Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 474-479.doi: 10.11896/JsJkx.190900046

• Database & Big Data & Data Science • Previous Articles     Next Articles

Research on HBase Configuration Parameter Optimization Based on Machine Learning

XU Jiang-feng and TAN Yu-long   

  1. School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China
  • Published:2020-07-07
  • About author:XU Jiang-feng, born in 1965, Ph.D, professor, is a member of China Computer Federation.His research interests include data encryption technology, and network security technology.
    TAN Yu-long, born in 1994, postgradua-te, is a member of China Computer Federation.His research interests include information security, network security technology, and machine lear-ning.
  • Supported by:
    This work was supported by the Fundamental Research Funds for the Central University (20190605).

Abstract: HBase is a distributed database management system.For applications that require fast random access to large amounts of data,it is becoming increasingly popular.However,it has many performance-critical configuration parameters that can interact with each other in complex ways,making it extremely difficult to adJust them manually for optimal performance.In this paper,a new method is proposed to automatically tuning the configuration parameters of a given HBase application,called auto-tuning HBase.The key is to build a low-cost performance model with configuration parameters as input.Therefore,different modeling techniques are systematically studied,and the integrated learning algorithm is used to construct the performance model.Then the genetic algorithm is used to search for the optimal configuration parameters for the application through the performance model.As a result,it can quickly and automatically identify a set of configuration parameter values to maximize application performance.By testing the 5 applications with Yahoo! cloud service benchmark,experimental results show that,compared with the default configuration,the optimized throughput increases by 41% on average and can be up to 97%.At the same time,delays in HBase operations decrease by an average of 11.3% to as high as 57%.

Key words: Auto tuning, HBase, Machine learning, Performance modeling, Performance optimization

CLC Number: 

  • TP391
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