A Novel Approach to Green Cloud Computing: QoS-Optimized Load Balancing with Artificial Bee Colony Algorithm

Abstract

This paper uses an artificial bee colony algorithm to present a QoS-oriented technique for load balancing in green cloud computing. The proposed technique aims to optimize workload allocation decisions while minimizing energy consumption and carbon emissions and ensuring QoS requirements are met. The technique involves a fitness function that evaluates the fitness of each solution based on a set of QoS and energy consumption metrics. It also involves the ABC algorithm, which generates novel solutions and explores the search space. The algorithm also applies a dynamic threshold-based approach to adjust the threshold values based on the current system load and QoS requirements. Experimental results demonstrate that the proposed technique outperforms other load-balancing techniques regarding QoS and energy consumption metrics. Compared to other load-balancing techniques, the new technique significantly improved response time, throughput, availability, resource utilization, energy consumption, and carbon emissions. The proposed technique is likely to improve energy efficiency, reduce cloud computing's carbon footprint, and meet quality of service requirements.