Network Load Balancing Strategies and Their Implications for Business Continuity

Authors

  • Lun Wang Meta Platforms

DOI:

https://doi.org/10.5281/zenodo.12737997

ARK:

https://n2t.net/ark:/40704/AJSM.v2n4a02

Keywords:

Network Load Balancing (NLB), Business Continuity, High Availability, Performance Optimization, Scalability, Reliability, Round Robin, Least Connections, IP Hash

Abstract

Network load balancing is a critical aspect of ensuring business continuity in modern enterprises. By distributing network traffic across multiple servers, load balancing can enhance performance, reliability, and availability. This paper examines various network load balancing strategies, their implementation, and their implications for business continuity. Experimental data are provided to support the analysis, and recommendations are made for businesses seeking to optimize their network infrastructure. Additionally, the paper discusses the cost-benefit analysis of implementing different load balancing strategies, considering factors such as initial setup costs, ongoing maintenance, and potential impact on business operations. The findings aim to provide a comprehensive guide for IT professionals and decision-makers in selecting the most appropriate load balancing strategy tailored to their organizational needs. Through this research, the paper underscores the pivotal role of network load balancing in sustaining seamless business operations amidst growing digital demands.

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Author Biography

Lun Wang, Meta Platforms

Electrical and computer engineering, Meta Platforms, USA.

References

Stewart, J. (2021). Network Load Balancing: Techniques and Best Practices. Wiley.

Tan, E., & Morris, R. (2019). High-Performance Load Balancing for Scalable Web Services. ACM Computing Surveys, 51(3), 56-78.

Kreutz, D., Ramos, F. M. V., & Verissimo, P. (2015). Software-Defined Networking: A Comprehensive Survey. Proceedings of the IEEE, 103(1), 14-76.

Hu, H., Cao, J., & Sun, Y. (2020). Dynamic Load Balancing in Distributed Systems. Journal of Parallel and Distributed Computing, 136, 87-99.

Liu, T., Cai, Q., Xu, C., Zhou, Z., Ni, F., Qiao, Y., & Yang, T. (2024). Rumor Detection with a novel graph neural network approach. arXiv Preprint arXiv:2403. 16206.

Liu, T., Cai, Q., Xu, C., Zhou, Z., Xiong, J., Qiao, Y., & Yang, T. (2024). Image Captioning in news report scenario. arXiv Preprint arXiv:2403. 16209.

Xu, C., Qiao, Y., Zhou, Z., Ni, F., & Xiong, J. (2024a). Accelerating Semi-Asynchronous Federated Learning. arXiv Preprint arXiv:2402. 10991.

Zhou, J., Liang, Z., Fang, Y., & Zhou, Z. (2024). Exploring Public Response to ChatGPT with Sentiment Analysis and Knowledge Mapping. IEEE Access.

Zhou, Z., Xu, C., Qiao, Y., Xiong, J., & Yu, J. (2024). Enhancing Equipment Health Prediction with Enhanced SMOTE-KNN. Journal of Industrial Engineering and Applied Science, 2(2), 13–20.

Zhou, Z., Xu, C., Qiao, Y., Ni, F., & Xiong, J. (2024). An Analysis of the Application of Machine Learning in Network Security. Journal of Industrial Engineering and Applied Science, 2(2), 5–12.

Zhou, Z. (2024). ADVANCES IN ARTIFICIAL INTELLIGENCE-DRIVEN COMPUTER VISION: COMPARISON AND ANALYSIS OF SEVERAL VISUALIZATION TOOLS.

Xu, C., Qiao, Y., Zhou, Z., Ni, F., & Xiong, J. (2024b). Enhancing Convergence in Federated Learning: A Contribution-Aware Asynchronous Approach. Computer Life, 12(1), 1–4.

Wang, L., Xiao, W., & Ye, S. (2019). Dynamic Multi-label Learning with Multiple New Labels. Image and Graphics: 10th International Conference, ICIG 2019, Beijing, China, August 23--25, 2019, Proceedings, Part III 10, 421–431. Springer.

Wang, L., Fang, W., & Du, Y. (2024). Load Balancing Strategies in Heterogeneous Environments. Journal of Computer Technology and Applied Mathematics, 1(2), 10–18.

Wang, L. (2024). Low-Latency, High-Throughput Load Balancing Algorithms. Journal of Computer Technology and Applied Mathematics, 1(2), 1–9.

Yao, J., Li, C., Sun, K., Cai, Y., Li, H., Ouyang, W., & Li, H. (2023). Ndc-scene: Boost monocular 3d semantic scene completion in normalized devicecoordinates space. 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 9421–9431. IEEE Computer Society.

Yao, J., Pan, X., Wu, T., & Zhang, X. (2024). Building lane-level maps from aerial images. ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and SignalProcessing (ICASSP), 3890–3894. IEEE.

Yao, J., Wu, T., & Zhang, X. (2023). Improving depth gradientcontinuity in transformers: A comparative study on monocular depth estimation with cnn. arXiv Preprint arXiv:2308. 08333.

Zou, Z., Careem, M., Dutta, A., & Thawdar, N. (2023). Joint spatio-temporal precoding for practical non-stationary wireless channels. IEEE Transactions on Communications, 71(4), 2396–2409.

Zou, Z., Careem, M., Dutta, A., & Thawdar, N. (2022). Unified characterization and precoding for non-stationary channels. ICC 2022-IEEE International Conference on Communications, 5140–5146. IEEE.

Zhibin, Z. O. U., Liping, S., & Xuan, C. (2019). Labeled box-particle CPHD filter for multiple extended targets tracking. Journal of Systems Engineering and Electronics, 30(1), 57–67.

Zou, Z.-B., Song, L.-P., & Song, Z.-L. (2017). Labeled box-particle PHD filter for multi-target tracking. 2017 3rd IEEE International Conference on Computer and Communications (ICCC), 1725–1730. IEEE.

Jia, J., Wang, N., Liu, Y., & Li, H. (2024). Fast Two-Grid Finite Element Algorithm for a Fractional Klein-Gordon Equation. Contemporary Mathematics, 1164–1180.

Xu, Y., Lin, Y.-S., Zhou, X., & Shan, X. (2024). Utilizing emotion recognition technology to enhance user experience in real-time. Computing and Artificial Intelligence, 2(1), 1388–1388.

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Published

2024-07-18

How to Cite

Wang, L. (2024). Network Load Balancing Strategies and Their Implications for Business Continuity. Academic Journal of Sociology and Management, 2(4), 8–13. https://doi.org/10.5281/zenodo.12737997

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