By Topic

A scheduling mechanism for multiple MapReduce jobs in a workflow application (position paper)

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Dongjin Yoo ; Multi-Agent & Cloud Comput. Syst. Lab., Gwangju Inst. of Sci. & Technol. (GIST), Gwangju, South Korea ; Kwang Mong Sim

MapReduce is currently an attractive model for data intensive application due to easy interface of programming, high scalability and fault tolerance capability. It is well suited for applications requiring processing large data with distributed processing resources such as web data analysis, bio informatics, and high performance computing area. There are many studies of job scheduling mechanism in shared cluster for MapReduce. However there is a need for scheduling workflow service composed of multiple MapReduce tasks with precedence dependency in multiple processing nodes. The contribution of this paper is proposing a scheduling mechanism for a workflow service containing multiple MapReduce jobs. The workflow application has precedence dependency constraints among multiple tasks, represented as directed acyclic graph (DAG). Also, for less data transfer cost in limited bisection bandwidth, data dependency criterion should be considered for scheduling multiple map-reduce jobs in a workflow. The proposed scheduling mechanism provides 1) scheduling MapReduce tasks regarding precedence constraints and 2) pre-data placement method considering data dependency constraints for saving data transfer cost over network.

Published in:

Computing, Communications and Applications Conference (ComComAp), 2012

Date of Conference:

11-13 Jan. 2012