Network resource scheduling algorithm for cloud data center

Shu Zhang; Fan Li; Weiguo Ju; Huanhuan Gu

doi:10.1117/12.3050089

8 November 2024 Network resource scheduling algorithm for cloud data center

Shu Zhang, Fan Li, Weiguo Ju, Huanhuan Gu

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134160B (2024) https://doi.org/10.1117/12.3050089
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

In order to reduce the query load of nodes and improve the processing efficiency of network system, this paper proposes a network resource scheduling algorithm for cloud data center. According to different information sources, the confidence interval index of resource scheduling is obtained, and the prior distribution of resources is obtained according to the splitting history of network resource data nodes, and the network resource characteristics of cloud-side data center are extracted through link distance; Accurately encode the data of various resource states, task requirements and scheduling strategies, analyze the source code states, conversion processes and programming representations, and realize the network resource data encoding of the cloud data center; Linear decreasing is carried out, and the network resource scheduling structure of cloud data center is established to realize the research of resource scheduling algorithm. The experimental results show that the load balance coefficient achieved by this algorithm is stable above 0.9, which ensures that it can still maintain an efficient and stable running state under high load, with high occupancy rate of each index and good scheduling effect.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shu Zhang, Fan Li, Weiguo Ju, and Huanhuan Gu "Network resource scheduling algorithm for cloud data center", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134160B (8 November 2024); https://doi.org/10.1117/12.3050089

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