期刊文献+

数据密集型应用中多优先级用户资源调度研究

Research on Resource Scheduling Algorithm for Multi-User under Data-Intensive Application
下载PDF
导出
摘要 针对用户之间具有明显优先级的数据密集型应用场景,提出了基于资源预留和资源抢占的用户级调度算法,该算法在提高资源利用率的前提下,能够快速响应高优先级用户的作业请求,降低资源抢占代价,并避免低优先级作业长时间得不到调度。提出了基于动态副本创建的任务调度算法,该算法实现了数据本地化执行,并解决了热点数据访问带来的负载均衡问题,降低了作业的响应时间。 On a special data-intensive application scenario, users have obvious priority. Based on resource reserve and resource preemption, this paper proposes a scheduling algorithm on user level. This algorithm can improve resource utilization rate, quickly respond to high-priority users' request, reduce resource preemption cost, and avoid the job with lower priority never being dealt with. Based on dynamic replica creation, this paper proposes another scheduling algorithm on task level. This algorithm can realize data performing locally, slove the load balancing in data access, reduce the response time of job execution.
出处 《计算机科学与探索》 CSCD 2013年第10期953-960,共8页 Journal of Frontiers of Computer Science and Technology
基金 国家自然科学基金No.60904082 国家科技重大专项No.2012ZX01045003-001~~
关键词 资源调度 资源预留 资源抢占 动态副本创建 MAPREDUCE resource scheduling resource reserve resource preemption dynamic replica creation MapReduce
  • 相关文献

参考文献1

二级参考文献13

  • 1ZAHARIA M,BORTHAKUR D,SEN SARMA J,et al.Delayscheduling:a simple technique for achieving locality and fairness incluster scheduling. Proceedings of the EuroSys’’10 . 2010
  • 2Hadoop on Demand. http://hadoop.apache.org/common/docs/r0.18.3/hod.html . 2011
  • 3FISCHER M J,SU X,YIN Y.Assigning tasks for efficiency inHadoop:extended abstract. Proceedings of the SPAA’’10 . 2010
  • 4Max-min fairness. http://en.wikipedia.org/wiki/Max-min_fairness .
  • 5ZAHARIA M,CHOWDHURY M,FRANKLIN M J,et al.Spark:cluster computing with working sets. Proceedings of the HotCloud’’10 . 2010
  • 6ALVARO P,CONDIE T,CONWAY N,et al.Boom analytics:exploring data-centric,declarative programming for the cloud. Proceedings of the EuroSys’’10 . 2010
  • 7Isard M,Prabhakaran V,Currey Jet al.Quincy:Fair sched-uling for distributed computing clusters. Proceedingsof the ACM SIGOPS 22nd Symposium on Operating Sys-tems Principles . 2009
  • 8Tom White.Hadoop:Definitive Guide. . 2009
  • 9Dean J,Ghemawat S.Map/Reduce:Simplified data processing on large clusters. Communications of the ACM . 2008
  • 10M. Isard et al.Dryad: Distributed Data-ParallelPrograms from Sequential Building Blocks. EuroSys . 2007

共引文献15

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部