TY  - JOUR
T1  - Big Data Clustering Using Grid Computing and Bionic Algorithms
Based an Entropic Optimization Technique
AU - Darwish, Saad M. AU - F. Ashry, Moustafa AU - El-Zoghabi, Adel A. 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 11
SP  - 4080
EP  - 4092
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.4080.4092
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.4080.4092
KW  - load balancing
KW  -bionic algorithm
KW  -big data
KW  -Grid computing
KW  -fault tolerance
KW  -significant
KW  -concerning
AB  - 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.
ER  -