Eco-Aware Load Balancing for Distributed Cloud Data Centers with Renewables

Abstract

Cloud data centers traditionally use grid energy which emits carbon dioxide and dissipates heat to the environment. To decrease the amount of carbon emissions, many Cloud Service Providers (CSPs) are exploring the possibility of using renewable energy to operate their data centers showing a clear trend to migrate towards green cloud datacenters. Green cloud data centers typically use solar and wind as renewable energy sources. To meet the highly dynamic user demand and to ensure effective power management, an efficient methodology is proposed that performs various load scheduling and power management plans with the integration of renewables for geographically distributed green cloud datacenters. The proposed approach applies nature-inspired Modified Bacterial Foraging Optimization Algorithm (MBFOA) to balance load and reduce eco-aware energy cost. Simulations were conducted to determine the efficacy of the proposed MBFOA algorithm. The algorithm efficiently reduces eco-aware power cost of the system than the baseline methods.