Research on data center load balancing based on particle swarm optimization fusion ant colony optimization algorithm

Shiyu Song; Jun Wang

doi:10.1117/12.2685899

28 July 2023 Research on data center load balancing based on particle swarm optimization fusion ant colony optimization algorithm

Shiyu Song, Jun Wang

Author Affiliations +

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 127563T (2023) https://doi.org/10.1117/12.2685899
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

In recent years, with the continuous development of big data and cloud computing services, the scale of data centers has become larger and larger, and the rapidly increasing network traffic has also put forward higher requirements for the load balancing technology of data centers. Most of the traditional traffic scheduling algorithms are derived from IP networks. These algorithms do not take into account performance parameters such as link bandwidth and delay of real networks, resulting in very unsatisfactory results. In view of the above problems, this paper proposes a traffic scheduling algorithm based on the Particle Swarm Optimization Fusion Ant Colony Optimization (P-ACO) algorithm, which is mainly for dynamic traffic scheduling of elephant flow. First, the Software Defined Networking (SDN) controller obtains the network performance parameters and topology, then uses the PSO algorithm to iteratively search for solutions, integrates the solutions into the initial pheromone distribution of the ACO algorithm, and finally combined with the global network state, the optimal solution is obtained through the improved ACO algorithm.

Citation Download Citation

Shiyu Song and Jun Wang "Research on data center load balancing based on particle swarm optimization fusion ant colony optimization algorithm", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 127563T (28 July 2023); https://doi.org/10.1117/12.2685899

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