计算机应用研究Issue(1)：111-114,4.DOI:10.3969/j.issn.1001-3695.2016.01.026
基于机器学习的 MapReduce 资源调度算法
Resource scheduling algorithm for MapReduce based on machine learning
摘要
Abstract
To solve the problem that a map and shuffle optimization model allowed overlapping phases in MapReduce was lack of adaptability,this paper proposed a resource scheduling algorithm based on this model of machine learning,it used Bayesian classifier based on the degree of matching the operating system resource requirements and system environment for job schedu-ling,and then updated the classification continuously,so that it had adaptability,considering the overlap phase map and shuf-fle.The simulation experiments verify that the solution can improve the performance of MapReduce system,get a better ave-rage response time.关键词
MapReduce/重叠阶段/自适应性/机器学习/贝叶斯分类器Key words
MapReduce/overlapping phases/adaptability/machine learning/Bayesian classifier分类
信息技术与安全科学引用本文复制引用
于倩,蔚承建,王开,朱林军..基于机器学习的 MapReduce 资源调度算法[J].计算机应用研究,2016,(1):111-114,4.基金项目
江苏省自然科学基金资助项目 （）
