• Feb 08, 2023 News!IJCTE Vol. 14, No. 4 has been indexed by SCOPUS.   [Click]
  • Feb 01, 2023 News!IJCTE Vol.15, No.1 has been published.   [Click]
  • Nov 29, 2022 News!IJCTE Vol. 14, No. 1-No. 3 have been indexed by SCOPUS.   [Click]
General Information
    • ISSN: 1793-8201 (Print)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta
    • Executive Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • E-mail: ijcte@iacsitp.com
Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2016 Vol.8(5): 423-428 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2016.V8.1083

HPC (High-Performance the Computing) for Big Data on Cloud: Opportunities and Challenges

Mohamed Riduan Abid

Abstract—Big data and Cloud computing are emerging as new promising technologies, gaining noticeable momentum in nowadays IT. Nowadays, and unprecedentedly, the amount of produced data exceeds all what has been generated since the dawn of computing; a fact which is mainly due to the pervasiveness of IT usage and to the ubiquity of Internet access. Nevertheless, this generated big data is only valuable if processed and mined. To process and mine big data, substantial HPC (high-performance computing) power is needed; a faculty which is not that affordable for most, unless we adopt for a convenient venue, e.g., cloud computing. In this paper, we propose a blue print for deploying a real-world HPC testbed. This will help simulating and evaluating HPC relevant concerns with minimum cost.
   Indeed, cloud computing provides the unique opportunity for circumventing the initial cost of owning private HPC platforms for big data processing, and this by providing HPC as a service (HPCaaS). In this paper, we present the subtleties of a synergetic “fitting” between big data and cloud computing. We delineate opportunities and address relevant challenges. To concretize, we advocate using private clouds instead of public ones, and propose using Hadoop along with MapReduce, on top of Openstack, as a promising venue for scientific communities to own research-oriented private clouds meant to provide HPCaaS for Big data mining.

Index Terms—High-performance computing, cloud computing, big data, Hadoop.

Mohamed Riduan Abid is with Alakhawayn University, Ifrane, Morocco (e-mail: R.Abid@aui.ma).


Cite:Mohamed Riduan Abid, "HPC (High-Performance the Computing) for Big Data on Cloud: Opportunities and Challenges," International Journal of Computer Theory and Engineering vol. 8, no. 5, pp. 423-428, 2016.

Copyright © 2008-2023. International Association of Computer Science and Information Technology. All rights reserved.