A green energy model for resource allocation in computational grid using differential evolution algorithm

Energy loss due to the complete equipping and overstatement of infrastructure networks has recently prompted internet service providers to reduce energy consumption. Increasing energy costs and environmental awareness have recently raised concerns about grid power consumption and the underlying structure of services; therefore, one of the top priorities for service providers is to reduce energy consumption. In this paper, a method based on the differential evolution optimization algorithm is introduced to reduce the energy consumption costs, network load balancing, and execution time. To increase the convergence speed in the optimization algorithm, two Mamdani fuzzy inference systems have been used which are used in the optimization objective function to determine the fitness amount of each allocation. To better understand proposed algorithm behavior, the results of the differential evolution algorithm are compared with the AGA and MPGA methods. Simulation results show that the proposed algorithm achieves more than 30% performance improvement in comparison with the previous algorithms.