Quality of Service in Dynamic Resource Provisioning: Literature Review

  • MonikaEmail author
  • Om Prakash Sangwan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 835)


Resource provisioning is major problem in cloud computing because of the rapid growth in demand of resources and these resources are allocated according to dynamic nature of application. Unconstraint use of these resources can lead to two major problems namely under provisioning and over provisioning. Therefore, to implement provisioning is major concern in cloud computing. This paper has discussed and analyzed the methods incorporated in different research papers to understand objectives, performance on various QoS attributes and issues related to current cloud computing environment. This research work also presents details about prior research work, popular factors and future direction in resource provisioning.


Cloud computing Dynamic provisioning Soft computing QoS attributes Literature review 


  1. 1.
    Singh, S., Chana, I.: Q-aware: quality of service based cloud resource provisioning. Comput. Electr. Eng. 47, 138–160 (2015)CrossRefGoogle Scholar
  2. 2.
    Ran, Y., Yang, J., Zhang, S., Xi, H.: Dynamic IaaS computing resource provisioning strategy with QoS constraint. IEEE Trans. Serv. Comput. 10, 190–202 (2017)CrossRefGoogle Scholar
  3. 3.
    Xu, X., Tang, M., Tian, Y.-C.: QoS-guaranteed resource provisioning for cloud-based MapReduce in dynamical environments. Futur. Gener. Comput. Syst. 78(Part 1), 18–30 (2018)CrossRefGoogle Scholar
  4. 4.
    Malawski, M., Juve, G., Deelman, E., Nabrzyski, J.: Algorithms for cost- and deadline constrained provisioning for scientific workflow ensembles in IaaS clouds. Futur. Gener. Comput. Syst. 483, 1–18 (2015)CrossRefGoogle Scholar
  5. 5.
    Benfenatki, H., Silva, C.F.D., Kemp, G., Benharkat, A.-N., Ghodous, P., Maamar, Z.: MADONA: a method for automated provisioning of cloud-based component-oriented business applications. Serv. Oriented Comput. Appl. 11, 87–100 (2017)CrossRefGoogle Scholar
  6. 6.
    Subramanian, T., Savarimuthu, N.: Application based brokering algorithm for optimal resource provisioning in multiple heterogeneous clouds. Vietnam J. Comput. Sci. 3, 57–70 (2016)CrossRefGoogle Scholar
  7. 7.
    Vecchiola, C., Calheiros, R.N., Karunamoorthy, D., Buyya, R.: Deadline-driven provisioning of resources for scientific applications in hybrid clouds with Aneka. Futur. Gener. Comput. Syst. 28, 58–65 (2012)CrossRefGoogle Scholar
  8. 8.
    Toosi, A., Sinnott, R., Buyya, R.: Resource provisioning for data-intensive applications with deadline constraints on hybrid clouds using Aneka. Futur. Gener. Comput. Syst. 79, 765–775 (2017)CrossRefGoogle Scholar
  9. 9.
    Reddy, K.H.K., Mudali, G., Sinha Roy, D.: A novel coordinated resource provisioning approach for cooperative cloud market. J. Cloud Comput. Adv. Syst. Appl. 6, 1–17 (2017)CrossRefGoogle Scholar
  10. 10.
    Landa, R., Charalambides, M., Clegg, R.G., Griffin, D., Rio, M.: Self-tuning service provisioning for decentralized cloud applications. IEEE Trans. Netw. Serv. Manag. 13, 197–211 (2016)CrossRefGoogle Scholar
  11. 11.
    Leslie, L.M., Lee, Y.C., Zomaya, A.Y.: RAMP: reliability-aware elastic instance provisioning for profit maximization. J. Supercomput. 71, 4529–4554 (2015)CrossRefGoogle Scholar
  12. 12.
    Bahrpeyma, F., Haghighi, H., Zakerolhosseini, A.: A bipolar resource management framework for resource provisioning in Cloud’s virtualized environment. Appl. Soft Comput. 46, 487–500 (2016)CrossRefGoogle Scholar
  13. 13.
    Islam, S., Keung, J., Lee, K., Liu, A.: Empirical prediction models for adaptive resource provisioning in the cloud. Futur. Gener. Comput. Syst. 28(1), 155–162 (2012)CrossRefGoogle Scholar
  14. 14.
