Flexible architecture for cluster evolution in cloud computing

Tzu Chi Huang; Sherali Zeadally

doi:10.1016/j.compeleceng.2014.08.006

Flexible architecture for cluster evolution in cloud computing

Tzu Chi Huang, Sherali Zeadally

School of Information Science

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

MapReduce is considered the key behind the success of cloud computing because it not only makes a cluster highly scalable but also allows applications to use resources in a cluster. However, MapReduce achieves this simplicity at the expense of flexibility for data partitioning, localization, and processing procedures by handling all issues on behalf of application developers. Unfortunately, MapReduce currently has no solution capable of giving application developers flexibility in customizing data partitioning, localization, and processing procedures. To address the aforementioned flexibility constraints of MapReduce, we propose an architecture called Flexible Architecture for Cluster Evolution (FACE) which is both language-independent and platform-independent. FACE allows a MapReduce cluster to be designed to match various application requirements by customizing data partitioning, localization, and processing procedures. We compare the performance of FACE with that of a general MapReduce system and then demonstrate performance improvements with our implemented procedures.

Original language	English
Pages (from-to)	90-106
Number of pages	17
Journal	Computers and Electrical Engineering
Volume	42
DOIs	https://doi.org/10.1016/j.compeleceng.2014.08.006
State	Published - Feb 1 2015

Bibliographical note

Funding Information:
We gratefully acknowledge the National Science Council of Taiwan for its support of this project under Grant number NSC 102-2221-E-262-014 . We thank Lunghwa University of Science and Technology for kindly providing us with the hardware equipment used to implement the prototype described in this work. We also thank the anonymous reviewers for their useful comments and Manu Malek for his kind advice and support throughout the preparation of the final revised version of this paper.

Publisher Copyright:
© 2014 Elsevier Ltd. All rights reserved.

Keywords

Architecture
Bandwidth
Cloud computing
Cluster
MapReduce
Operating system

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science (all)
Electrical and Electronic Engineering

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10.1016/j.compeleceng.2014.08.006

Cite this

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title = "Flexible architecture for cluster evolution in cloud computing",

abstract = "MapReduce is considered the key behind the success of cloud computing because it not only makes a cluster highly scalable but also allows applications to use resources in a cluster. However, MapReduce achieves this simplicity at the expense of flexibility for data partitioning, localization, and processing procedures by handling all issues on behalf of application developers. Unfortunately, MapReduce currently has no solution capable of giving application developers flexibility in customizing data partitioning, localization, and processing procedures. To address the aforementioned flexibility constraints of MapReduce, we propose an architecture called Flexible Architecture for Cluster Evolution (FACE) which is both language-independent and platform-independent. FACE allows a MapReduce cluster to be designed to match various application requirements by customizing data partitioning, localization, and processing procedures. We compare the performance of FACE with that of a general MapReduce system and then demonstrate performance improvements with our implemented procedures.",

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KW - Operating system

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Flexible architecture for cluster evolution in cloud computing

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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