Advertisement

International Journal of Parallel Programming

, Volume 42, Issue 5, pp 739–754 | Cite as

Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization

  • Fahimeh RamezaniEmail author
  • Jie Lu
  • Farookh Khadeer Hussain
Article

Abstract

Live virtual machine (VM) migration is a technique for achieving system load balancing in a cloud environment by transferring an active VM from one physical host to another. This technique has been proposed to reduce the downtime for migrating overloaded VMs, but it is still time- and cost-consuming, and a large amount of memory is involved in the migration process. To overcome these drawbacks, we propose a Task-based System Load Balancing method using Particle Swarm Optimization (TBSLB-PSO) that achieves system load balancing by only transferring extra tasks from an overloaded VM instead of migrating the entire overloaded VM. We also design an optimization model to migrate these extra tasks to the new host VMs by applying Particle Swarm Optimization (PSO). To evaluate the proposed method, we extend the cloud simulator (Cloudsim) package and use PSO as its task scheduling model. The simulation results show that the proposed TBSLB-PSO method significantly reduces the time taken for the load balancing process compared to traditional load balancing approaches. Furthermore, in our proposed approach the overloaded VMs will not be paused during the migration process, and there is no need to use the VM pre-copy process. Therefore, the TBSLB-PSO method will eliminate VM downtime and the risk of losing the last activity performed by a customer, and will increase the Quality of Service experienced by cloud customers.

Keywords

Cloud computing Particle swarm optimization Virtual machine migration Task scheduling Cloudsim Jswarm 

