Trust Model Based Scheduling of Stochastic Workflows in Cloud and Fog Computing

  • J. Angela Jennifa Sujana
  • M. Geethanjali
  • R. Venitta Raj
  • T. Revathi
Part of the Studies in Big Data book series (SBD, volume 49)


The Cloud computing is a lucrative, challenging and beneficial technology in the IT world. The emergence of Internet of Things (IoT) has made cloud computing to be combined with fog computing, in order to avoid latency. These technologies have daring challenges. This chapter focuses on two major challenges, namely security and scheduling of user requests. The security is met by our proposed trust model which includes both direct trust and reputation relationship. This chapter initially, focuses on assuring trusted environment in the cloud. Then a trust model for cloud cum fog environment is proposed. The new trust model would ensure that the user’s requests are serviced with enough security guaranteed level based on the Service Level Agreement (SLA) negotiated with the cloud provider. Based on the trust value computed, the user’s requests are scheduled to the appropriate resource by applying the Trust based Stochastic Scheduling (TSS) algorithm. The trust based stochastic scheduling minimizes makespan of the schedule is achieved for a secured cloud environment


Trust model Stochastic scheduling Service level agreement Cloud computing 


  1. 1.
    Barik, R.K., Tripathi, A., Dubey, H., Lenka, R.K., Pratik, T., Sharma, S., Das, H.: Mistgis: optimizing geospatial data analysis using mist computing. In: Progress in Computing, Analytics and Networking, pp. 733–742. Springer, Singapore (2018)Google Scholar
  2. 2.
    Barik, R.K., Dubey, H., Misra, C., Borthakur, D., Constant, N., Sasane, S.A., Mankodiya, K.: Fog Assisted Cloud Computing in Era of Big Data and Internet-of-Things: Systems, Architectures, and Applications. In: Cloud Computing for Optimization: Foundations, Applications, and Challenges, pp. 367–394. Springer, Cham (2018)Google Scholar
  3. 3.
    Wang, T., et al.: A novel trust mechanism based on fog computing in sensor cloud system. Future Gener. Comput. Syst. (2018). Scholar
  4. 4.
    Nitti, M., Girau, R., Atzori, L.: Trustworthiness management in the social Internet of Things. IEEE Trans. Knowl. Data Eng. 26(5) (2014)Google Scholar
  5. 5.
    Durillo, J.J., Prodan, R.: Multi-objective workflow scheduling in Amazon EC2. Clust. Comput. 17(2), 169–189 (2014)CrossRefGoogle Scholar
  6. 6.
    Malawski, M., Figiela, K., Nabrzyski, J.: Cost minimization for computational applications on hybrid cloud infrastructures. Future Gener. Comput. Syst. 29(7), 1786–1794Google Scholar
  7. 7.
    Yu, H., Kaminsky, M., Gibbons, P.B., Flax-man, A.D.: SybilGuard: defending against sybil attacks via social networks. IEEE/ACM Trans. Netw. 16(3), 576 589 (2008)Google Scholar
  8. 8.
    Xie, T., Qin, X.: Scheduling security-critical real-time applications on clusters. IEEE Trans. Comput. 55(7) (2006)Google Scholar
  9. 9.
    Tang, X., Li, K., Zeng, Z., Veeravalli, B.: A novel security-driven scheduling algorithm for precedence-constrained tasks in heterogeneous distributed systems. IEEE Trans. Comput. 60(7), 1017–1029 (2011)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Xie, T., Qin, X.: Performance evaluation of a new scheduling algorithm for distributed systems with security heterogeneity. J. Parallel Distrib. Comput. 67, 1067–1081 (2007)CrossRefGoogle Scholar
  11. 11.
    Jia, C., Xie, L., Gan, X.C., Liu, W., Han, Z.: A trust and reputation model considering overall peer consulting distribution. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 42(1), 164–177 (2012)Google Scholar
  12. 12.
    Zhang, P., Zhou, M., Fortino, G.: Security and trust issues in fog computing: a survey. Future Gener. Comput. Syst. 88, 16–27 (2018)Google Scholar
  13. 13.
    Al-Kahtani, M.A., Sandhu, R.: Induced Role Hierarchies with Attribute-Based RBAC, SACMAT03, June 2–3, Como, Italy. ACM 1-58113-681-1/03/0006 (2003)Google Scholar
  14. 14.
    Wang, W., Zeng, G., Tang, D., Yao, J.: Cloud-DLS: dynamic trusted scheduling for cloud computing. Expert Syst. Appl. Elsevier 39, 23212329 (2012)Google Scholar
  15. 15.
    Tao Xie and Xiao Qin: Security-aware resource allocation for real-time parallel jobs on homogeneous and heterogeneous clusters. IEEE Trans. Parallel Distrib. Syst. 19(5), 682–697 (2008)CrossRefGoogle Scholar
  16. 16.
    Gary, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Co., San Francisco, CA (1979)Google Scholar
  17. 17.
    Kar, I., Parida, R.R., Das, H.: Energy aware scheduling using genetic algorithm in cloud data centers. In: International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), pp. 3545–3550. IEEE (2016)Google Scholar
  18. 18.
    Kar, I., Das, H.: Energy aware task scheduling using genetic algorithm in cloud datacentres. Int. J. Comput. Sci. Inf. Technol. Res. 4(1), 106–111 (2016)Google Scholar
  19. 19.
    Topcuoglu, H., Hariri, S., Wu, M.-Y.: Performance-effective and low complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002)Google Scholar
