Qi Liu, Weidong Cai, Jian Shen, Zhangjie Fu, N. Linge
{"title":"异构Hadoop系统中基于节点分类的智能推测执行策略","authors":"Qi Liu, Weidong Cai, Jian Shen, Zhangjie Fu, N. Linge","doi":"10.1109/ICACT.2016.7423338","DOIUrl":null,"url":null,"abstract":"MapReduce (MR) has been widely used to process distributed large data sets. Meanwhile, speculative execution is known as an approach for dealing with same problems by backing up those tasks running on a low performance machine to a higher one. In this paper, we have modified some pitfalls and taken heterogeneous environment into consideration. We also have implemented it in Hadoop-2.6 based on node classification, this strategy is called Speculation-NC and optimized Hadoop is called Hadoop-NC. Experiment results show that our method can correctly backup a task, improve the performance of MRV2 and decrease the execution time and resource consumption compared with traditional strategy.","PeriodicalId":125854,"journal":{"name":"2016 18th International Conference on Advanced Communication Technology (ICACT)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A smart speculative execution strategy based on node classification for heterogeneous Hadoop systems\",\"authors\":\"Qi Liu, Weidong Cai, Jian Shen, Zhangjie Fu, N. Linge\",\"doi\":\"10.1109/ICACT.2016.7423338\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"MapReduce (MR) has been widely used to process distributed large data sets. Meanwhile, speculative execution is known as an approach for dealing with same problems by backing up those tasks running on a low performance machine to a higher one. In this paper, we have modified some pitfalls and taken heterogeneous environment into consideration. We also have implemented it in Hadoop-2.6 based on node classification, this strategy is called Speculation-NC and optimized Hadoop is called Hadoop-NC. Experiment results show that our method can correctly backup a task, improve the performance of MRV2 and decrease the execution time and resource consumption compared with traditional strategy.\",\"PeriodicalId\":125854,\"journal\":{\"name\":\"2016 18th International Conference on Advanced Communication Technology (ICACT)\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-03-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 18th International Conference on Advanced Communication Technology (ICACT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICACT.2016.7423338\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 18th International Conference on Advanced Communication Technology (ICACT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACT.2016.7423338","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A smart speculative execution strategy based on node classification for heterogeneous Hadoop systems
MapReduce (MR) has been widely used to process distributed large data sets. Meanwhile, speculative execution is known as an approach for dealing with same problems by backing up those tasks running on a low performance machine to a higher one. In this paper, we have modified some pitfalls and taken heterogeneous environment into consideration. We also have implemented it in Hadoop-2.6 based on node classification, this strategy is called Speculation-NC and optimized Hadoop is called Hadoop-NC. Experiment results show that our method can correctly backup a task, improve the performance of MRV2 and decrease the execution time and resource consumption compared with traditional strategy.