SD-ALB: Software Defined Adaptive load balancing in Data Center Network
Vol. 6 No. 2 (2023), Articles
Vol. 6 No. 2 (2023)

SD-ALB: Software Defined Adaptive load balancing in Data Center Network

Articles
https://doi.org/10.51153/kjcis.v6i2.181
Published 2023-08-07
Riwan Fazal
1University Institute of Information Technology, PMAS Arid Agriculture University, Rawalpindi
Syed Mushhad M. Gilani
Department of Computer Science, University of Agriculture, Faisalabad
Muuhammad Junaid Khalid
University Institute of Information Technology, PMAS Arid Agriculture University, Rawalpindi
PDF

Keywords

Adaptive load balancing
Ant Colony Optimization
Path Selection
Response Time
Software Defined Networking

How to Cite

Fazal, R., Gilani, S. M. M., & Khalid, M. J. (2023). SD-ALB: Software Defined Adaptive load balancing in Data Center Network. KIET Journal of Computing and Information Sciences, 6(2), 51-67. https://doi.org/10.51153/kjcis.v6i2.181
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Abstract

Network monitoring has crucial importance in data center networks to analyze the behavior of the underlying network. This analysis is used for working on multiple network parameters and load balancing is one of them. This article proposes an adaptive load-balancing approach to balance the load between the servers while changing its behavior with a change in traffic. Software Defined Networking (SDN) provides the single point of network configuration called SDN controller. This approach is facilitating the easy implementation of adaptive load balancing in Data Center Networks. The proposed approach is an extension to the LBBCLT load balancing approach that uses a dynamic probe generator to probe the servers about the response time and link bandwidth. We incorporate a path selection module in it and the path is selected using the Ant Colony Optimization. The results show that the bandwidth consumption and throughput have been improved and servers are receiving the load according to their capacities.

https://doi.org/10.51153/kjcis.v6i2.181
PDF

References

Guo, Pengfei, et al. “A Congestion Control Algorithm Based on Deep Reinforcement Learning in SDN Data Center Networks.” International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), vol. 12258, SPIE, 2022, pp. 211–16. www. spiedigitallibrary.org, https://doi.org/10.1117/12.2639258.

Pang, Shuanglong, et al. “Research on SDN-Based Data Center Network Traffic Management and Optimization.” 2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA), 2022, pp. 600–04. IEEE Xplore, https://doi.org/10.1109/ ICPECA53709.2022.9718973.

Tang, Qian, et al. “Elephant Flow Detection Mechanism in SDN-Based Data Center Networks.” Scientific Programming, vol. 2020, Sept. 2020, pp. 1–8. DOI.org (Crossref), https://doi. org/10.1155/2020/8888375.

Wang, You-Chiun, and Ting-Jui Hsiao. “URBM: User-Rank-Based Management of Flows in Data Center Networks through SDN.” 2022 4th International Conference on Computer Communication and the Internet (ICCCI), 2022, pp. 142–49. IEEE Xplore, https://doi. org/10.1109/ICCCI55554.2022.9850240.

Vani, K. A., and K. N. RamaMohanBabu. “An Intelligent Server Load Balancing Based on Multi-Criteria Decision-Making in SDN.” International Journal of Electrical and Computer Engineering Systems, vol. 14, no. 4, Apr. 2023, pp. 433–42. hrcak.srce.hr, https://doi.org/10.32985/ ijeces.14.4.7.

Sridevi, K., and Md Abdul Saifulla. “LBABC: Distributed Controller Load Balancing Using Artificial Bee Colony Optimization in an SDN.” Peer-to-Peer Networking and Applications, vol. 16, no. 2, Mar. 2023, pp. 947–57. Springer Link, https://doi.org/10.1007/s12083-023-01448-2.

Ramasubbareddy, Somula, and R. Sasikala. “RTTSMCE: A Response Time Aware Task Scheduling in Multi-Cloudlet Environment.” International Journal of Computers and Applications, vol. 43, no. 7, Aug. 2021, pp. 691–96. DOI.org (Crossref), https://doi.org/10.1080

/1206212X.2019.1629098.

Xu, Chen, et al. “A Survey of SDN Traffic Management Research.” 2022 11th International Conference on Communications, Circuits and Systems (ICCCAS), 2022, pp. 231–36. IEEE Xplore, https://doi.org/10.1109/ICCCAS55266.2022.9824926.

