A New Multi-Objective Optimal Programming Model for Task Scheduling using Genetic Gray Wolf Optimization in Cloud Computing

Nowadays, the cloud computing has emerged as the advanced form of distributed computing, grid computing, utility computing and virtualization. Efficient task scheduling algorithms would help reduce the number of virtual machines used and in turn reduce the cost and increase the fitness function. According to this, a new multi-objective function is proposed combining load utilization, energy consumption, migration cost and time. Using this objective function, we proposed a hybrid algorithm namely Genetic Gray Wolf Optimization Algorithm (GGWO) by combining Gray Wolf Optimizer (GWO) and Genetic Algorithm (GA). The performance of the algorithm is analyzed based on the different evaluation measures. The algorithm such as GWO and GA algorithm is compared with proposed GGWO and it is taken for the comparative analysis. To improve the performance analysis the work has been computed with five common scientific workflows such as LIGO, Montage, Epigenomics, SIPHT and Cybershake. Experiments show that GGWO can improve task scheduling when compared with standard GWO and GA with minimum computation time, migration cost, energy consumption and maximum load utilization.