Hadoop Performance Prediction Model Based on Random Forest

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

摘要：Map Reduce is a programming model for processing large data sets,and Hadoop is the most popular open-source implementation of MapReduce.To achieve high performance,up to 190 Hadoop configuration parameters must be manually tunned.This is not only time-consuming but also error-pron.In this paper,we propose a new performance model based on random forest,a recently developed machine-learning algorithm.The model,called RFMS,is used to predict the performance of a Hadoop system according to the system’s configuration parameters.RFMS is created from 2000 distinct fine-grained performance observations with different Hadoop configurations.We test RFMS against the measured performance of representative workloads from the Hadoop Micro-benchmark suite.The results show that the prediction accuracy of RFMS achieves 95% on average and up to 99%.This new,highly accurate prediction model can be used to automatically optimize the performance of Hadoop systems.

原文链接:http://www.cqvip.com//QK/70429X/201302/46536953.html

送人玫瑰，手留余香~如您已下载到该资源，可在回帖当中上传与大家共享，欢迎来CDA社区交流学习。（仅供学术交流用。）

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏0 回帖

关键词：performance Prediction Performan Perform predict 性能预测 模型基 森林 随机 配置参数