Abstract
As the use of cloud computing continues to grow, issues related to cloud services such as resource allocation, security, virtual machine migration and quality of service (QoS) are also increasing. To overcome this, resource provisioning and stack adjusting were employed. In this paper, we suggest another approach that allocates resources with least waste and provides the greatest benefit. Here, the customer first submits a request for resources to the resource allocation manager, which forwards this request to the request tuner. It creates the charge and sends this to all resources connected in cloud system with the guide of grouping algorithm. After the GWO algorithm is applied for prioritization. The virtual machines are distributed for resources based on require therefore; the load on the server can be substantially reduced. In addition, allocating resources on virtual machines based on demand achieves a better response time and preparation time.
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References
Ardagna D, Panicucci B, Passacantando M (2012) Generalized NASH equilibria for the service provisioning problem in cloud systems. IEEE Trans Serv Comput 6(4):429–442
Arianyan E, Taheri H, Sharifian S (2015) Novel energy and SLA efficient resource management heuristics for consolidation of virtual machines in cloud data centers. Elsevier J Comput Electr Eng 47:222–240
Bahrpeyma F, Haghighi H, Zakerolhosseini A (2016) A bipolar resource management framework for resource provisioning in cloud’s virtualized environment. Elsevier J Appl Soft Comput 46:487–500
Banu M Uthaya, Subha M (2014) A survey on resource provisioning in cloud. Int J Eng Res Appl 4(2):30–35
Bhavani BH, Guruprasad HS (2014) Resource provisioning techniques in cloud computing environment: a survey. IJRCCT Int J Res Comput Commun Technol 3(3):395–401
Casalicchio E, Silvestri L (2013) Mechanisms for SLA provisioning in cloud-based service providers. Elsevier J Comput Netw 57(3):795–810
Chaisiri S, Lee BS, Niyato D (2012) Optimization of resource provisioning cost in cloud computing. IEEE Trans Serv Comp 5(2):164–177
Fahmi A, Abdullah S, Amin F, Ali A (2017a) Precursor selection for sol–gel synthesis of titanium carbide nanopowders by a new cubic fuzzy multi-attribute group decision-making model. Intell Syst. https://doi.org/10.1515/jisys-2017-0083
Fahmi A, Abdullah S, Amin F, Siddque N, Ali A (2017b) Aggregation operators on triangular cubic fuzzy numbers and its application to multi-criteria decision making problems. Intell Fuzzy Syst. https://doi.org/10.3233/JIFS-162007
Fahmi A, Abdullah S, Amin F, Ali A, Ahmad Khan W (2018a) Some geometric operators with triangular cubic linguistic hesitant fuzzy number and their application in group decision-making. Intell Fuzzy Syst. https://doi.org/10.3233/JIFS-18125
Fahmi A, Abdullah S, Amin F, Khan MSA (2018b) Trapezoidal cubic fuzzy number Einstein hybrid weighted averaging operators and its application to decision making. Soft Comput. https://doi.org/10.1007/s00500-018-3242-6
Fahmi A, Abdullah S, Amin F, Ali A (2018c) Weighted average rating (war) method for solving group decision making problem using triangular cubic fuzzy hybrid aggregation (tcfha). Punjab Univ J Math 50(1):23–34
Fahmi A, Abdullah S, Amin F, Ahmed R, Ali A (2018d) Triangular cubic linguistic hesitant fuzzy aggregation operators and their application in group decision making. Intell Fuzzy Syst. https://doi.org/10.3233/JIFS-171567
Ficco M, Esposito C, Palmieri F et al (2016) A coral-reefs and game theory-based approach for optimizing elastic cloud resource allocation. Elsevier J Fut Gener Comput Syst 1–10
Heilig E, Lalla-Ruiz E, Voß S (2016) A cloud brokerage approach for solving the resource management problem in multi-cloud environments. Elsevier J Comput Ind Eng 95:16–26
Huang D, He B, Miao C (2014) A survey of resource management in multi-tier web applications. IEEE Trans Commun Surv Tutor 16(3):1574–1590
Jiang Y, Perng CS, Li T et al (2013) Cloud analytics for capacity planning and instant VM provisioning. IEEE Trans Netw Serv Manag 10(3):312–325
Mann ZA (2015) Rigorous results on the effectiveness of some heuristics for the consolidation of virtual machines in a cloud data center. Elsevier J Fut Gener Comput Syst 51:1–6
Mei J, Li K, Ouyang A, Li K (2015) A profit maximization scheme with guaranteed quality of service in cloud computing. IEEE Trans Comput 64(11):3064–3078
Niu S, Zhai J, Ma X et al (2015) Building semi-elastic virtual clusters for cost-effective HPC cloud resource provisioning. IEEE Trans Parallel Distrib Syst 27:1915–1928
Soni A, Hasan M (2017) Time and cost based resource provisioning mechanism in cloud computing. Int J Adv Res Comp Sci 8(5):288–292
Tian W, Zhao Y, Xu M et al (2013) A toolkit for modeling and simulation of real-time virtual machine allocation in a cloud data center. IEEE Trans Autom Sci Eng 12(1):153–161
Vasoya S, Gadhavi L, Bhatia J, Bhavsar M (2016) Resource provisioning strategies in cloud: a survey. Resource 7(2):12–15
Wu L, Garg SK, Versteeg S et al (2013) SLA-based resource provisioning for software-as-a-service applications in cloud computing environments. IEEE Trans Serv Comput 7:465–485
Xiao W, Bao W (2015) Dynamic request redirection and resource provisioning for cloud-based video services under heterogeneous environment. IEEE Trans Parallel Distrib 27(7):1954–1967
Zhang J, Huang H, Wang X (2016) Resource provision algorithms in cloud computing: a survey. J Netw Comput Appl 64:23–42
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Meenakshi, A., Sirmathi, H. & Anitha Ruth, J. Cloud computing-based resource provisioning using k-means clustering and GWO prioritization. Soft Comput 23, 10781–10791 (2019). https://doi.org/10.1007/s00500-018-3632-9
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DOI: https://doi.org/10.1007/s00500-018-3632-9