{"title":"YARN中不同的调度选项","authors":"Sajal Tyagi, Shipra Saraswat","doi":"10.1109/ICMETE.2016.52","DOIUrl":null,"url":null,"abstract":"Today's world, it is critical to manage huge information as the volume of digital information is increasing day by day. The popular handling system like Hadoop to process information proficiently and utilize such planning calculations accurately in brisk time. The Map Reduce structure has turned into the genuine plan for versatile half organized and not organized information handling lately. The Hadoop environment has developed into the next era, which embraces exquisite asset administration plans for occupation booking. It is a framework for reducing the overall length while doing mapping of jobs. As the time has passed MapReduce has achieved few of its impediments with its various pluggable schedulers. So with a specific end goal to conquer the constraints of MapReduce, the upcoming era of MapReduce has been created called as YARN (Yet Another Resource Negotiator). This paper presents various pluggable schedulers that can be configured in a Hadoop cluster along with their implementation and further discussing recently developed scheduling techniques with a brief prologue to YARN.","PeriodicalId":167368,"journal":{"name":"2016 International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Different Scheduling Options in YARN\",\"authors\":\"Sajal Tyagi, Shipra Saraswat\",\"doi\":\"10.1109/ICMETE.2016.52\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Today's world, it is critical to manage huge information as the volume of digital information is increasing day by day. The popular handling system like Hadoop to process information proficiently and utilize such planning calculations accurately in brisk time. The Map Reduce structure has turned into the genuine plan for versatile half organized and not organized information handling lately. The Hadoop environment has developed into the next era, which embraces exquisite asset administration plans for occupation booking. It is a framework for reducing the overall length while doing mapping of jobs. As the time has passed MapReduce has achieved few of its impediments with its various pluggable schedulers. So with a specific end goal to conquer the constraints of MapReduce, the upcoming era of MapReduce has been created called as YARN (Yet Another Resource Negotiator). This paper presents various pluggable schedulers that can be configured in a Hadoop cluster along with their implementation and further discussing recently developed scheduling techniques with a brief prologue to YARN.\",\"PeriodicalId\":167368,\"journal\":{\"name\":\"2016 International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE)\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMETE.2016.52\",\"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 International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMETE.2016.52","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
当今世界,随着数字信息量的日益增加,管理海量信息变得至关重要。像Hadoop这样流行的处理系统可以熟练地处理信息,并在快速的时间内准确地利用这些规划计算。Map Reduce结构最近已经成为多功能半组织和非组织信息处理的真正方案。Hadoop环境已经发展到下一个时代,它包含了用于职业预订的精美资产管理计划。它是一个框架,用于在进行作业映射时减少总长度。随着时间的推移,MapReduce通过其各种可插拔调度器实现了一些障碍。因此,为了克服MapReduce的限制,即将到来的MapReduce时代被称为YARN (Yet Another Resource Negotiator)。本文介绍了可以在Hadoop集群中配置的各种可插拔调度程序及其实现,并进一步讨论了最近开发的调度技术,并简要介绍了YARN。
Today's world, it is critical to manage huge information as the volume of digital information is increasing day by day. The popular handling system like Hadoop to process information proficiently and utilize such planning calculations accurately in brisk time. The Map Reduce structure has turned into the genuine plan for versatile half organized and not organized information handling lately. The Hadoop environment has developed into the next era, which embraces exquisite asset administration plans for occupation booking. It is a framework for reducing the overall length while doing mapping of jobs. As the time has passed MapReduce has achieved few of its impediments with its various pluggable schedulers. So with a specific end goal to conquer the constraints of MapReduce, the upcoming era of MapReduce has been created called as YARN (Yet Another Resource Negotiator). This paper presents various pluggable schedulers that can be configured in a Hadoop cluster along with their implementation and further discussing recently developed scheduling techniques with a brief prologue to YARN.