  20. 20.
    Arabnejad, H., Barbosa, J.G.: List scheduling algorithm for heterogeneous systems by an optimistic cost table. IEEE Trans. Parallel Distrib. Syst. 25(3), 682–694 (2014)CrossRefGoogle Scholar
  21. 21.
    Tang, X., Li, K., Liao, G., Li, R.: List scheduling with duplication for heterogeneous computing systems. J. Parallel Distrib. Comput. Elsevier 70, 323–329 (2010)CrossRefGoogle Scholar
  22. 22.
    Sih, G.C., Lee, E.A.: A compile-time scheduling heuristic for interconnection-constrained heterogeneous machine architectures. IEEE Trans. Parallel Distrib. Syst. 4(2), 175–187 (1993)CrossRefGoogle Scholar
  23. 23.
    Li, K., Tang, X., Veeravalli, B.: Scheduling precedence constrained stochastic tasks on heterogeneous cluster systems. IEEE Trans. Comput. 63(99), 191–204 (2013)Google Scholar
  24. 24.
    Zhao, H., Sakellariou, R.: An experimental investigation into the rank function of the heterogeneous earliest finish time scheduling algorithm. In: Proceedings of 9th International Euro-Par Conference, vol. 2790, pp. 189–194. Springer (2003)Google Scholar
  25. 25.
    Das, H., Jena, A.K., Badajena, J.C., Pradhan, C., Barik, R.K.: Resource allocation in cooperative cloud environments. In: Progress in Computing, Analytics and Networking, pp. 825–841. Springer, Singapore (2018)Google Scholar
  26. 26.
    Nayak, J., Naik, B., Jena, A. K., Barik, R.K., Das, H.: Nature inspired optimizations in cloud computing: applications and challenges. In: Cloud Computing for Optimization: Foundations, Applications, and Challenges, pp. 1–26. Springer, Cham (2018)Google Scholar
  27. 27.
    Sarkhel, P., Das, H., Vashishtha, L.K.: Task-scheduling algorithms in cloud environment. In: Computational Intelligence in Data Mining, pp. 553–562. Springer, Singapore (2017)Google Scholar
  28. 28.
    El-Rewini, H., Lewis, T.G.: Scheduling parallel program tasks onto arbitrary target machines. J. Parallel Distrib. Comput. 9(2), 138–153 (1990)CrossRefGoogle Scholar
  29. 29.
    Ilavarasan, E., Thambidurai, P., Mahilmannan, R.: High Performance Task Scheduling Algorithm for Heterogeneous Computing System, Distributed and Parallel Computing, Springer LNCS, vol. 3719, pp. 193–203 (2005)Google Scholar
  30. 30.
    Bertsekas, D.P., Castanon, D.A.: Rollout algorithms for stochastic scheduling problems. J. Heuristics 5(1), 89–108 (1999)CrossRefGoogle Scholar
  31. 31.
    Shmoys, D.B., Sozio, M.: Approximation algorithms for 2-stage stochastic scheduling problems. In: Lecture Notes in Computer Science, vol. 4513, pp. 145–157. Springer (2007)Google Scholar
  32. 32.
    Gourgand, M., Grangeon, N., Norre, S.: A contribution to the stochastic flow shop scheduling problem. Eur. J. Oper. Res. 151(2), 415433 (2003)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Megow, N., Uetz, M., Vredeveld, T.: Models and algorithms for stochastic online scheduling. Math. Oper. Res. 31(3), 513525 (2006)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Skutella, M., Uetz, M.: Stochastic machine scheduling with precedence constraints. SIAM J. Comput. 34(4), 788802 (2005)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Tang, X., Li, K., Liao, G., Fang, K., Wu, F.: A stochastic scheduling algorithm for precedence constrained tasks on grid. Future Gener. Comput. Syst. 27(8), 1083–1091 (2011)CrossRefGoogle Scholar
  36. 36.
    Canon, L.C., Jeannot, E.: Evaluation and optimization of the robustness of DAG schedules in heterogeneous environments. IEEE Trans. Parallel Distrib. Syst. 21(4), 532–546 (2010)CrossRefGoogle Scholar
  37. 37.
    Kamvar, S., Schlosser, M., Garcia-Molina, H.: The Eigen trust algorithm for reputation management in P2P networks. In: Proceedings of the 12th International World Wide Web Conference, Budapest, Hungary, pp. 640651 (2003)Google Scholar
  38. 38.
    Nielsen, M., Krukow, K., Sassone, V.: A Bayesian model for event-based trust. Electron. Notes Theor. Comput. Sci. 172(1), 499–521 (2007)MathSciNetCrossRefGoogle Scholar
  39. 39.
    Xiong, L., Liu, L.: Peer trust: supporting reputation-based trust for peer-to-peer electronic communities. IEEE Trans. Knowl. Data Eng. 16(7), 843–857 (2004)CrossRefGoogle Scholar
  40. 40.
    Zhou, R., Hwang, K.: Power trust: a robust and scalable reputation system for trusted peer-to-peer computing. IEEE Trans. Parallel Distrib. Syst. 18(4), 460–473 (2007)CrossRefGoogle Scholar
  41. 41.
  42. 42.
  43. 43.
    Clark, C.: The greatest of a finite set of random variables. Oper. Res. 9(2), 145–162 (1961)MathSciNetCrossRefGoogle Scholar
  44. 44.
    Kwok, K.Y.-K., Ahmed, I.: Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput. Surv. 31(4), 406–471 (1999)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • J. Angela Jennifa Sujana
    • 1
  • M. Geethanjali
    • 1
  • R. Venitta Raj
    • 1
  • T. Revathi
    • 1
  1. 1.Department of Information TechnologyMepco Schlenk Engineering CollegeSivakasiIndia

Personalised recommendations