article · 2024

Streaming Media Load Balancing with Improved Genetic Algorithm

Yang Zhang, Xing Yang, Yuan Xu, Yan‐Lin He, Mingqing Zhang, Qunxiong Zhu

Cited by 0Abstract and details only

Abstract

The load balancing algorithm of streaming media server is facing unique challenges. Different from the traditional Web server, the streaming media server needs to consider the characteristics of real-time, bandwidth occupation and multimedia data transmission. Therefore, the traditional load balancing algorithm is not necessarily suitable for streaming media servers, and needs special design and research. Aiming at the network requests of streaming media server clusters, a multi-dimensional matrix evaluation method based on streaming media characteristics is proposed, the genetic algorithm is improved, and it is applied to the dynamic load balancing algorithm scheduling scenario, and analyzed and simulated. Experimental results show that the algorithm can effectively reduce the average execution time of all requests, speed up the average response time and improve throughput.

Cite this paper

APA 7

Zhang, Y., Yang, X., Xu, Y., He, Y., Zhang, M., & Zhu, Q. (2024). Streaming Media Load Balancing with Improved Genetic Algorithm. https://doi.org/10.1109/ccdc62350.2024.10588365

MLA 9

Zhang, Yang, et al. “Streaming Media Load Balancing with Improved Genetic Algorithm.” vol. 2, 2024, pp. 6251–56, https://doi.org/10.1109/ccdc62350.2024.10588365.

Chicago

Zhang, Yang, Xing Yang, Yuan Xu, Yan‐Lin He, Mingqing Zhang, and Qunxiong Zhu. 2024. “Streaming Media Load Balancing with Improved Genetic Algorithm.” https://doi.org/10.1109/ccdc62350.2024.10588365.

Harvard

Zhang, Y. et al. (2024) ‘Streaming Media Load Balancing with Improved Genetic Algorithm’. Available at: https://doi.org/10.1109/ccdc62350.2024.10588365.

BibTeX

@article{zhang2024streaming,
  title = {{Streaming Media Load Balancing with Improved Genetic Algorithm}},
  author = {Zhang, Yang and Yang, Xing and Xu, Yuan and He, Yan‐Lin and Zhang, Mingqing and Zhu, Qunxiong},
  volume = {2},
  pages = {6251--6256},
  year = {2024},
  doi = {10.1109/ccdc62350.2024.10588365}
}

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