Abstract
VM Consolidation is one of the prodigious challenges in Cloud Computing as VMs have to be automatically placed into a physical machine based on the load running on the corresponding physical machine i.e., host is in overloaded condition or it may be in underloaded condition. VM consolidation is enacted based on the condition i.e., either overloading or underloading of VMs into a physical host. Energy consumption in data centers is one of the huge challenges because when we consolidate the VMs into a single physical machine based on the conditions it reduces energy consumption in the data centers which is a huge advantage for the cloud provider. Many of the authors proposed VM Consolidation algorithms by addressing energy consumption as a parameter but those algorithms not meeting the standards in terms of energy consumption. In this paper, we have proposed a new hybridized Meta-heuristic approach by combining Particle swarm optimization (PSO) and Cuckoo Search (CS) algorithms for consolidation of VMs based on the status Index of VMs and thereby addressing the energy consumption as a parameter. This work is simulated on Cloudsim and the workload is generated randomly in clouds and is given as input to the algorithm. To evaluate the efficiency of the algorithm in the view of energy consumption we have compared the proposed approach against existing algorithms such as PSO and CS. Simulation results revealed that our proposed approach is improved significantly over compared algorithms with mentioned parameters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
F. Liu, J. Tong, J. Mao, R. Bohn, J. Messina, L. Badger, D. Leaf, NIST cloud computing reference architecture. NIST Spec. Publ. 500, 1–28 (2011)
M.S. Sudheer, M. Vamsi Krishna, Dynamic PSO for task scheduling optimization in cloud computing. Int. J. Recent Technol. Eng. 8(2), 332–338 (2019)
R.N. Calheiros et al., CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)
S.K. Mishra et al., Energy-efficient VM-placement in cloud data center. Sustain. Comput. Inform. Syst. 20, 48–55 (2018)
M. Abdel-Basset, L. Abdle-Fatah, A.K. Sangaiah, An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment. Clust. Comput. 22(4), 8319–8334 (2019)
E. Barlaskar, N. Ajith Singh, Y. Jayanta, Energy optimization methods for virtual machine placement in cloud data center. ADBU J. Eng. Technol. 1 (2014)
A. Tripathi, I. Pathak, D.P. Vidyarthi, Modified dragonfly algorithm for optimal virtual machine placement in cloud computing. J. Netw. Syst. Manage. 28, 1316–1342 (2020)
A. Tripathi, I. Pathak, D.P. Vidyarthi, Energy efficient VM placement for effective resource utilization using modified binary PSO. Comput. J. 61(6), 832–846 (2018)
S. Gharehpasha, M. Masdari, A. Jafarian, Virtual machine placement in cloud data centers using a hybrid multi-verse optimization algorithm. Artif. Intell. Rev. 1–37 (2020)
S. Gharehpasha, M. Masdari, A discrete chaotic multi-objective SCA-ALO optimization algorithm for an optimal virtual machine placement in cloud data center. J. Ambient Intell. Humaniz. Comput. 1–17 (2020)
E. Barlaskar, Y.J. Singh, B. Issac, Enhanced cuckoo search algorithm for virtual machine placement in cloud data centres. Int. J. Grid Util. Comput. 9(1), 1–17 (2018)
S. Mangalampalli, V.K. Mangalampalli, S.K. Swain, Energy aware task scheduling algorithm in cloud computing using PSO and cuckoo search hybridization. Solid State Technol. 63(6), 13995–14010 (2020)
R. Chi et al., A hybridization of cuckoo search and particle swarm optimization for solving optimization problems. Neural Comput. Appl. 31(1), 653–670 (2019)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mangalampalli, S., Sree, P.K., Usha Devi N, S.S.S.N., Mallela, R.B. (2022). An Effective VM Consolidation Mechanism by Using the Hybridization of PSO and Cuckoo Search Algorithms. In: Nayak, J., Behera, H., Naik, B., Vimal, S., Pelusi, D. (eds) Computational Intelligence in Data Mining. Smart Innovation, Systems and Technologies, vol 281. Springer, Singapore. https://doi.org/10.1007/978-981-16-9447-9_37
Download citation
DOI: https://doi.org/10.1007/978-981-16-9447-9_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9446-2
Online ISBN: 978-981-16-9447-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)