Zaher, Maiass, et al. “Sieve: A Flow Scheduling Framework in SDN Based Data Center Networks.” Computer Communications, vol. 171, Apr. 2021, pp. 99–111. ScienceDirect, https:// doi.org/10.1016/j.comcom.2021.02.013.

Schaller, Sibylle, and Dave Hood. “Software Defined Networking Architecture Standardization.” Computer Standards & Interfaces, vol. 54, Nov. 2017, pp. 197–202. DOI.org (Crossref), https:// doi.org/10.1016/j.csi.2017.01.005.

Hamdan, Mosab, et al. “A Comprehensive Survey of Load Balancing Techniques in Software- Defined Network.” Journal of Network and Computer Applications, vol. 174, Jan. 2021, p. 102856. ScienceDirect, https://doi.org/10.1016/j.jnca.2020.102856.

Semong, Thabo, et al. “Intelligent Load Balancing Techniques in Software Defined Networks: A Survey.” Electronics, vol. 9, no. 7, July 2020, p. 1091. DOI.org (Crossref), https://doi. org/10.3390/electronics9071091.

Abdelrahman, Abdallah Mustafa, et al. “Software-Defined Networking Security for Private Data Center Networks and Clouds: Vulnerabilities, Attacks, Countermeasures, and Solutions.” International Journal of Communication Systems, vol. 34, no. 4, 2021, p. e4706. Wiley Online Library, https://doi.org/10.1002/dac.4706.

Montazerolghaem, Ahmadreza. “Software-Defined Load-Balanced Data Center: Design, Implementation and Performance Analysis.” Cluster Computing, vol. 24, no. 2, June 2021, pp. 591–610. DOI.org (Crossref), https://doi.org/10.1007/s10586-020-03134-x.

Malavika, Rajadurai, and Muniappan Lakshapalam Valarmathi. “Load Balancing Based on Closed Loop Control Theory (LBBCLCT): A Software Defined Networking (SDN) Powered Server Load Balancing System Based on Closed Loop Control Theory.” Concurrency and Computation: Practice and Experience, Feb. 2022. DOI.org (Crossref), https://doi.org/10.1002/ cpe.6854.

Soleimanzadeh, Kiarash, et al. “SD-WLB: An SDN-Aided Mechanism for Web Load Balancing Based on Server Statistics.” ETRI Journal, vol. 41, no. 2, 2019, pp. 197–206. Wiley Online Library, https://doi.org/10.4218/etrij.2018-0188.

Fancy, C., and M. Pushpalatha. “Traffic-Aware Adaptive Server Load Balancing for Software Defined Networks.” International Journal of Electrical and Computer Engineering (IJECE), vol. 11, no. 3, June 2021, p. 2211. DOI.org (Crossref), https://doi.org/10.11591/ijece.v11i3. pp2211-2218.

Emad Ali, Tariq, et al. “Load Balance in Data Center SDN Networks.” International Journal of Electrical and Computer Engineering (IJECE), vol. 8, no. 5, Oct. 2018, p. 3084. DOI.org (Crossref), https://doi.org/10.11591/ijece.v8i5.pp3084-3091.

H. Zhong, Y. Fang, and J. Cui, “Reprint of ‘LBBSRT: An efficient SDN load balancing scheme based on server response time,’Future Gener. Comput. Syst., vol. 80, pp. 409–416, Mar. 2018, doi: 10.1016/j.future.2017.11.012.

Xiangyun, Zeng, etal.“Deep Reinforcement Learning with Graph Convolutional Networks for Load Balancing in SDN-Based Data Center Networks.” 2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), IEEE, 2021, pp. 344–52. DOI.org (Crossref), https://doi.org/10.1109/ICCWAMTIP53232.2021.9674074.

Chakravarthy, V. Deeban, and Balakrishnan Amutha. “A Novel Software-Defined Networking Approach for Load Balancing in Data Center Networks.” International Journal of Communication Systems, vol. 35, no. 2, 2022, p. e4213. Wiley Online Library, https://doi.org/10.1002/dac.4213.

Zhang, Jiao, et al. “Load Balancing in Data Center Networks: A Survey.” IEEE Communications Surveys & Tutorials, vol. 20, no. 3, 2018, pp. 2324–52. IEEE Xplore, https://doi.org/10.1109/ COMST.2018.2816042.