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Wireless Personal Communications

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24-01-2022

Load Balancing: DCN Servers based on Regression Analysis During Heavy and Frequent Messages

Authors: G. Sulthana Begam, M. Sangeetha, N. R. Shanker

Published in: Wireless Personal Communications | Issue 4/2022

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Abstract

Data center network (DCN) consists of server-farms and provides various services, which includes software, storage and applications. DCN uses software-defined networking (SDN) to centralize control for enhancing performance, scalability and security in servers. Load balancing in DCN server is significant and efficient management of resources improves network performance. In this paper, logistic regression based load balancing (LLB) algorithm is proposed for Energy-aware task scheduling, routing and server load balancing of SDN based DCN, which minimizes packet loss, delay, energy efficiency, operational cost maximizes throughput, optimizes load balance during heavy and frequent messages. The proposed LLB algorithm addresses the dynamic nature of DCN in terms of span and size of message flows. LLB algorithm dynamically selects optimal server for routing based on energy consumptions of server. LLB proposes routing strategy and finds optimal routing path based on Logistic Regression analysis with considering the utility function of DCN servers and bandwidth utilization of the network. The proposed algorithm is based on Logistic regression analysis, reduces the energy consumption by 4.7–18% and improves the server utilization by 86%, in comparison to heuristic algorithms, because of stochastic gradient decent weights calculation.

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Kreutz, D., Ramos, F. M., Verissimo, P. E., Rothenberg, C. E., Azodolmolky, S., & Uhlig, S. (2015). Software-defined networking: A comprehensive survey. Proceedings of the IEEE, 103(1), 14–76. https://doi.org/10.1109/JPROC.2014.2371999CrossRef

Greenberg, A., Hamilton, J., Jain, R. N., Kandula, S., Kim, C., Lahiri, P., Maltz, D. A., Patel, P., & Sengupta, S. (2009). Vl2: A scalable and flexible data center network. Proceedings of the ACM SIGCOMM Conference on Data Communication. https://doi.org/10.1145/1594977.1592576CrossRef

Raiciu, C., Sebastien, B., Christopher, P., Adam, G., Damon, W., & Mark, H. (2011). Improving datacenter performance and robustness with multipath tcp. In SIGCOMM: 266–277. https://doi.org/10.1145/2018436.2018467

Alizadeh, M., Edsall, T., Dharmapurikar, S., Vaidyanathan, R., Chu, K., Fingerhut, A., Matus, F., Pan, R., Yadav, N., & Varghese, N. G. (2014). CONGA: Distributed congestion-aware load balancing for datacenters. In Proceedings of the 2014 ACM conference on SIGCOMM 44(4): 503–514. https://doi.org/10.1145/2740070.2626316

Vanini, E., Pan, R., Alizadeh, M., Taheri, P., Edsall, T., (2017). Let It Flow: Resilient asymmetric load balancing with flowlet switching. In Proceedings of the NSDI: 407–420.

Huang, Q., Su, S., Li, J., Xu, P., Shuang, K., Huang. (2012). Enhanced energyefficient scheduling for parallel applications in cloud. In IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing: 781–786. https://doi.org/10.1109/CCGrid.2012.49

Lin, C., Wang, K., & Deng, G. (2017). A QoS-aware routing in SDN hybrid networks. International Conference on Future Networks and Communications, 110, 242–249.

Qiongxiao, Fu., Sun, E., Meng, K., Li, M., & Zhang, Y. (2020). Deep Q-learning for routing schemes in SDN-based data center networks. IEEE Access, 8, 103491–103499. https://doi.org/10.1109/ACCESS.2020.2995511CrossRef

Yuxiang, H., Ziyong, L., Julong, L., Jiangxing, W., & Lan, Y. (2020). Intelligence-driven experiential network architecture for automatic routing in software-defined networking. China Communications, 17(2), 149–162. https://doi.org/10.23919/JCC.2020.02.013CrossRef

10.

