Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
More effective marketing, along with new revenue opportunities, enhanced customer service, improved operational efficiency, competitive advantages over peer organizations and huge business benefits are the outcome of the analytical findings. The organizations performance is raised to the maximum using big data which transforms the tremendous amounts of data into knowledge. Performance and utilization of the grid computing are basically dependent on a complex and excessively dynamic way of optimally balancing the load between the available nodes. This study introduces a framework for big data clustering which utilizes grid technology and bionic based algorithms. Analysis of Genetic agorithm, ant colony optimization and particle swarm optimization are implemented regarding to their solutions, issues and improvements concerning load balancing in computational grid. Consequently, a significant system utilization improvement was attained.
How to cite this article:
Saad M. Darwish, Moustafa F. Ashry and Adel A. El-Zoghabi. Big Data Clustering Using Grid Computing and Bionic Algorithms
Based an Entropic Optimization Technique.
DOI: https://doi.org/10.36478/jeasci.2018.4080.4092
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2018.4080.4092