Energy-Aware Parallel Computing for Cloud Architecture using ANN

In parallel processing, task scheduling is a way to assign task to different processors. A number of algorithms are used by parallel computing so that the time for executing jobs can be minimized. Presently, there are number of algorithms that are utilized for the enhancement of the execution time of task as well as to reduce the energy utilization. In this research, to resolve the energy consumption problem, the concept of load balancing has been introduced along with the Artificial Bee Colony (ABC) and Artificial Neural Network (ANN) techniques. ABC is used to optimize the properties of processors based on the appropriate selection of fitness function. On the basis of these optimized properties, ANN is trained and hence balance the load so that energy can be optimized along with minimization of makespan.