Baig, S.-u-R., Iqbal, W., Berral, J. L., Erradi, A., & Carrera, D. (2019). Adaptive prediction models for data center resources utilization estimation. IEEE Transactions on Network and Service Management, 16(4), 1681–1693. https://doi.org/10.1109/TNSM.2019.2932840CrossRef

11.

Jamali, S., Badirzadeh, A., & Siapoush, M. S. (2019). On the use of the genetic programming for balanced load distribution in software-defined networks. Digital Communications and Networks, 5(4), 288–296. https://doi.org/10.1016/j.dcan.2019.10.002CrossRef

12.

Bi, Y., Han, G., Lin, C., Member, S., Peng, Y., Huayan, Pu., & Jia, Y. (2020). Intelligent QoS-aware traffic forwarding for SDN/OSPF hybrid industrial internet. IEEE Transactions on Industrial Informatics, 16(2), 1395–1405. https://doi.org/10.1109/TII.2019.2946045CrossRef

13.

Dong, E., Xiaoming, Fu., Mingwei, Xu., & Yang, Y. (2020). Low-cost datacenter load balancing with multipath transport and top-of-rack switches. IEEE Transactions on Parallel and Distributed Systems, 31(10), 2232–2247. https://doi.org/10.1109/TPDS.2020.2989441CrossRef

14.

Yekkehkhany, A., & Nag, R. (2020). Blind GB-PANDAS: A blind throughput-optimal load balancing algorithm for affinity scheduling. IEEE/ACM Transactions on Networking, 28(3), 1199–1212. https://doi.org/10.1109/TNET.2020.2978195CrossRef

15.

Gul, B., Khan, I. A., Mustafa, S., Khalid, O., Hussain, S. S., Dancey, D., & Nawaz, R. (2020). CPU and RAM energy-based SLA-aware workload consolidation techniques for clouds. IEEE Access, 8, 62990–63003. https://doi.org/10.1109/ACCESS.2020.2985234CrossRef

16.

Ali, H., Qureshi, M. S., Qureshi, M. B., Khan, A. A., Zakarya, M., & Fayaz, M. (2020). An energy and performance aware scheduler for real-time tasks in cloud datacentres. IEEE Access, 8, 161288–161303. https://doi.org/10.1109/ACCESS.2020.3020843CrossRef

17.

Liang, B., Dong, X., Wang, Y., & Zhang, X. (2020). Memory-aware resource management algorithm for low-energy cloud data centers. Future Generation Computer Systems, 113, 329–342. https://doi.org/10.1016/j.future.2020.07.026CrossRef

18.

Hadeer Hassan, A., Sameh Salem, A., & Elsayed Saad, M. (2020). A smart energy and reliability aware scheduling algorithm for workflow execution in DVFS-enabled cloud environment. Future Generation Computer Systems, 112, 431–448. https://doi.org/10.1016/j.future.2020.05.040CrossRef

19.

Mishra, S. K., Mishra, S., Alsayat, A., Jhanjhi, N. Z., Humayun, M., Sahoo, K. S., & Luhach, A. K. (2020). Energy-aware task allocation for multi-cloud networks. IEEE Access, 8, 178825–178834. https://doi.org/10.1109/ACCESS.2020.3026875CrossRef

20.

Noormohammadpour, M., & Cauligi Raghavendra, S. (2018). Datacenter traffic control: understanding techniques and trade-offs. IEEE Communications Surveys and Tutorials, 20(2), 1492–1525. https://doi.org/10.1109/COMST.2017.2782753CrossRef

21.

Assefa, B. G., & Oznur Ozkasap, S. (2020). Datacenter RESDN: A novel metric and method for energy efficient routing in software defined networks. IEEE Transactions on Network and Service Management, 17(2), 736–749. https://doi.org/10.1109/TNSM.2020.2973621CrossRef

Title: Load Balancing: DCN Servers based on Regression Analysis During Heavy and Frequent Messages
Authors: G. Sulthana Begam
M. Sangeetha
N. R. Shanker
Publication date: 24-01-2022
Publisher: Springer US
Published in: Wireless Personal Communications / Issue 4/2022
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-022-09523-2

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