Abstract
The major concerns of cloud providers is resource management. A wide range of approaches and tools have been proposed such as meta-heuristics. Among the most recent suggested meta-heuristics are: Cuckoo Search (CS) and Particle Swarm Optimization (PSO). The main contribution of this paper is to compare the performance of CS and PSO on the resource allocation in a data center. Extensive simulation, using a dataset varying from the range of 200 to 1000 demands, demonstrates that PSO converges most rapidly than CS. Moreover, the results show an enhancement of as much as 1% to 10% of energy consumption and 1% to 5% of resource utilization on average.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Pietri, I., Sakellariou, R.: Mapping virtual machines onto physical machines in cloud computing: a survey. ACM Comput. Surv. (CSUR) 49(3), 1–30 (2016). Article ID 49
El Amri, A., Meddeb, A.: Impact of server placement on routing performance in network virtualization. In: 13th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 1321–1326. IEEE (2017)
Mohamadi Bahram Abadi, R., Rahmani, A.M., Alizadeh, S.H.: Server consolidation techniques in virtualized data centers of cloud environments: a systematic literature review. Softw. Pract. Exp. 48(9), 1688–1726 (2018)
Braiki, K., Youssef, H.: Multi-objective virtual machine placement algorithm based on particle swarm optimization. In: 14th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 279–284. IEEE (2018)
Braiki, K., Youssef, H.: Resource management in cloud data centers: a survey. In: 15th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 1007–1012. IEEE (2019)
Braiki, K., Youssef, H.: Fuzzy-logic-based multi-objective best-fit-decreasing virtual machine reallocation. J. Supercomput. 76, 427–454 (2020)
Sait, S.M., Bala, A., El-Maleh, A.H.: Cuckoo search based resource optimization of datacenters. Appl. Intell. 44(3), 489–506 (2016)
Sharma, N., Guddeti, R.M.: Multi-objective energy efficient virtual machines allocation at the cloud data center. IEEE Trans. Serv. Comput. 12, 158–171 (2016)
Speitkamp, B., Bichler, M.: A mathematical programming approach for server consolidation problems in virtualized data centers. IEEE Trans. Serv. Comput. 3(4), 266–278 (2010)
Ferreto, T.C., Netto, M.A., Calheiros, R.N., De Rose, C.A.: Server consolidation with migration control for virtualized data centers. Future Gener. Comput. Syst. 27(8), 1027–1034 (2011)
Chaisiri, S., Lee, B.-S., Niyato, D.: Optimal virtual machine placement across multiple cloud providers. In: Services Computing Conference, pp. 103–110. IEEE (2009)
Ribas, B.C., Suguimoto, R.M., Montano, R.A., Silva, F., Castilho, M.: PBFVMC: a new Pseudo-Boolean formulation to virtual machine consolidation. In: Intelligent Systems (BRACIS) Conference, pp. 201–206. IEEE (2013)
Yang, J.-S., Liu, P., Wu, J.-J.: Workload characteristics-aware virtual machine consolidation algorithms. In: Cloud Computing Technology and Science (CloudCom), pp. 42–49. IEEE (2012)
Panigrahy, R., Talwar, K., Uyeda, L., Wieder, U.: Heuristics for vector bin packing. microsoftresearch.com (2011)
Chekuri, C., Khanna, S.: On multi-dimensional packing problems. In: ACM-SIAM Symposium on Discrete Algorithms, pp. 185–194. Society for Industrial and Applied Mathematics (1999)
Anand, A., Lakshmi, J., Nandy, S.: Virtual machine placement optimization supporting performance SLAs. In: Cloud Computing Technology and Science (CloudCom), pp. 298–305. IEEE (2013)
Goudarzi, H., Pedram, M.: Energy-efficient virtual machine replication and placement in a cloud computing system. In: Cloud Computing (CLOUD), pp. 750–757. IEEE (2012)
Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28(5), 755–768 (2012)
Verma, A., Ahuja, P., Neogi, A.: pMapper: power and migration cost aware application placement in virtualized systems. In: ACM/IFIP/USENIX International Conference on Middleware, pp. 243–264. Springer (2008)
Zhao, H., Zheng, Q., Zhang, W., Chen, Y., Huang, Y.: Virtual machine placement based on the VM performance models in cloud. In: Computing and Communications Conference (IPCCC), pp. 1–8. IEEE (2015)
Feller, E., Rilling, L., Morin, C.: Energy-aware ant colony based workload placement in clouds. In: IEEE/ACM International Conference on Grid Computing, pp. 26–33. IEEE (2011)
Ferdaus, M.H., Murshed, M., Calheiros, R.N., Buyya, R.: Virtual machine consolidation in cloud data centers using ACO metaheuristic. In: European Conference on Parallel Processing, pp. 306–317. Springer (2014)
Reddy, V.D., Gangadharan, G., Rao, G.S.V.: Energy-aware virtual machine allocation and selection in cloud data centers. Soft. Comput. 23(6), 1917–1932 (2019)
Abdessamia, F., Tai, Y., Zhang, W.Z., Shafiq, M.: An improved particle swarm optimization for energy-efficiency virtual machine placement. In: Cloud Computing Research and Innovation (ICCCRI), pp. 7–13. IEEE (2017)
Gao, Y., Guan, H., Qi, Z., Wang, B.: An ant colony system algorithm for the problem of server consolidation in virtualized data centers. J. Comput. Inf. Syst. 8(16), 6631–6640 (2012)
Yang, X.-S., Deb, S.: Cuckoo search via Lévy flights. In: Nature & Biologically Inspired Computing, pp. 210–214. IEEE (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Braiki, K., Youssef, H. (2020). Data Center Resource Provisioning Using Particle Swarm Optimization and Cuckoo Search: A Performance Comparison. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Advanced Information Networking and Applications. AINA 2020. Advances in Intelligent Systems and Computing, vol 1151. Springer, Cham. https://doi.org/10.1007/978-3-030-44041-1_98
Download citation
DOI: https://doi.org/10.1007/978-3-030-44041-1_98
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44040-4
Online ISBN: 978-3-030-44041-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)