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Fusion: Practice and Applications
Volume 6 , Issue 1, PP: 08-16 , 2021 | Cite this article as | XML | Html |PDF


A Novel Fuzzy Clustering with Metaheuristic based Resource Provisioning Technique in Cloud Environment

Authors Names :   Ahmed N. Al-Masri   1 *     Manal Nasir   2  

1  Affiliation :  American University in the Emirates, Dubai, UAE

    Email :  ahmed.almasri@aue.ae

2  Affiliation :  Information Technology Department, Georgia Gwinnett College, USA

    Email :  Mnasir1@ggc.edu

Doi   :   https://doi.org/10.54216/FPA.060102

Received: February 07, 2021 Accepted: May 11, 2021

Abstract :

Cloud Computing (CC) becomes a commonly available tool to enable quick, on-demand services from a shared pool of configurable computing resources which can be allocated and utilized. Resource provisioning is a major issue in CC environment which ensures guaranteed outcomes on the applications related to CC. This study introduces an efficient fuzzy c-means clustering (FCM) with hybrid grey wolf optimization (GWO) and differential evolution (DE) algorithm, called FCM-GWODE for resource provisioning in cloud environment. The aim of the FCM-GWODE technique is to allocate the resources in such a way that the resource utilization can be accomplished. In addition, the FCM technique with metaheuristics is applied to partition the resources and scalable searching process can be minimized. Moreover, the GWODE algorithm is derived by resolving the local optima issue of the GWO and improve the population diversity using DE. A comprehensive simulation process takes place using CloudSim tool and the results are inspected interms of several evaluation metrics. The simulation results highlighted the supremacy of the FCM-GWODE technique over the other methods.

Keywords :

Resource provisioning , Cloud computing , Fuzzy clustering , Hybrid algorithms , Resource utilization , GWO algorithm.

References :


[1]      Varshney, S., Sandhu, R. and Gupta, P.K., 2019, April. QoS based resource provisioning in cloud computing environment: a technical survey. In International conference on advances in computing and data sciences (pp. 711-723). Springer, Singapore.

[2]      Ralha, C.G., Mendes, A.H., Laranjeira, L.A., Araújo, A.P. and Melo, A.C., 2019. Multiagent system for dynamic resource provisioning in cloud computing platforms. Future Generation Computer Systems, 94, pp.80-96.

[3]      Shahidinejad, A., Ghobaei-Arani, M. and Masdari, M., 2021. Resource provisioning using workload clustering in cloud computing environment: a hybrid approach. Cluster Computing, 24(1), pp.319-342.

[4]      Suresh, A. and Varatharajan, R., 2019. Competent resource provisioning and distribution techniques for cloud computing environment. Cluster Computing, 22(5), pp.11039-11046.

[5]      Aslanpour, M.S., Dashti, S.E., Ghobaei-Arani, M. and Rahmanian, A.A., 2018. Resource provisioning for cloud applications: a 3-D, provident and flexible approach. The Journal of Supercomputing, 74(12), pp.6470-6501.

[6]      Ma, X., Wang, S., Zhang, S., Yang, P., Lin, C. and Shen, X.S., 2019. Cost-efficient resource provisioning for dynamic requests in cloud assisted mobile edge computing. IEEE Transactions on Cloud Computing.

[7]      Khorsand, R., Ghobaei‐Arani, M. and Ramezanpour, M., 2018. FAHP approach for autonomic resource provisioning of multitier applications in cloud computing environments. Software: Practice and Experience, 48(12), pp.2147-2173.

[8]      Nigam, S. and Bajpai, A., 2017. An Optimal Resource Provisioning Algorithm for Cloud Computing Environment. Oriental Journal of Computer Science and Technology, 10(2), p.371.

[9]      Xu, X., Mo, R., Dai, F., Lin, W., Wan, S. and Dou, W., 2019. Dynamic resource provisioning with fault tolerance for data-intensive meteorological workflows in cloud. IEEE Transactions on Industrial Informatics, 16(9), pp.6172-6181.

[10]   Toosi, A.N., Sinnott, R.O. and Buyya, R., 2018. Resource provisioning for data-intensive applications with deadline constraints on hybrid clouds using Aneka. Future Generation Computer Systems, 79, pp.765-775.

[11]   Bhardwaj, T. and Sharma, S.C., 2018. Fuzzy logic-based elasticity controller for autonomic resource provisioning in parallel scientific applications: A cloud computing perspective. Computers & Electrical Engineering, 70, pp.1049-1073.

[12]   Moreno-Vozmediano, R., Montero, R.S., Huedo, E. and Llorente, I.M., 2019. Efficient resource provisioning for elastic Cloud services based on machine learning techniques. Journal of Cloud Computing, 8(1), pp.1-18.

[13]   Ghobaei-Arani, M., Jabbehdari, S. and Pourmina, M.A., 2018. An autonomic resource provisioning approach for service-based cloud applications: A hybrid approach. Future Generation Computer Systems, 78, pp.191-210.

[14]   Shahidinejad, A., Ghobaei-Arani, M. and Masdari, M., 2021. Resource provisioning using workload clustering in cloud computing environment: a hybrid approach. Cluster Computing, 24(1), pp.319-342. 

[15]   Shahidinejad, A. and Ghobaei‐Arani, M., 2020. Joint computation offloading and resource provisioning for e dge‐cloud computing environment: A machine learning‐based approach. Software: Practice and Experience, 50(12), pp.2212-2230. 

[16]   Vinothiyalakshmi, P. and Anitha, R., 2021. Efficient dynamic resource provisioning based on credibility in cloud computing. Wireless Networks, 27(3), pp.2217-2229. 

[17]   Mazidi, A., Mahdavi, M. and Roshanfar, F., 2021. An autonomic decision tree‐based and deadline‐constraint resource provisioning in cloud applications. Concurrency and Computation: Practice and Experience, 33(10), p.e6196.

[18]   Asghari, A., Sohrabi, M.K. and Yaghmaee, F., 2021. Task scheduling, resource provisioning, and load balancing on scientific workflows using parallel SARSA reinforcement learning agents and genetic algorithm. The Journal of Supercomputing, 77(3), pp.2800-2828.

[19]   Porkodi, V., Singh, A.R., Sait, A.R.W., Shankar, K., Yang, E., Seo, C. and Joshi, G.P., 2020. Resource provisioning for cyber–physical–social system in cloud-fog-edge computing using optimal flower pollination algorithm. IEEE Access, 8, pp.105311-105319.


[20]   Sivaram, M., Lydia, E.L., Pustokhina, I.V., Pustokhin, D.A., Elhoseny, M., Joshi, G.P. and Shankar, K., 2020. An optimal least square support vector machine based earnings prediction of blockchain financial products. IEEE Access, 8, pp.120321-120330.

Cite this Article as :
Ahmed N. Al-Masri , Manal Nasir, A Novel Fuzzy Clustering with Metaheuristic based Resource Provisioning Technique in Cloud Environment, Fusion: Practice and Applications, Vol. 6 , No. 1 , (2021) : 08-16 (Doi   :  https://doi.org/10.54216/FPA.060102)