Advertisement

Non-live Task Migration Approach for Scheduling in Cloud Based Applications

Conference paper
  • 1.3k Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 828)

Abstract

Cloud computing is one of the most innovative technologies to present computerized generation. Scheduling plays a major role in it. The connectivity of Virtual Machines (VMs) to schedule the assigned tasks is most attractive field to research. This paper introduces a confined Task Migration based Scheduling Algorithm using enhanced-First Come First Serve (TM-eFCFS) method. This paper focuses on Non-live task migration to transmit partially executed tasks to another VM in order to achieve fastest execution. Objective of this work is to minimize the MakeSpan and to optimize the resource utilization. The proposed work has been simulated in CloudSim toolkit package. The results have been compared with pre-existing scheduling algorithms with same experimental configuration. Important parameters such as MakeSpan and utilization of resources are compared to measure the performance of TM-eFCFS. Extensive simulation results prove that introduced work has better results compared to existing approaches. Results show that 99% resource utilization has been achieved. Plotted graphs and calculated values show that the proposed work is very effective for task scheduling.

Keywords

Cloud computing Task Task migration Virtual machine Resource utilization 

References

  1. 1.
    Li, Q., Hao, Q., Xiao, L., Li, Z.: Adaptive management of virtualized resources in cloud computing using feedback control. In: First International Conference on Information Science and Engineering, Nanjing, China, pp. 99–102. IEEE (2009)Google Scholar
  2. 2.
    Parikh, K., Hawanna, N., Haleema, P.K., Jayasubalakshmi, R., Iyengar, N.: Virtual machine allocation policy in cloud computing using CloudSim in Java. Int. J. Grid Distrib. Comput. 8(1), 145–158 (2015)CrossRefGoogle Scholar
  3. 3.
    Tawfeek, M., El-Sisi, A., Keshk, A., Torkey, F.: Cloud task scheduling based on ant colony optimization. Int. Arab J. Inf. Technol. 12(2), 129–137 (2015)Google Scholar
  4. 4.
    Gao, K., Wang, Q., Xi, L.: Reduct algorithm based execution times prediction in knowledge discovery cloud computing environment. Int. Arab J. Inf. Technol. 11(3), 268–275 (2014)Google Scholar
  5. 5.
    Pop, F., Dobre, C., Cristea, V., Bessis, N.: Scheduling of sporadic tasks with deadline constrains in cloud environments. In: 27th International Conference on Advanced Information Networking and Applications, Barcelona, Spain, pp. 764–771. IEEE (2013)Google Scholar
  6. 6.
    Dorigo, M., Blum, C.: Ant colony optimization theory: a survey. Theor. Comput. Sci. 344, 243–278 (2005)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Guo, L., Zhao, S., Shen, S., Jiang, C.: Task scheduling optimization in cloud computing based on heuristic algorithm. J. Netw. 7(3), 547–553 (2012)Google Scholar
  8. 8.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Washington, USA, pp. 1942–1948 (1995)Google Scholar
  9. 9.
    Cope, J.M., Trebon, N., Tufo, H.M., Beckman, P.: Robust data placement in urgent computing environments. Paper Presented at the 23rd IEEE International Symposium on Parallel and Distributed Processing, IPDPS, Rome, Italy, 23–29 May 2009 (2009)Google Scholar
  10. 10.
    Saaty, T.L.: Decision making with the analytic hierarchy process. Int. J. Serv. Sci. (IJSS) 1(1), 83–98 (2008)Google Scholar
  11. 11.
    Lin, W., Liang, C., Wang, J.Z., Buyya, R.: Bandwidth-aware divisible task scheduling for cloud computing. Softw.-Pract. Exp. 44(2), 163–174 (2014)CrossRefGoogle Scholar
