@article{MAKHILLJEAS2018131116301,
    title = {Big Data Clustering Using Grid Computing and Bionic Algorithms
Based an Entropic Optimization Technique},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {11},
    pages = {4080-4092},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.4080.4092},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.4080.4092},
    author = {Saad M.,Moustafa and},
    keywords = {load balancing,bionic algorithm,big data,Grid computing,fault tolerance,significant,concerning},
    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.}
    }