Flexible architecture for cluster evolution in cloud computing

Tzu Chi Huang, Sherali Zeadally

Research output: Contribution to journalArticlepeer-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 languageEnglish
Pages (from-to)90-106
Number of pages17
JournalComputers and Electrical Engineering
Volume42
DOIs
StatePublished - Feb 1 2015

Bibliographical note

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
  • General Computer Science
  • Electrical and Electronic Engineering

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