References

  1. 1.
    Celesti, A., Fazio, M., Villari, M., Puliafito, A.: Virtual machine provisioning through satellite communications in federated cloud environments. Futur Gener Comput Syst 28(1), 85–93 (2012)CrossRefGoogle Scholar
  2. 2.
    Buyya, R., Broberg, J., Goscinski, A. (eds.): Cloud Computing, Principles and Paradigms. Wiley, Hoboken (2011)Google Scholar
  3. 3.
    Rochwerger, B., Breitgand, D., Epstein, A., Hadas, D., Loy, I., Nagin, K., Tordsson, J., Ragusa, C., Villari, M., Clayman, S.: Reservoir-when one cloud is not enough. Computer 44(3), 44–51 (2011)CrossRefGoogle Scholar
  4. 4.
    Ranjan, R., Buyya, R.: Decentralized overlay for federation of enterprise clouds. Technical report, The University of Melbourne (2008)Google Scholar
  5. 5.
    Goiri, I., Guitart, J., Torres, J.: Characterizing cloud federation for enhancing providers’ profit. In: IEEE 3rd International Conference on Cloud Computing (CLOUD), pp. 123–130 (2010)Google Scholar
  6. 6.
    Clark, C., Fraser, K., Hand, S., Jacob, G.H.: Live migration of virtual machines. In: 2nd ACM/USENIX Symposium on Network Systems, Design and Implementation (NSDI), pp. 273–286 (2005)Google Scholar
  7. 7.
    Jun, C., Xiaowei, C.: IPv6 virtual machine live migration framework for cloud computing. Energy Procedia 13, 5753–5757 (2011)Google Scholar
  8. 8.
    Jin, H., Gao, W., Wu, S., Shi, X., Wu, X., Zhou, F.: Optimizing the live migration of virtual machine by CPU scheduling. J. Netw. Comput. Appl. 34(4), 1088–1096 (2011)CrossRefGoogle Scholar
  9. 9.
    Liao, X., Jin, H., Liu, H.: Towards a green cluster through dynamic remapping of virtual machines. Futur. Gener. Comput. Syst. 28(2), 469–477 (2012)CrossRefGoogle Scholar
  10. 10.
    Ramezani, F., Lu, J.,Hussain, F.: Tasks based system load balancing approach in cloud environment. In: International Conference on Intelligent Systems and Knowledge (ISKE), pp. 31–42 (2012)Google Scholar
  11. 11.
    Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R.: Cloudsim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Experience 41(1), 23–50 (2011)CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Jain, N., Menache, I., Naor, J., Shepherd, F.: Topology-aware VM migration in bandwidth oversubscribed datacenter networks. In: 39th International Colloquium, pp. 586-597 (2012)Google Scholar
  14. 14.
    Kozuch, M., Satyanarayanan, M.: Internet suspend/resume. In: 4th IEEE Workshop on Mobile Computing Systems and Applications, pp. 40– 46 (2002)Google Scholar
  15. 15.
    Sapuntzakis, C.P., Chandra, R., Pfaff, B., Chow, J., Lam, M.S., Rosenblum, M.: Optimizing the migration of virtual computers. ACM SIGOPS Oper. Syst. Rev. 36(SI), 377–390 (2002)CrossRefGoogle Scholar
  16. 16.
    Whitaker, A., Cox, R.S., Shaw, M., Gribble, S.D.: Constructing services with interposable virtual hardware. In: 1st Symposium on Networked Systems Design and Implementation (NSDI), pp. 169–182 (2004)Google Scholar
  17. 17.
    Osman, S., Subhraveti, D., Su, G., Nieh, J.: The design and implementation of ZAP: a system for migrating computing environments. ACM SIGOPS Oper. Syst. Rev. 36(SI), 361–376 (2002)CrossRefGoogle Scholar
  18. 18.
    Nelson, M., Lim, B.H., Hutchins, G.: Fast transparent migration for virtual machines. In: USENIX Annual Technical Conference, pp. 391–394 (2005)Google Scholar
  19. 19.
    Nicolae, B., Cappello, F.: Blobcr: virtual disk based checkpoint-restart for HPC applications on IaaS clouds. J. Parallel Distrib. Comput. 73(5), 698–711 (2013)Google Scholar
  20. 20.
    Atif, M., Strazdins, P.: Adaptive parallel application resource remapping through the live migration of virtual machines. Futur. Gener. Comput. Syst. doi: 10.1016/j.future.2013.06.028 (2013)
  21. 21.
    Lin, W., Wang, J.Z., Liang, C., Qi, D.: A threshold-based dynamic resource allocation scheme for cloud computing. Procedia Eng. 23, 695–703 (2011)CrossRefGoogle Scholar
  22. 22.
    Zomaya, A.Y., Yee-Hwei, T.: Observations on using genetic algorithms for dynamic load-balancing. IEEE Trans. Parallel Distrib. Syst. 12(9), 899–911 (2001)CrossRefGoogle Scholar
  23. 23.
    Zhao, C., Zhang, S., Liu, Q., Xie, J., Hu, J.: Independent tasks scheduling based on genetic algorithm in cloud computing. In: 5th International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1–4 (2009)Google Scholar
  24. 24.
    Juhnke, E., Dörnemann, T., Böck, D., Freisleben, B.: Multi-objective scheduling of BPEL workflows in geographically distributed clouds. In: 4th IEEE International Conference on Cloud Computing, pp. 412–419 (2011)Google Scholar
  25. 25.
    Song, B., Hassan, M.M., Huh, E.: A novel heuristic-based task selection and allocation framework in dynamic collaborative cloud service platform. In: 2nd IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 360–367 (2010)Google Scholar
  26. 26.
    Li, J., Qiu, M., Ming, Z., Quan, G., Qin, X., Gu, Z.: Online optimization for scheduling preemptable tasks on IaaS cloud systems. J. Parallel Distrib. Comput. 72(5), 666–677 (2012)Google Scholar
  27. 27.
    Li, J., Peng, J., Cao, X., Li, H.-Y.: A task scheduling algorithm based on improved ant colony optimization in cloud computing environment. Energy Procedia 13, 6833–6840 (2011)CrossRefGoogle Scholar
  28. 28.
    Taheri, J., Choon Lee, Y., Zomaya, A.Y., Siegel, H.J.: A bee colony based optimization approach for simultaneous job scheduling and data replication in grid environments. Comput. Oper. Res. 40(6), 1564–1578 (2013)CrossRefMathSciNetGoogle Scholar
  29. 29.
    Kolodziej, J., Xhafa, F.: Modern approaches to modeling user requirements on resource and task allocation in hierarchical computational grids. Int. J. Appl. Math. Comput. Sci. 21(2), 243–257 (2011)CrossRefzbMATHGoogle Scholar
  30. 30.
    Lei, Z., Yuehui, C., Runyuan, S., Shan, J., Bo, Y.: A task scheduling algorithm based on PSO for grid computing. Int. J. Comput. Intell. Res. 4(1), 37–43 (2008)Google Scholar
  31. 31.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  32. 32.
    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
  33. 33.
    Liu, H., Abraham, A., Snášel, V., McLoone, S.: Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments. Inf. Sci. 192, 228–243 (2012)CrossRefGoogle Scholar
  34. 34.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  35. 35.
    Engelbrecht, A.P.: Fundamentals of Computational Swarm Intelligence. Wiley, Hoboken (2005)Google Scholar
  36. 36.
    Engelbrecht, A.P.: Computational Intelligence: An introduction. Wiley, Hoboken (2007)CrossRefGoogle Scholar
  37. 37.
    Mahmoodabadi, M.J., Bagheri, A., Nariman-zadeh, N., Jamali, A.: A new optimization algorithm based on a combination of particle swarm optimization, convergence and divergence operators for single-objective and multi-objective problems. Eng. Optim. 44(10), 1–20 (2012)CrossRefMathSciNetGoogle Scholar
  38. 38.
    Gao, Y., Zhang, G., Lu, J., Wee, H.-M.: Particle swarm optimization for bi-level pricing problems in supply chains. J. Glob. Optim. 51(2), 245–254 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  39. 39.
    Lu, J., Zhang, G., Ruan, D.: Multi-Objective Group Decision Making: Methods, Software and Applications with Fuzzy Set Techniques. Imperial College Press, London (2007)CrossRefGoogle Scholar
  40. 40.
    Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)CrossRefGoogle Scholar
  41. 41.
    Islam, S., Keung, J., Lee, K., Liu, A.: Empirical prediction models for adaptive resource provisioning in the cloud. Futur. Gener. Comput. Syst. 28, 155–162 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Fahimeh Ramezani
    • 1
    Email author
  • Jie Lu
    • 1
  • Farookh Khadeer Hussain
    • 1
  1. 1.Decision Systems and e-Service Intelligence Lab, Faculty of Engineering and Information Technology, Centre for Quantum Computation and Intelligent Systems, School of SoftwareUniversity of Technology, SydneySydneyAustralia

Personalised recommendations