Abstract
Map reduce is a parallel programming paradigm used for processing massive data sets. A popular open-source implementation of Map reduce is Hadoop. There are basic schedulers embedded in Hadoop, including First in First out (FIFO), Fair Scheduler, and Capacity Scheduler (CS). Currently, researches have been focused on Capacity Scheduler to improve the Capacity Scheduler. Native Capacity Scheduler does not support the preemption, which results in the starvation caused by the non-preemptive scheduling. To resolve this problem, a Preemptive Capacity Scheduler Policy (PCSP) is proposed. Finally, we implement the PCSP on Hadoop, the experimental results of which indicate that PCSP we proposed is efficient in running Hadoop jobs.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Grid and Distributed Computing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
