Automatic Control and Computer Sciences

, Volume 53, Issue 1, pp 45–55 | Cite as

PSO-based Load Balancing Method in Cloud Computing

  • R. M. Alguliyev
  • Y. N. Imamverdiyev
  • F. J. AbdullayevaEmail author


The optimization of task scheduling process in the cloud-computing environment is the multi-criteria NP-hard problem. The paper proposes a PSO based αPSO-TBLB (Task Based Load Balancing) load balancing method. The method provides an optimal migration of tasks causing overload from loaded virtual machines to corresponding virtual machines in the cloud environment. The minimization of task execution and transfer time in the suggested optimization model are chosen as target functions. The experimental testing of the suggested approach is carried out in Cloudsim and Jswarm software tools. As a result of the simulation based on proposed method found, the optimal solution for the scheduling of tasks and equal distribution of tasks to VMs (Virtual Machines) has been provided, and less time consumption has been achieved for the assignment process of tasks to VMs.


cloud computing Particle Swarm Optimization (PSO) virtual machine migration task scheduling Cloudsim Jswarm 


  1. 1.
    Metri, G., Srinivasaraghavan, S., Shi, W., and Brockmeyer, M., Experimental analysis of application specific energy efficiency of datacenters with heterogeneous servers, Proc. of the IEEE 5th International Conference on Cloud Computing, 2012, pp. 786–793.Google Scholar
  2. 2.
    Vaquero, L.M., Rodero-Merino, L., Caceres, J., and Lindner, M., A break in the clouds: Towards a cloud definition, ACM SIGCOMM Comput. Commun. Rev., 2008, vol. 39, no. 1, pp. 50–55.CrossRefGoogle Scholar
  3. 3.
    Ramezani, F., Lu, J., and Hussain, F.K., Task-based system load balancing in cloud computing using Particle Swarm Optimization, Int. J. Parallel Program., 2013, vol. 42, no. 5, pp. 739–754.CrossRefGoogle Scholar
  4. 4.
    Guo, L., Zhao, S., Shen, S., and Jiang, C., Task scheduling optimization in cloud computing based on heuristic algorithm, J. Networks, 2012, vol. 7, no. 3, pp. 547–553.Google Scholar
  5. 5.
    Alguliev, R.M., Alyguliev, R.M., and Alekperov, R.K., An approach to optimal task assignment in a distributed system, J. Autom. Inf. Sci., 2004, vol. 36, no. 10, pp. 51–55.CrossRefGoogle Scholar
  6. 6.
    Ramezani, F., Lu, J., and Hussain, F., Task scheduling optimization in cloud computing applying multi-objective particle swarm optimization, Serv.-Oriented Comput., 2013, vol. 8274, pp. 237–251.CrossRefGoogle Scholar
  7. 7.
    Ramezani, F., Lu, J., and Hussain, F.K., Task-based system load balancing in cloud computing using particle swarm optimization, Int. J. Parallel Program., 2014, vol. 42, no. 5, pp. 739–754.Google Scholar
  8. 8.
    Ramezani, F., Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments, World Wide Web, 2015, vol. 18, no. 6, pp. 1737–1757.CrossRefGoogle Scholar
  9. 9.
    Babu, L.D. and Krishna, P.V., Honey bee behavior inspired load balancing of tasks in cloud computing environments, Appl. Soft Comput., 2013, vol. 13, no. 5, pp. 2292–2303.CrossRefGoogle Scholar
  10. 10.
    Babu, K.R. and Samuel, P., Enhanced bee colony algorithm for efficient load balancing and scheduling in cloud, in Innovations in Bio-Inspired Computing and Applications, Snášel, V., Abraham, A., Krömer, P., Pant, M., and Muda, A., Eds., Springer, Cham, 2016, pp. 67–78.Google Scholar
  11. 11.
    Banerjee, S., Adhikari, M., Kar, S., and Biswas, U., Development and analysis of a new cloudlet allocation strategy for QoS improvement in cloud, Arabian J. Sci. Eng., 2015, vol. 40, no. 5, pp. 1409–1425.MathSciNetCrossRefGoogle Scholar
  12. 12.
    Sajjan, R.S. and Biradar, R.Y., Task based approach towards Load Balancing in Cloud Environment, Int. J. Comput. Appl., 2018, vol. 179, no. 31, pp. 39–43.Google Scholar
  13. 13.
    Madhumathi, C. and Ganapathy, G., An effective time based load balancer for an academic cloud environment, International Conference on Computer Communication and Informatics (ICCCI), Coimbator, 2015, pp. 1–6.Google Scholar
  14. 14.
    Kaur, R. and Ghumman, N.S., Task-based load balancing algorithm by efficient utilization of VMs in cloud computing, in Big Data Analytics, 2018, Singapore: Springer, pp. 55–61.Google Scholar
  15. 15.
    Devi, D.C. and Uthariaraj, V.R., Load balancing in cloud computing environment using improved weighted round robin algorithm for nonpreemptive dependent tasks, Sci. World J., 2016, vol. 2016, pp. 1–14.CrossRefGoogle Scholar
  16. 16.
    Liu, Y., Zhang, C., Li, B., and Niu, J., DeMS: A hybrid scheme of task scheduling and load balancing in computing clusters, J. Network Comput. Appl., 2015, pp. 1–8.Google Scholar
  17. 17.
    Cho, K.M., Tsai, P.W., Tsai, C.W., and Yang, C.S., A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing, Neural Comput. Appl., 2015, vol. 26, no. 6, pp. 1297–1309.CrossRefGoogle Scholar
  18. 18.
    Alguliev, R.M., Aliguliyev, R.M., and Mehdiyev, C.A., An optimization approach to automatic generic document summarization, Comput. Intell., 2013, vol. 29, no. 1, pp. 129–155.MathSciNetCrossRefGoogle Scholar
  19. 19.
    Aliguliyev, R.M., Clustering techniques and discrete particle swarm optimization algorithm for multi-document summarization, Comput. Intell., 2010, vol. 26, no. 4, pp. 420–448.MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Cakar, T. and Koker, R., Solving single machine total weighted tardiness problem with unequal release date using neurohybrid particle swarm optimization approach, Comput. Intell. Neurosci., 2015, vol. 2015, pp. 1–13.CrossRefGoogle Scholar

Copyright information

© Allerton Press, Inc. 2019

Authors and Affiliations

  • R. M. Alguliyev
    • 1
  • Y. N. Imamverdiyev
    • 1
  • F. J. Abdullayeva
    • 1
    Email author
  1. 1.Institute of Information Technology Azerbaijan National Academy of SciencesBakuAzerbaijan

Personalised recommendations