Abstract
The ‘cloud computing’ technology is requisite for modern technology. It has a notable facet called Resource Allocation. This given paper proposes Hybridized Optimization algorithm that is the combination of ‘Shuffled Frog Leaping Algorithm’ (SFLA) and ‘Cuckoo Search’ (CS) Algorithm. This technique overcomes the limitations of the existing works like HABCCS algorithm, GTS algorithm task, krill herd algorithm, also combines the advantages of SFLA and CS. In this method, SFLA section performs the preceding steps; initializing the request size, generating requests, and estimate fitness value of SFLA, sorting, dividing and evaluating the requests of user. The SFLA encompasses the advantage of higher speed convergence and easier implementation, with the capacity of having global optimization and are utilized widely in numerous areas. Then, CS algorithm executes operations like initializing, generating, evaluate fitness function, modification and then evaluating the new solutions. The CS algorithms possess the advantage of easier evaluation and it is utilized in complex situations. In this given system, the request speed, sizes are evaluated. Those evaluations are utilized in allocating the resources on the server-side. Less computed times are consumed in this technique. An experimental outcome displays that the approach performs well in contrasting with other related approaches.
Similar content being viewed by others
References
Xiaoying, T., Dan, H., Yuchun, G., Changjia, C.: Dynamic resource allocation in cloud download service. J. China Univ. Posts Telecommun. 24(5), 53–59 (2017)
Pradhan, P., Prafulla, B.K., Ray, B.N.B.: Modified round robin algorithm for resource allocation in cloud computing. Procedia Comput. Sci. 85, 878–890 (2016)
Mingxin, W.: Research on improvement of task scheduling algorithm in cloud computing. Appl. Math. Inf. Sci. 9(1), 507–516 (2015)
Lee, H.M., Jeong, Y.S., Jang, H.J.: Performance analysis based resource allocation for green cloud computing. J. Supercomput. 69(3), 1013–1026 (2014)
Madni, S.H.H., Latiff, M.S.A., Coulibaly, Y.: Recent advancements in resource allocation techniques for cloud computing environment: a systematic review. Clust. Comput. 20(3), 2489–2533 (2017)
Kumar, N., Saxena, S.: A preference-based resource allocation in cloud computing systems. Procedia Comput. Sci. 57, 104–111 (2015)
Xue, C.T.S., Xin, F.T.W.: benefits and challenges of the adoption of cloud computing in business. Int. J. Cloud Comput. Serv. Arch. (IJCCSA) 6(6), 1–15 (2016)
Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: issues and challenges. J. Grid Comput. 14(2), 217–264 (2016)
Ergu, D., Kou, G., Peng, Y., Shi, Y., Shi, Yu.: The analytic hierarchy process: task scheduling and resource allocation in cloud computing environment. J. Supercomput. 64(3), 835–848 (2013)
Kolhar, M., Abd El-atty, S.M., Rahmath, M.: Storage allocation scheme for virtual instances of cloud computing. Neural Comput. Appl. 28(6), 1397–1404 (2017)
Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Zeadally, S., et al.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016)
Sudeepa, R., Guruprasad, H.S.: Resource allocation in cloud computing. Int. J. Mod. Commun. Technol. Res. 2(4), 19–21 (2014)
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)
Sharma, N., Guddeti, R.M.: Multi-objective energy efficient virtual machines allocation at the cloud data center. IEEE Trans. Serv. Comput. (2016). https://doi.org/10.1186/s13677-017-0086-z
Kayalvili, S., Selvam, M.: Hybrid SFLA-GA algorithm for an optimal resource allocation in cloud. Clust. Comput. (2018). https://doi.org/10.1007/s10586-018-2011-8
Pillai, P.S., Rao, S.: Resource allocation in cloud computing using the uncertainty principle of game theory. IEEE Syst. J. 10(2), 637–648 (2016)
Mireslami, S., Rakai, L., Far, B.H., Wang, M.: Simultaneous cost and QoS optimization for cloud resource allocation. IEEE Trans. Netw. Serv. Manag. 14(3), 676–689 (2017)
Zheng, H., Feng, Y., Tan, J.: A hybrid energy-aware resource allocation approach in cloud manufacturing environment. IEEE Access 5, 12648–12656 (2017)
Chen, M., Huang, S., Fu, X., Liu, X., He, J.: Statistical model checking-based evaluation and optimization for cloud workflow resource allocation. IEEE Trans. Cloud Comput. (2016). https://doi.org/10.1109/TCC.2016.2586067
Di, S., Wang, C.L., Cappello, F.: Adaptive algorithm for minimizing cloud task length with prediction errors. IEEE Trans. Cloud Comput. 2(2), 194–207 (2014)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Durgadevi, P., Srinivasan, S. Resource Allocation in Cloud Computing Using SFLA and Cuckoo Search Hybridization. Int J Parallel Prog 48, 549–565 (2020). https://doi.org/10.1007/s10766-018-0590-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10766-018-0590-x