Low-Latency, High-Throughput Load Balancing Algorithms

Lun Wang

doi:10.5281/zenodo.12587888

Authors

Lun Wang Meta Platforms

DOI:

https://doi.org/10.5281/zenodo.12587888

ARK:

https://n2t.net/ark:/40704/JCTAM.v1n2a01

PURL:

https://purl.archive.org/suas/JCTAM.v1n2a01

References:

16

Keywords:

Load Balancing, Low Latency, High Throughput, Distributed Systems, Adaptive Algorithms, Predictive Algorithms, Machine Learning, Software-Defined Networking (SDN), Network Traffic Management, Performance Optimization

Abstract

This paper explores the development and implementation of advanced load balancing algorithms aimed at minimizing latency while maximizing throughput in distributed systems. Traditional load balancing methods, such as round-robin and least connections, often fail to address dynamic workloads effectively. To overcome these limitations, we propose two novel algorithms: an adaptive load balancing algorithm that adjusts to real-time changes in server load and network conditions, and a predictive load balancing algorithm that uses historical data and machine learning to forecast traffic patterns. Through a combination of simulated environments and real-world data, our experimental results demonstrate that these algorithms significantly outperform traditional methods, achieving lower latency and higher throughput. This study provides a comprehensive solution to the challenges of optimizing load balancing in modern distributed systems.

Author Biography

Lun Wang, Meta Platforms

Electrical and computer engineering, Meta Platforms, USA.

References

T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms. MIT Press, 2009.

Y. Liu, X. Liu, W. Sun, and Q. Zhang, "Machine Learning-Based Load Balancing for Cloud Data Centers," IEEE Transactions on Parallel and Distributed Systems, vol. 29, no. 5, pp. 1307-1320, May 2018.

M. Jammal, T. Singh, A. Shami, R. Asal, and Y. Li, "Software-Defined Networking: State of the Art and Research Challenges," Computer Networks, vol. 72, pp. 74-98, 2014.

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

Bu, X., Peng, J., Yan, J., Tan, T., & Zhang, Z. (2021). Gaia: A transfer learning system of object detection that fits your needs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 274-283).

Gao, Z., Wu, Y., Bu, X., Yu, T., Yuan, J., & Jia, Y. (2019). Learning a robust representation via a deep network on symmetric positive definite manifolds. Pattern Recognition, 92, 1-12.

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

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

Yao, J., Wu, T., & Zhang, X. (2023). Improving depth gradient continuity in transformers: A comparative study on monocular depth estimation with cnn. arXiv preprint arXiv:2308.08333.

Zhang, Y., Gui, K., Zhu, M., Hao, Y., & Sun, H. (2024). Unlocking personalized anime recommendations: Langchain and llm at the forefront. Journal of Industrial Engineering and Applied Science, 2(2), 46-53.

Zhu, M., Zhang, Y., Gong, Y., Xing, K., Yan, X., & Song, J. (2024). Ensemble methodology: Innovations in credit default prediction using lightgbm, xgboost, and localensemble. arXiv preprint arXiv:2402.17979.

Zhang, Y., Gong, Y., Cui, D., Li, X., & Shen, X. (2024). Deepgi: An automated approach for gastrointestinal tract segmentation in mri scans. arXiv preprint arXiv:2401.15354.

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

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

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

Zhou, Z. (2024, February). ADVANCES IN ARTIFICIAL INTELLIGENCE-DRIVEN COMPUTER VISION: COMPARISON AND ANALYSIS OF SEVERAL VISUALIZATION TOOLS. In The 8th International scientific and practical conference “Priority areas of research in the scientific activity of teachers” (February 27–March 01, 2024) Zagreb, Croatia. International Science Group. 2024. 298 p. (p. 224).