Shuffle:最大限度地提高YARN Shuffle的网络带宽利用率

Feng Liang, F. Lau
{"title":"Shuffle:最大限度地提高YARN Shuffle的网络带宽利用率","authors":"Feng Liang, F. Lau","doi":"10.1145/2907294.2907296","DOIUrl":null,"url":null,"abstract":"YARN is a popular cluster resource management platform. It does not, however, manage the network bandwidth resources which can significantly affect the execution performance of those tasks having large volumes of data to transfer within the cluster. The shuffle phase of MapReduce jobs features many such tasks. The impact of under utilization of the network bandwidth in shuffle tasks is more pronounced if the network bandwidth capacities of the nodes in the cluster are varied. We present BAShuffler, a bandwidth-aware shuffle scheduler, that can maximize the overall network bandwidth utilization by scheduling the source nodes of the fetch flows at the application level. BAShuffler can fully utilize the network bandwidth capacity in a max-min fair network. The experimental results for a variety of realistic benchmarks show that BAShuffler can substantially improve the cluster's shuffle throughput and reduce the execution time of shuffle tasks as compared to the original YARN, especially in heterogeneous network bandwidth environments.","PeriodicalId":20515,"journal":{"name":"Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing","volume":"4 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2016-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"BAShuffler: Maximizing Network Bandwidth Utilization in the Shuffle of YARN\",\"authors\":\"Feng Liang, F. Lau\",\"doi\":\"10.1145/2907294.2907296\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"YARN is a popular cluster resource management platform. It does not, however, manage the network bandwidth resources which can significantly affect the execution performance of those tasks having large volumes of data to transfer within the cluster. The shuffle phase of MapReduce jobs features many such tasks. The impact of under utilization of the network bandwidth in shuffle tasks is more pronounced if the network bandwidth capacities of the nodes in the cluster are varied. We present BAShuffler, a bandwidth-aware shuffle scheduler, that can maximize the overall network bandwidth utilization by scheduling the source nodes of the fetch flows at the application level. BAShuffler can fully utilize the network bandwidth capacity in a max-min fair network. The experimental results for a variety of realistic benchmarks show that BAShuffler can substantially improve the cluster's shuffle throughput and reduce the execution time of shuffle tasks as compared to the original YARN, especially in heterogeneous network bandwidth environments.\",\"PeriodicalId\":20515,\"journal\":{\"name\":\"Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing\",\"volume\":\"4 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2907294.2907296\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2907294.2907296","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

摘要

YARN是一个流行的集群资源管理平台。但是,它不管理网络带宽资源,这可能会严重影响那些在集群内传输大量数据的任务的执行性能。MapReduce作业的shuffle阶段有很多这样的任务。当集群中节点的网络带宽容量不同时,shuffle任务中网络带宽利用率不足的影响更为明显。我们提出了一个带宽感知的shuffle调度器BAShuffler,它可以通过在应用程序级别调度获取流的源节点来最大化整体网络带宽利用率。BAShuffler可以充分利用最大最小公平网络中的网络带宽容量。各种实际基准测试的实验结果表明,与原始YARN相比,BAShuffler可以大幅提高集群的shuffle吞吐量并减少shuffle任务的执行时间,特别是在异构网络带宽环境下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BAShuffler: Maximizing Network Bandwidth Utilization in the Shuffle of YARN
YARN is a popular cluster resource management platform. It does not, however, manage the network bandwidth resources which can significantly affect the execution performance of those tasks having large volumes of data to transfer within the cluster. The shuffle phase of MapReduce jobs features many such tasks. The impact of under utilization of the network bandwidth in shuffle tasks is more pronounced if the network bandwidth capacities of the nodes in the cluster are varied. We present BAShuffler, a bandwidth-aware shuffle scheduler, that can maximize the overall network bandwidth utilization by scheduling the source nodes of the fetch flows at the application level. BAShuffler can fully utilize the network bandwidth capacity in a max-min fair network. The experimental results for a variety of realistic benchmarks show that BAShuffler can substantially improve the cluster's shuffle throughput and reduce the execution time of shuffle tasks as compared to the original YARN, especially in heterogeneous network bandwidth environments.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信