SCHEDULE: NOV 16-22, 2013
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CooMR: Cross-Task Coordination for Efficient Data Management in MapReduce Programs
SESSION: System-wide Application Performance Assessments
EVENT TYPE: Papers
TIME: 4:30PM - 5:00PM
SESSION CHAIR: Dominik Ulmer
AUTHOR(S):Xiaobing Li, Yandong Wang, YIzheng Jiao, Cong Xu, Weikuan Yu
ROOM:401/402/403
ABSTRACT:
Hadoop is a popular open-source implementation of a MapReduce programming model for big data processing. It represents system resources as map and reduce slots and schedules them to various tasks. This execution model gives little regard to the need of cross-task coordination on the use of shared resources on each node, which results in task interference. In addition, the existing merge algorithm causes excessive I/O. In this study, we undertake an effort to address both issues. Accordingly, we introduce a cross-task coordination framework called CooMR for efficient data management in MapReduce programs. CooMR consists of three component schemes including cross-task opportunistic memory sharing and log-structured I/O consolidation, which aim to facilitate task coordination, and a key-based in-situ merge algorithm designed to enable the sorting/merging of intermediate data without actually moving the
Chair/Author Details:
Dominik Ulmer (Chair) - Cray Switzerland
Xiaobing Li - Auburn University
Yandong Wang - Auburn University
YIzheng Jiao - Auburn University
Cong Xu - Auburn University
Weikuan Yu - Auburn University
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The full paper can be found in the ACM Digital Library