Skip to main content
Log in

Design and development of exponential lion algorithm for optimal allocation of cluster resources in cloud

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Cloud computing is one of the new age technologies which has great prominent factor in the development of the enterprises and markets. The major exertion in the cloud computing is related to the resource being allocated. The optimal resource allocation is one which allocates the best suitable cluster resources for the task to execute with consideration of the different parameters, such as time, cost, and scalability, makespan, reliability, availability, throughput, resource utilization and so on. In this paper, a resource allocation optimization method in the cloud computing based on the exponential lion algorithm is proposed. The exponential lion based resource allocation for cloud computing taken into account saves the execution time, run time, and improves the revenue for the cloud provider. The proposed E-Lion based resource allocation approaches are compared with the PSO, SL-PSO, and Lion using the performance measures profit, CPU utilization rate, and memory utilization rate. The simulations of the experiments show that the algorithm in this paper has improved the algorithm performance efficiently with profit maximal profit of 38.74 and minimal CPU and memory utilization rate of 0.00031, and 0.00036 respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Kundra, V.: Federal cloud computing strategy (2011)

  2. Gan, G.N., Huang, T.L., Gao, S.: Genetic simulated annealing algorithm for task scheduling based on cloud computing environment. In: Proceedings of International Conference on Intelligent Computing and Integrated Systems, pp. 60–63 (2010)

  3. Bhardwaj, S., Jain, L., Jain, S.: Cloud computing: a study of infrastructure as a service (IaaS). Int. J. Eng. Inf. Technol. 2(1), 60–63 (2010)

    Google Scholar 

  4. Vakilinia, S., Ali, M.M., Qiu, D.: Modeling of the resource allocation in cloud computing centers. Comput. Netw. 91, 453–470 (2015)

    Article  Google Scholar 

  5. Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25(6), 599–616 (2009)

    Article  Google Scholar 

  6. Manvi, S.S., Krishna Shyam, G.: Resource management for infrastructure as a service (IaaS) in cloud computing: a survey. J. Netw. Comput. Appl. 41, 424–440 (2014)

    Article  Google Scholar 

  7. Mustafa, S., Nazir, B., Hayat, A., Madani, S.A.: Resource management in cloud computing: taxonomy, prospects, and challenges. Comput. Electr. Eng. 47, 186–203 (2015)

    Article  Google Scholar 

  8. Durao, F., Carvalho, J.F.S., Fonseka, A., Garcia, V.C.: A systematic review on cloud computing. J. Supercomput. 68, 1321–1346 (2014)

    Article  Google Scholar 

  9. Yang, H., Tate, M.: A descriptive literature review and classification of cloud computing research. Commun. Assoc. Inf. Syst. 31, 35–60 (2012)

    Google Scholar 

  10. Zheng, K., Meng, H., Chatzimisios, P., Lei, L., Shen, X.: An SMDP-based resource allocation in vehicular cloud computing systems. IEEE Trans. Ind. Electron. 62(12), 7920–7928 (2015)

    Article  Google Scholar 

  11. Lu, D., Ma, J., Xi, N.: A universal fairness evaluation framework for resource allocation in cloud computing. Netw. Technol. Appl. 12(5), 113–122 (2015)

    Google Scholar 

  12. Maguluri, S.T., Srikant, R., Ying, L.: Heavy traffic optimal resource allocation algorithms for cloud computing clusters. Perform. Eval. 81, 20–39 (2014)

    Article  Google Scholar 

  13. Saraswathi, A.T., Kalaashri, Y.R., Padmavathi, S.: Dynamic resource allocation scheme in cloud computing. Procedia Comput. Sci. 47, 30–36 (2015)

    Article  Google Scholar 

  14. Alasaad, A., Shafiee, K., Behairy, H.M., Leung, V.C.: Innovative schemes for resource allocation in the cloud for media streaming applications. IEEE Trans. Parallel Distrib. Syst. 26(4), 1021–1033 (2015)

    Article  Google Scholar 

  15. Papagianni, C., Leivadeas, A., Papavassiliou, S., Maglaris, V., Cervello-Pastor, C., Monje, A.: On the optimal allocation of virtual resources in cloud computing networks. IEEE Trans. Comput. 62(6), 1060–1071 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  16. Xiao, Z., Song, W., Chen, Q.: Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans. Parallel Distrib. Syst. 24(6), 1107–1117 (2013)

    Article  Google Scholar 

  17. Ibarraki, T., Katoh, N.: Resource Allocation Problems. MIT Press, Cambridge (1988)

    Google Scholar 

  18. Bjorndal, A.M.H., Caprara, A., Cowling, P.I., Croce, D., Lourenco, H., Malucelli, F., Orman, A.J., Pisinger, D., Rego, C., Salazar, J.J.: Some thoughts on combinatorial optimization. Eur. J. Oper. Res. 83(2), 253–270 (1995)

    Article  MATH  Google Scholar 

  19. Hammer, P.L., Hansen, P., Simeone, B.: Roof duality complementation and persistency in quadratic 0–1 optimization. Math. Program. 28, 121–155 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  20. Lee, Z.J., Lee, C.Y.: A hybrid search algorithm with heuristics for resource allocation problem. Inf. Sci. 173, 155–167 (2005)

    Article  Google Scholar 

  21. Zuo, X., Zhang, G., Tan, W.: Self-adaptive learning PSO-based deadline constrained task scheduling for hybrid IaaS cloud. IEEE Trans. Autom. Sci. Eng. 11(2), 564–573 (2014)

    Article  Google Scholar 

  22. Awad, A.I., El-Hefnawy, N.A., Abdel kader, H.M.: Enhanced particle swarm optimization for task scheduling in cloud computing environments. Proc. Int. Conf. Commun. Manag. Inf. Technol. 65, 920–929 (2015)

    Google Scholar 

  23. Saccucci, M.S., Amin, R.W., Lucas, J.M.: Exponentially weighted moving average control schemes with variable sampling intervals. Commun. Stat. Simul. Comput. 21(3), 627–657 (1992)

    Article  MathSciNet  Google Scholar 

  24. Wang, B., Jin, X.P., Cheng, B.: Lion pride optimizer: an optimization algorithm inspired by lion pride behavior. Sci. China Inf. Sci. 55(10), 2369–2389 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  25. Rajakumar, B.: The Lion’s algorithm: a new nature-inspired search algorithm. Procedia Technol. 6, 126–135 (2012)

    Article  Google Scholar 

  26. Pandey, S., Wu, L., Guru, S.M., Buyya, R.: A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: The Cloud Computing and Distributed Systems (CLOUDS) Laboratory, University of Melbourne

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to J. Devagnanam.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Devagnanam, J., Elango, N.M. Design and development of exponential lion algorithm for optimal allocation of cluster resources in cloud. Cluster Comput 22 (Suppl 1), 1385–1400 (2019). https://doi.org/10.1007/s10586-018-1976-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-1976-7

Keywords

Navigation