014)CrossRefGoogle Scholar
  17. 17.
    Topcuoglu, H., Hariri, S., Wu, M.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Congyang Chen
    • 1
    • 2
    • 3
    Email author
  • Jianxun Liu
    • 1
  • Yiping Wen
    • 1
    • 2
  • Jinjun Chen
    • 1
    • 2
    • 3
  • Dong Zhou
    • 1
    • 2
  1. 1.Key Laboratory of Knowledge Processing and Networked ManufactureHunan University of Science and TechnologyXiangtanChina
  2. 2.College of Computer Science and EngineeringHunan University of Science and TechnologyXiangtanChina
  3. 3.Faculty of Engineering and Information TechnologyUniversity of Technology SydneyUltimoAustralia

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