  12. 12.
    Hong, B., Prasanna, V.K.: Distributed adaptive task allocation in heterogeneous computing environments to maximize throughput. In: Proceedings of the 18th International Parallel and Distributed Processing Symposium (IPDPS 2004), Santa Fe, USA, pp. 52–60. IEEE (2004)Google Scholar
  13. 13.
    Santhosh, B., Manjaiah, D.H.: An improved task scheduling algorithm based on max-min for cloud computing. Int. J. Innov. Res. Comput. Commun. Eng. 2(2), 84–88 (2014)Google Scholar
  14. 14.
    Elzeki, O.M., Reshad, M.Z., Elsoud, M.A.: Improved max-min algorithm in cloud computing. Int. J. Comput. Appl. 50(12), 22–27 (2012)Google Scholar
  15. 15.
    Chawla, Y., Bhonsle, M.: Dynamically optimized cost based task scheduling in cloud computing. Int. J. Emerg. Trends Technol. Comput. Sci. (IJETTCS) 2(3), 38–42 (2013)Google Scholar
  16. 16.
    Chen, H., Wang, F., Helian, N., Akanmu, G.: User-priority guided min-min scheduling algorithm for load balancing in cloud computing. In: National Conference on Parallel Computing Technologies, Bengaluru, India, pp. 1–8. IEEE (2013)Google Scholar
  17. 17.
    Yu, X., Yu, X.: A new grid computation-based min-min algorithm. In: IEEE 6th International Conference on Fuzzy Systems and Knowledge Discovery, Tianjin, China, pp. 43–45 (2009)Google Scholar
  18. 18.
    Buyya, R., Ranjan, R., 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, Leipzig, Germany, pp. 1–11 (2009)Google Scholar
  19. 19.
    Radulescu, A., Gemund, A.: Fast and effective task scheduling in heterogeneous systems. In: Proceedings of the 9th Heterogeneous Computing Workshop (HCW 2000), Cancun, Mexico, pp. 229–238 (2000)Google Scholar
  20. 20.
    Priyadarsini, R.J., Arockiam, L.: Performance evaluation of min-min and max-min algorithms for job scheduling in federated cloud. Int. J. Comput. Appl. 99(18), 47–54 (2014)Google Scholar
  21. 21.
    Chen, W., Xie, G., Li, R., Bai, Y., Fan, C., Li, K.: Efficient task scheduling for budget constrained parallel applications on heterogeneous cloud computing systems. Future Gen. Comput. Syst. 74, 1–11 (2017)CrossRefGoogle Scholar
  22. 22.
    Awad, A.I., El-Hefnawy, N.A., Abdel_kader, H.M.: Enhanced particle swarm optimization for task scheduling in cloud computing environments. Procedia Comput. Sci. 65, 920–929 (2015)Google Scholar
  23. 23.
    Ali, H.G., Saroit, I.A., Kotb, A.M.: Grouped tasks scheduling algorithm based on QoS in cloud computing network. Egypt. Inf. J. 18, 11–19 (2017)CrossRefGoogle Scholar
  24. 24.
    Xiong, F., Yeliang, C., Lipeng, Z., Bin, H., Song, D., Dong, W.: Deadline based scheduling for data-intensive applications in clouds. J. China Univ. Posts Telecommun. 23(6), 8–15 (2016)CrossRefGoogle Scholar
  25. 25.
    Wu, X., Deng, M., Zhang, R., Zeng, B., Zhou, S.: A task scheduling algorithm based on QoS-driven in cloud computing. Procedia Comput. Sci. 17, 1162–1169 (2013)CrossRefGoogle Scholar
  26. 26.
    Chitra, D., Uthariaraj, Y.R.: Load balancing in cloud computing environment using improved weighted round robin algorithm for non-preemptive dependent tasks. Sci. World J. Hindawi 2016, 1–14 (2016)Google Scholar
  27. 27.
    Tian, W., Xu, M., Chen, A., Li, G., Wang, X., Chen, Y.: Open-source simulators for cloud computing: comparative study and challenging issues. Simul. Model. Pract. Theory 58, 239–254 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Engineering and TechnologyHNB Garhwal UniversitySrinagar GarhwalIndia
  2. 2.Uttarakhand Technical UniversityDehradunIndia

Personalised recommendations