An Optimum Apparent-Integrated Virtual Network Embedding Algorithm In Cloud Computing Data Center Network

Main Article Content

D.M.Kalai Selvi
V.Sharmila
M.AmalaSweena
S.Gayathri
Rejin Paul
P.Ezhumalai

Keywords

cloud service providers, virtual network embedding, quality of service, resource mapping

Abstract

The cloud computing model is enabling innovative and disruptive services by allowing industries to lease/rent computing , storage and network resources from cloud service providers.(ex: google cloud platform,IBM cloud platform) .The insertion of virtual network in the existing physical network is called virtual network embedding problem(VNE). The major challenges faced by cloud service providers (CSP) are unexpected failure of services which have direct impact on Quality of Service, energy consumption, service level agreement violation and huge revenue loss. To overcome this, an optimum apparent –integrated virtual network embedding algorithm is used in cloud computing fat tree data center network .The intellectual property of the proposed algorithm is that the virtual machines are embedded to different physical machines in both consolidated and distributed manner. In other words, the proposed algorithm executed in multiple physical servers in order to concurrently mapping virtual resources of a virtual network and reduces the resource-mapping time and also increase QoS of services provided by CSP

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References

1. J. Alqahtani and B. Hamdaoui, "Rethinking Fat-Tree Topology Design for Cloud Data Centers," 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, 2018, pp. 1-6, doi: 10.1109/GLOCOM.2018.8647774
2. S. Hacham, N. M. Din and N. Balasubramanian, "Load Balancing in Software-Defined Data Centre With Fat Tree Architecture," 2022 4th International Conference on Smart Sensors and Application (ICSSA), Kuala Lumpur, Malaysia, 2022, pp. 45-49, doi: 10.1109/ICSSA54161.2022.9870977.
3. N. Ogino, T. Kitahara, S. Arakawa, and M. Murata, “Virtual network embedding with multiple priority classes sharing substrate resources,” Comput. Network., vol. 112, pp. 52–66, Jan. 2017.
4. Z. Xu, F. Liu, T. Wang, and H. Xu, “Demystifying the energy efficiency of network function virtualization,” in Proc. IEEE/ACM 24th Int. Symp. Quality Service, 2016, pp. 1–10.
5. R. Mijumbi. On the energy efficiency prospects of network function virtualization. arXiv preprint arX-iv:1512.00215, 2015.
6. W. Wu, K. He, and A. Akella. Perfsight: Performance diagnosis for software dataplanes. In IMC. ACM, 2015.
7. F. Xu, F. Liu, H. Jin, and A. V. Vasilakos. Managing performance overhead of virtual machines in cloud computing: A survey, state of the art, and future directions.Proceedings of the IEEE, 102(1):11–31, 2014. 8. H. Xu and B. Li. Reducing electricity demand charge for data centers with partial execution. In e-Energy. ACM,2014.
9. Bermejo Gonzalez, Belen & Juiz, Carlos. (2022). A general method for evaluating the overhead when consolidating servers: performance degradation in virtual machines and containers. The Journal of Supercomputing. 78. 10.1007/s11227-022-04318-5.
10. Qian, Ling & Luo, Zhiguo & Du, Yujian & Guo, Leitao. (2009). Cloud Computing: An Overview. 5931. 626-631. 10.1007/978-3-642-10665-1_63.
11. van de Belt, Jonathan & Ahmadi, Hamed & Doyle, Linda. (2014). A Dynamic Embedding Algorithm for Wireless Network Virtualization. IEEE Vehicular Technology Conference. 10.1109/VTCFall.2014.6965811.
12. Yu, Ruozhou & Xue, Guoliang & Zhang, Xiang. (2014). Towards Min-Cost Virtual Infrastructure Embedding. 1-6. 10.1109/GLOCOM.2014.7416953.
13. P. Zhang, H. Yao, and Y. Liu, “Virtual network embedding based on the degree and clustering coefficient information,” IEEE Access, vol. 4, pp. 8572–8580, 2016.

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