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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Furkan Rabee
University of Kufa
Electrical & Electronics Engineering

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An innovativefractal architecture model for implementing MapReduce in an open multiprocessing parallel environment Muslim Mohsin Khudhair; Adil AL-Rammahi; Furkan Rabee
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp1059-1067

Abstract

One of the infrastructure applications that cloud computing offers as a service is parallel data processing. MapReduce is a type of parallel processing used more and more by data-intensive applications in cloud computing environments. MapReduce is based on a strategy called "divide and conquer," which uses regular computers, also called "nodes," to do processing in parallel. This paper looks at how open multiprocessing (OpenMP), the best shared-memory parallel programming model for high-performance computing, can be used with the proposed fractal network model in the MapReduce application. A well-known model, the cube, is used to compare the fractal network model and its work. Where experiments demonstrated that the fractal model is preferable to the cube model. The fractal model achieved an average speedup of 2.7 and an efficiency rate of 67.7%. In contrast, the cube model could only reach an average speedup of 2.5 and an efficiency rate of 60.4%.
New efficient fractal models for MapReduce in OpenMP parallel environment Muslim Mohsin Khudhair; Furkan Rabee; Adil AL_Rammahi
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4977

Abstract

Parallel data processing is one of the specific infrastructure applications categorized as a service provided by cloud computing. In cloud computing environments, data-intensive applications increasingly use the parallel processing paradigm known as MapReduce. MapReduce is based on a strategy called "divide and conquer," which uses ordinary computers, also called "nodes," to do processing in parallel. This paper looks at how open multiprocessing (OpenMP), the best shared-memory parallel programming model for high-performance computing, can be used in the MapReduce application using proposed fractal network models. Two fractal network models are offered, and their work is compared with a well-known network model, the hypercube. The first fractal network model achieved an average speedup of 3.239 times while an efficiency ranged from 73-95%. In the second model of the network, the speedup got to 3.236 times while keeping an efficiency of 70-92%. Furthermore, the path-finding algorithm employed in the recommended fractal network models remarkably identified all paths and calculated the shortest and longest routes.
Co-Authors Adil AL-Rammahi Adil AL_Rammahi Muslim Mohsin Khudhair Muslim Mohsin Khudhair