Earticle

초록

영어

The job scheduling for Map Reduce clusters has received significant attention in recent years, because it plays an important role on Map Reduce clusters. Traditional job scheduling performs poorly in assigning a task to appropriate nodes, and can not predict the resource utilization of the unexecuted tasks. To address the problems, an efficient job scheduling for Map Reduce clusters is proposed in this paper. The job scheduling introduces dynamic priority scheduling and real-time prediction model. Dynamic priority scheduling introduces the minimum cost data locality algorithm with a weight to deal with different size jobs, and real-time prediction model can predict the resource utilization of unexecuted tasks by calculating the running tasks. The resource utilization contains CPU, memory, and network. Experimental results prove that the proposed job scheduling is able to perform well in Map Reduce clusters.

목차

Abstract
 1. Introduction
 2. Related Work
 3. An Efficient Job Scheduling
  3.1. Dynamic Priority Scheduling
  3.2. The Real-Time Prediction Model of Jobs
 4. Experimental Evaluations
  4.1. Experimental Environment
  4.2. Experiment Result
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Jun Liu [ College of Computer Science, Chongqing University, Chongqing, China ]
• Tianshu Wu [ College of Computer Science, Chongqing University, Chongqing, China ]
• Ming Wei Lin [ College of Computer Science, Chongqing University, Chongqing, China ]
• Shuyu Chen [ College of Software Engineering, Chongqing University, Chongqing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605