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PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:1.0
BEGIN:VEVENT
DTSTART:20131119T233000Z
DTEND:20131120T000000Z
LOCATION:401/402/403
DESCRIPTION;ENCODING=QUOTED-PRINTABLE: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 <key,value> pairs. Our evaluation demonstrates that CooMR can increase task coordination, improve resource utilization, and effectively accelerate MapReduce programs.
SUMMARY:CooMR: Cross-Task Coordination for Efficient Data Management in MapReduce Programs
PRIORITY:3
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