Abstract
Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and a resource allocation methodology. The existing methodologies for dynamic resource allocation do not provide effective performance isolation between the VM and Artificial Demand Analysis machines since it gets affected by interferences. To overcome these issues, this paper proposes a conceptual model and an effective algorithm to achieve dynamic resource allocation by migrating tasks or requests in VMs. At first, task demands from the multiple users go to the feature extraction process. In feature extraction, features of the user's tasks and cloud server are extracted. Next both features are reduced by using Modified PCA algorithm to reduce the dynamic resource allocation processing time. Finally, both the features are combined and resource allocation is performed using Hybrid Particle Swarm Optimization and Modified Genetic Algorithm (HPSO-MGA). Then the optimized task has been scheduled to particular VM for allocating the resources. The experimental result of the proposed resource allocation methodology indicates better performance when compared with the existing methods Firefly and Krill herd Load Balancing (LB). For 100 VMs the reliability of HPSO-MGA is 0.87 but the exiting krill herd LB and IDSA gives 0.78 and 0.85, which is lower than the proposed one.
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Abbreviations
- VM:
-
Virtual machine
- PCA:
-
Principal component analysis
- HPSO-MGA:
-
Hybrid particle swarm optimization and modified genetic algorithm
- Krill herd (LB):
-
Krill herd load balancing
- VMM:
-
Virtual machine monitors
- DPRA:
-
Dynamic power-saving resource allocation
- PM:
-
Physical machine
- SLA:
-
Service level-agreement
- DBN:
-
Deep belief networks
- OVMAP:
-
Online incentive-compatible mechanism
- PSO:
-
Particle swarm optimization
- MPCA:
-
Modified PCA
- IDSA:
-
Improved differential search algorithm
References
Kumar, N., Agarwal, S.: An analytical model for dynamic resource allocation framework in cloud environment. Res. J. Recent Sci. 3, 1–6 (2014)
Gawali Anita, D., Sonkar, S.K.: Dynamic resource allocation using virtualization technology in cloud computing. Int. J. Adv. Res. Comput. Eng. Technol. 4, 5 (2015)
Patil, S.S., Bhavani, K.: Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans. Parallel Distrib. Syst. 3(6), 2249 (2014)
Kumar, K.P., Kumar, S.A., Jagadeeshan, D.: Effective load balancing for dynamic resource allocation in cloud computing. Int. J. Innov. Res. Comput. Commun. Eng. 2(3), 758–762 (2013)
Gawali, M.B., Shinde, S.K.: Task scheduling and resource allocation in cloud computing using a heuristic approach. J. Cloud Comput. 7(1), 4 (2018)
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)
Rekha, R., Saroha, V.: A review paper on dynamic resource allocation in cloud environment. Int. J. Res. Appl. Sci. Eng. Technol. 5, 5856 (2017)
Verma, M., Gangadharan, G.R., Narendra, N.C., Vadlamani, R., Inamdar, V., Ramachandran, L., Calheiros, R.N., Buyya, R.: Dynamic resource demand prediction and allocation in multi-tenant service clouds. Concurr. Comput. 28(17), 4429–4442 (2016)
Shelke, R., Rajani, R.: Dynamic resource allocation in Cloud Computing. Int. J. Eng. Res. Technol. (IJERT) 2(10), 1–4 (2013)
Alsadie, D., Tari, Z., Alzahrani, E.J., Zomaya, A.Y.: Dynamic resource allocation for an energy efficient vm architecture for cloud computing. In Proceedings of the Australasian Computer Science Week Multiconference, pp. 16. ACM (2018).
Arul Mary, M.A., Jahir Husain, A., Dhasarathan, N.: Dynamic resource allocation to support server consolidation. Int. J. Pure Appl. Math. 119(16), 3759–3762 (2018)
Lavanya, M., Vaithiyanathan, V.: Load prediction algorithm for dynamic resource allocation. Indian J. Sci. Technol. 8, 35 (2015)
Selokar, A., Zade, S.D., Chavan, C.U.: Survey on dynamic resource allocation using virtual machines for cloud computing environment. Int. J. Adv. Res. Comput. Commun. Eng. 3(5), 6449 (2014)
Rohini, A., Sudalai Muthu, T.: Weight-based approach for improving the accuracy of relationship in social network. J. Adv. Res. Dyn. Control Syst. 11(8), 188–192 (2020)
Pandiaraj, S., Sudalai Muthu, T.: Prioritization of replica for replica replacement in data grid. Int. J. Recent Technol. Eng. 7(5), 245–248 (2019)
Natarajan, S., Pugazendi, R.: Survey on dynamic resource allocation techniques in cloud environment. Int. J. Comput. Sci. Mob. Comput. 3(8), 395–403 (2014)
Sudalai Muthu, T., Vadivel, R.: A quantified weight based approach for replica replacement in data grid. In: 5th IEEE International Conference on Parallel, Distributed and Grid Computing (PDGC-2018). IEEE, Solan (2018). ISBN No. 978-1-5386-6026-3/18/$31©2018
Sudalai Muthu, T., Ramesh, A., Vadivel, R., Vasanth, G.: A novel protocol for secure data storage in data grid environment. In: Proceedings of International Conference on Trendz in Information Science and Computing-2010. IEEE (2010). ISBN No : 978-1-4244-9008-0/10/$26.00 ©2010
Chou, L.D., Chen, H.F., Tseng, F.H., Chao, H.C., Chang, Y.J.: DPRA: dynamic power-saving resource allocation for cloud data center using particle swarm optimization. IEEE Syst. J. 12(2), 1554–1565 (2016)
Ma, A., Gao, Y., Huang, L., Zhang, B.: Improved differential search algorithm based dynamic resource allocation approach for cloud application. Neural Comput. Appl. 31(8), 3431–3442 (2017)
Mashayekhy, L., Nejad, M.M., Grosu, D., Vasilakos, A.V.: An online mechanism for resource allocation and pricing in clouds. IEEE Trans. Comput. 65(4), 1172–1184 (2015)
Saraswathi, A.T., Rab Kalaashri, Y., Padmavathi, S.: Dynamic resource allocation scheme in cloud computing. Procedia Comput. Sci. 47, 30–36 (2015)
Tseng, F.H., Wang, X., Chou, L.D., Chao, H.C., Leung, V.C.: Dynamic resource prediction and allocation for cloud data center using the multiobjective genetic algorithm. IEEE Syst. J. 12(2), 1688–1699 (2017)
Wang, W., Jiang, Y., Wu, W.: Multiagent-based resource allocation for energy minimization in cloud computing systems. IEEE Trans. Syst. Man Cybern. 47(2), 205–220 (2016)
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Ramasamy, V., Thalavai Pillai, S. An effective HPSO-MGA optimization algorithm for dynamic resource allocation in cloud environment. Cluster Comput 23, 1711–1724 (2020). https://doi.org/10.1007/s10586-020-03118-x
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DOI: https://doi.org/10.1007/s10586-020-03118-x