Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (5): 313-321.doi: 10.11896/jsjkx.220400019

• Computer Network • Previous Articles     Next Articles

Study on Load Balancing Algorithm of Microservices Based on Machine Learning

YANG Qianlong1, JIANG Lingyun1,2   

  1. 1 School of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
    2 Internet of Things Research Institute,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
  • Received:2022-04-02 Revised:2022-04-19 Online:2023-05-15 Published:2023-05-06
  • About author:YANG Qianlong,born in 1998,postgraduate.His main research interests include microservice load balancing and machine learning.
    JIANG Lingyun,born in 1971,master,associate professor.Her main research interest is the next generation network.
  • Supported by:
    Key Research and Development Program of Jiangsu Province(BE2020084-4).

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Abstract: With the continuous development of cloud computing technology,the microservice architecture has received more and more attention.Since it is more convenient in development and maintenance to divide large-scale applications into fine-grained single services,lots of large applications have evolved from monolithic architecture to microservice architecture.In the microservice architecture,in order to improve the availability of microservices,microservice instances are usually deployed in a cluster structure.Aiming at the problem of unbalanced load of server nodes in a microservice cluster with the increase of the number of tasks,a load balancing algorithm based on Xgboost,shortest predictive response time,is proposed.By selecting the characteristic para-meters that affect the response time of the task,and then using machine learning to predict the response time of new task,the task is finally assigned to the server node with the smallest predicted response time,so as to achieve the purpose of load balancing between server nodes.The results show that using the proposed load balancing algorithm has a certain improvement in throughput,cut-off rate and average response time compared with other load algorithms,and it is more suitable for microservice clusters in high concurrency environments.

Key words: Microservices, Load balancing, Ensemble learning, Predicted response time, Xgboost

CLC Number: 

  • TP302
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