    Fakhfakh, F., Kacem, H.H., Kacem, A.H.: Dealing with structural changes on provisioning resources for deadline-constrained workflow. J. Super Comput. 73(7), 2896–2918 (2017)CrossRefGoogle Scholar
  15. 15.
    Eawna, M.H., Mohammed, S.H., El-Horbaty, E.-S.M.: Hybrid algorithm for resource provisioning of multi-tier cloud computing. Procedia Comput. Sci. 65(8), 682–690 (2015)CrossRefGoogle Scholar
  16. 16.
    Amiri, M., Derakhshi, M.-R.F., Khanli, L.M.: IDS fitted Q improvement using fuzzy approach for resource provisioning in cloud. J. Intell. Fuzzy Syst. 32(1), 229–240 (2017)CrossRefGoogle Scholar
  17. 17.
    Nikravesh, A.Y., Ajila, S.A., Lung, C.-H.: An autonomic prediction suite for cloud resource provisioning. J. Cloud Comput. Adv. 6(3), 1–20 (2017)Google Scholar
  18. 18.
    Wu, H., Zhang, W., Zhang, J., Wei, J., Huang, T.: A benefit-aware on-demand provisioning approach for multi-tier applications in cloud computing. Front. Comput. Sci. 7(4), 459–474 (2013)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Bi, J., et al.: Application-aware dynamic fine-grained resource provisioning in a virtualized cloud data center. IEEE Trans. Autom. Sci. Eng. 14(2), 1172–1184 (2017)CrossRefGoogle Scholar
  20. 20.
    Niu, S., Zhai, J., Ma, X., Tang, X., Chen, W., Zheng, W.: Building semi-elastic virtual clusters for cost-effective HPC cloud resource provisioning. IEEE Trans. Parallel Distrib. Syst. 27(7), 1915–1928 (2016)CrossRefGoogle Scholar
  21. 21.
    Li, X., Cai, Z.: Elastic resource provisioning for cloud workflow applications. IEEE Trans. Autom. Sci. Eng. 14(2), 1195–1210 (2017)CrossRefGoogle Scholar
  22. 22.
    Choi, Y., Lim, Y.: A cost-efficient mechanism for dynamic VM provisioning in cloud computing. In: Conference on Research in Adaptive and Convergent Systems, USA, pp. 344–349 (2014)Google Scholar
  23. 23.
    Prashanth, R.H., Pushpalatha, S.: Optimized resource provisioning for dynamic flow on cloud infrastructure using meta heuristic technique. In: Conference on Intelligent Systems and Control, pp. 1–8 (2016)Google Scholar
  24. 24.
    Leena Sri, R., Balaji, N.: Speculation based decision support system for efficient resource provisioning in cloud data center. Int. J. Comput. Intell. Syst. 10, 363–374 (2017)CrossRefGoogle Scholar
  25. 25.
    Eawna, M.H., Hamdy, S., El-Horbaty, E.S.M.: New trends of resource provisioning in multi-tier Cloud computing. In: Conference on Intelligent Computing and Information Systems, pp. 224–230 (2015)Google Scholar
  26. 26.
    Feller, E., Rilling, L., Morin, C.: Energy-aware ant colony based workload placement in clouds. In: IEEE/ACM International Conference on Grid Computing, pp. 26–33 (2011)Google Scholar
  27. 27.
    Florence, A.P., Shanthi, V., Florence, A.P., Shanthi, V.: A load balancing model using firefly algorithm in cloud computing. J. Comput. Sci. 10(7), 1156–1165 (2014)CrossRefGoogle Scholar
  28. 28.
    Budihal, S.V., Mallapur, J., Hiremath, T.C.: QoS based resource provision in cloud network: fuzzy approach. In: International Conference on Advances in Computer Science and Application, pp. 33–40 (2015)Google Scholar
  29. 29.
    Rao, J., Wei, Y., Gong, J., Xu, C.Z.: DynaQoS: model-free self-tuning fuzzy control of virtualized resources for QoS provisioning. In: IEEE International Workshop on Quality of Service, pp. 1–9 (2011)Google Scholar
  30. 30.
    Gurav, R., Patil, D.: Heterogeneity-aware resource provisioning using genetic algorithm. Int. J. Manag. Appl. Sci. 4(9), 39–44 (2016)Google Scholar
  31. 31.
    Dasgupta, K., Mandal, B., Dutta, P., Mandal, J.K., Dam, S.: A genetic algorithm (GA) based load balancing strategy for cloud Computing. In: International Conference on Computational Intelligence: Modelling Techniques and Applications, pp. 340–347 (2013)CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of CSEGuru Jambheshwar University of Science & TechnologyHisarIndia

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