An Efficient Big Data Analytics in Grid Framework Using Ant Colony Optimization (ACO) Algorithm

Abstract

Big Data is about the volume, variety and speed of knowledge being generated today and the potential that arises from the effective use of data for insight and competitive advantage. Big data represents a new generation of technologies and architectures designed to economically extract value from these very broad and complex data volumes by allowing high-speed collection, discovery and/or analysis. In big data, the word is significant for three key characteristics. The architecture for the processing of big data consists of a variety of software resources which will be discussed in this study and briefly outlined. This is the field where the use of grid technology can help. Grid computing refers to a special form of distributed computing. The main goal of this article is to present a way of processing big data using grid technologies. In order to do this, the structure for handling big data will be presented along with the way it will be applied around the grid architecture.

But there are so many challenges in dealing with big data, such as storing, transferring, handling and manipulating big data. Many techniques are needed to explore hidden trends within big data that have limitations in hardware and software implementation. Big data has been applied in the proposed work using grid framework developments and the Ant Colony Optimization (ACO) algorithm. The performance of the big data and grid framework system integration is evaluated using the big data analytics parameters and has shown that the grid framework is effectively supported by the big data process.