@article{4321,
  author = {Kai Wang, Na Wang},
  title = {Research on Machine Learning-Based Microservices Load Balancing Algorithm},
  journal = {Journal of Intelligent Computing},
  year = {2024},
  volume = {15},
  number = {4},
  doi = {https://doi.org/10.6025/jic/2024/15/4/135-141},
  url = {https://www.dline.info/jic/fulltext/v15n4/jicv15n4_3.pdf},
  abstract = {The rapid progress of cloud computing technology has attracted widespread
attention, and the importance of microservices architecture is also increasing. In
this regard, we studied a new algorithm, Xgboost, which effectively addresses
the load balancing issues in the current microservices cluster, thus better meeting
user demands. We identified factors that significantly impact load balancing
effectiveness through in-depth research on various features. We used ensemble
learning to estimate the load received by each server node, thereby achieving
load balancing. Experimental results confirm that our proposed algorithm
significantly improves throughput, reduces interception errors, and lowers the
system’s average response time compared to other load-balancing algorithms.},
}