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Authors: Yulu Hao ; Meina Song ; Jing Han and Junde Song

Affiliation: Beijing University of Posts and Telecommunications, China

Keyword(s): Cloud computing, The computing model, Hadoop, Task scheduling algorithms.

Abstract: Cloud Computing is a great revolution in the IT field, which means that, computing can be used as a commodity, like coal, gas or electricity. As an efficient computing model of Cloud Computing, within HDFS and the Map/Reduce system, Hadoop is an-open source code architecture for very large data process application. In this paper, we will do specific research on the Cloud Computing based on Hadoop and through the implement of it, optimize the task scheduling algorithms of this architecture. Through this paper, a better algorithm and more practical ways based on Hadoop to build up a Cloud Computing model are achieved.

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Paper citation in several formats:
Hao, Y.; Song, M.; Han, J. and Song, J. (2011). A CLOUD COMPUTING MODEL BASED ON HADOOP WITH AN OPTIMIZATION OF ITS TASK SCHEDULING ALGORITHMS. In Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS 2011) - Volume 2: SSE; ISBN 978-989-8425-53-9; ISSN 2184-4992, SciTePress, pages 524-528. DOI: 10.5220/0003586805240528

@conference{sse11,
author={Yulu Hao. and Meina Song. and Jing Han. and Junde Song.},
title={A CLOUD COMPUTING MODEL BASED ON HADOOP WITH AN OPTIMIZATION OF ITS TASK SCHEDULING ALGORITHMS},
booktitle={Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS 2011) - Volume 2: SSE},
year={2011},
pages={524-528},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003586805240528},
isbn={978-989-8425-53-9},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS 2011) - Volume 2: SSE
TI - A CLOUD COMPUTING MODEL BASED ON HADOOP WITH AN OPTIMIZATION OF ITS TASK SCHEDULING ALGORITHMS
SN - 978-989-8425-53-9
IS - 2184-4992
AU - Hao, Y.
AU - Song, M.
AU - Han, J.
AU - Song, J.
PY - 2011
SP - 524
EP - 528
DO - 10.5220/0003586805240528
PB - SciTePress

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