Offline Scheduling of Map and Reduce Tasks on Hadoop Systems
Aymen Jlassi, Patrick Martineau, Vincent Tkindt
2015
Abstract
MapReduce is a model to manage quantities massive of data. It is based on the distributed and parallel execution of tasks over the cluster of machines. Hadoop is an implementation of MapReduce model, it is used to offer BigData services on the cloud. In this paper, we expose the scheduling problem on Hadoop systems. We focus on the offline-scheduling, expose the problem in a mathematic model and use the timeindexed formulation. We aim consider the maximum of constraints of the MapReduce environment. Solutions for the presented model would be a reference for the on-line Schedules in the case of low and medium instances. Our work is useful in term of the problem definition: constraints are based on observations and take into account resources consumption, data locality, heterogeneous machines and workflow management; this paper defines boundaries references to evaluate the online model.
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Paper Citation
in Harvard Style
Jlassi A., Martineau P. and Tkindt V. (2015). Offline Scheduling of Map and Reduce Tasks on Hadoop Systems . In Proceedings of the 5th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-104-5, pages 178-185. DOI: 10.5220/0005483601780185
in Bibtex Style
@conference{closer15,
author={Aymen Jlassi and Patrick Martineau and Vincent Tkindt},
title={Offline Scheduling of Map and Reduce Tasks on Hadoop Systems},
booktitle={Proceedings of the 5th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},
year={2015},
pages={178-185},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005483601780185},
isbn={978-989-758-104-5},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 5th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - Offline Scheduling of Map and Reduce Tasks on Hadoop Systems
SN - 978-989-758-104-5
AU - Jlassi A.
AU - Martineau P.
AU - Tkindt V.
PY - 2015
SP - 178
EP - 185
DO - 10.5220/0005483601780185