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Data Center Resource Provisioning Using Particle Swarm Optimization and Cuckoo Search: A Performance Comparison

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1151))

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.

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References

  1. 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

    Article  Google Scholar 

  2. 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)

    Google Scholar 

  3. 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)

    Article  Google Scholar 

  4. 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)

    Google Scholar 

  5. 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)

    Google Scholar 

  6. Braiki, K., Youssef, H.: Fuzzy-logic-based multi-objective best-fit-decreasing virtual machine reallocation. J. Supercomput. 76, 427–454 (2020)

    Article  Google Scholar 

  7. Sait, S.M., Bala, A., El-Maleh, A.H.: Cuckoo search based resource optimization of datacenters. Appl. Intell. 44(3), 489–506 (2016)

    Article  Google Scholar 

  8. 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)

    Article  Google Scholar 

  9. 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)

    Article  Google Scholar 

  10. 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)

    Article  Google Scholar 

  11. Chaisiri, S., Lee, B.-S., Niyato, D.: Optimal virtual machine placement across multiple cloud providers. In: Services Computing Conference, pp. 103–110. IEEE (2009)

    Google Scholar 

  12. 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)

    Google Scholar 

  13. 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)

    Google Scholar 

  14. Panigrahy, R., Talwar, K., Uyeda, L., Wieder, U.: Heuristics for vector bin packing. microsoftresearch.com (2011)

    Google Scholar 

  15. 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)

    Google Scholar 

  16. 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)

    Google Scholar 

  17. 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)

    Google Scholar 

  18. 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)

    Article  Google Scholar 

  19. 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)

    Google Scholar 

  20. 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)

    Google Scholar 

  21. 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)

    Google Scholar 

  22. 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)

    Google Scholar 

  23. 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)

    Article  Google Scholar 

  24. 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)

    Google Scholar 

  25. 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)

    Google Scholar 

  26. Yang, X.-S., Deb, S.: Cuckoo search via Lévy flights. In: Nature & Biologically Inspired Computing, pp. 210–214. IEEE (2009)

    Google Scholar 

  27. http://www.spec.org/power_ssj2008/

  28. http://aws.amazon.com/ec2/instance-types/

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Correspondence to Khaoula Braiki .

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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

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