Dynamic Resource Management Through Task Migration in Cloud

  • K. S. AryaEmail author
  • P. V. Divya
  • K. R. Remesh Babu
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 26)


Cloud computing allows sharing of centralized data storage, data-processing of tasks, online access to a computing resources and services. İn cloud environment, one of the critical issue is resource management. Over and under utilization of resources will affect the job response time, provider’s profit and Quality of Service (QoS). In order to improve resource utilization, the computational load among distinct nodes needs to be distributed evenly. An efficient and effective scheduling method assures that all the nodes are uniformly loaded with user’s computation requests. In this paper dynamic method for task allocation and resource allocation is introduced to reduce virtual machine migrations and execution time. The proposed algorithm is simulated and results are compared with the existing algorithm.


Cloud computing Scheduling Load balancing VM migration Datacenter 


  1. 1.
    Alakeel, A.M.: A guide to dynamic load balancing in distributed computer systems. Int. J. Comput. Sci. Netw. Secur. 10(6), 153–160 (2010)Google Scholar
  2. 2.
    Lu, K., Yahyapour, R., Wieder, P., Kotsokalis, C., Yaqub, E., Jehangiri, A.: QoS-aware VM placement in multi-domain service level agreements scenarios. In: IEEE 6th Cloud Computing (CLOUD), pp. 661–668 (2013)Google Scholar
  3. 3.
    Sahu, Y., Pateriya, R.K., Gupta, R.K.: Cloud server optimization with load balancing and green computing techniques using dynamic compare and balance algorithm. In: IEEE 5th International Conference on Computational Intelligence and Communication Networks (CICN), pp. 527–531 (2013)Google Scholar
  4. 4.
    Ghafari, S.M., Fazeli, M., Patooghy, A., Rikhtechi, L.: Bee-MMT: a loadbalancing method for power consumption management in cloud computing. In: Sixth IEEE International Conference on Contemporary Computing (IC3), pp. 76–80 (2013)Google Scholar
  5. 5.
    Domanal, S.G., Reddy, G.R.M.: Load balancing in cloud computing using modified throttled algorithm. In: IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), pp. 1–7 (2013)Google Scholar
  6. 6.
    Remesh Babu, K.R., Samuel, P.: Virtual machine placement for improved quality in IaaS cloud. In: 4th IEEE International Conference on Advances in Computing and Communications (ICACC), pp. 190–194Google Scholar
  7. 7.
    Nishant, K., Sharma, P., Krishna, V., Pratap Singh, C.G.K., Nitin, Rastogi, R.: Load balancing of nodes in cloud using ant colony optimization. In: Proceedings of 14th IEEE International Conference on Modeling and Simulation, pp. 3–8 (2012)Google Scholar
  8. 8.
    Wang, S.-C., Yan, K.-Q., Liao, W.-P., Wang, S.-S.: Towards a load balancing in a three-level cloud computing network. In: Proceedings of 3rd IEEE International conference on Computer Science and Information Technology (ICCSIT), Vol. 1, pp. 108–113 (2010)Google Scholar
  9. 9.
    Lin, C.-C., Liu, P., Wu, J.-J.: Energy-aware virtual machine dynamic provision and scheduling for cloud computing. 2011 IEEE International Conference on Cloud Computing (CLOUD), pp. 736–737Google Scholar
  10. 10.
    Yun, D., Wub, C.Q., Gu, Y.: An integrated approach to workflow mapping and task scheduling for delay minimization in distributed environments. J. Parallel Distrib. Comput. 84, 51–64 (2015)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Mezmaz, M., Melab, N., Kessaci, Y., Lee, Y.C., Talbi, E.-G., Zomaya, A.Y., Tuyttens, D.: A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. J. Parallel Distrib. Comput. 71(11), 1497–1508 (2011)CrossRefGoogle Scholar
  13. 13.
    Babu, K.R.R., Samuel, P.: Interference aware prediction mechanism for auto scaling in cloud. Comput. Electr. Eng. 69, 351–363 (2017). Scholar
  14. 14.
    Madni, S.H.H., Latiff, M.S.A., Coulibaly, Y., Abdulhamid, S.M.: Review: resource scheduling for infrastructure as a service (IaaS) in cloud computing: challenges and opportunities. J. Netw. Comput. Appl. 68, 173–200 (2016)CrossRefGoogle Scholar
  15. 15.
    Kumar, M., Sharma, S.C.: Deadline constrained based dynamic load balancing algorithm with elasticity in cloud environment. Elsevier Comput. Electr. Eng. 1–17 (2017)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Information TechnologyGovernment Engineering CollegeIdukkiIndia

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