A novel approach for energy consumption management in cloud centers based on adaptive fuzzy neural systems

Abstract

Cloud computing enables global access to tool-based IT services, accommodating a wide range of applications across consumer, scientific, and commercial sectors, operating on a pay-per-use model. However, the substantial energy consumption of data centers hosting cloud applications leads to significant operational costs and environmental impact due to carbon emissions. Each day, these centers handle numerous requests from diverse users, necessitating powerful servers that consume substantial energy and associated peripherals. Efficient resource utilization is essential for mitigating energy consumption in cloud centers. In our research, we adopted a novel hybrid approach to dynamically allocate resources in the cloud, focusing on energy reduction and load prediction. Specifically, we employed neural fuzzy systems for load prediction and the ant colony optimization algorithm for virtual machine migration. Comparative analysis against existing literature demonstrates the effectiveness of our approach. Across 810 time periods, our method exhibits an average resource loss reduction of 21.3% and a 5.6% lower average request denial rate compared to alternative strategies. Using the PlanetLab workload and the created CloudSim simulator, the suggested methods have been assessed. Moreover, our methodology was validated through comprehensive experiments using the SPECpower benchmark, achieving over 98% accuracy in forecasting energy consumption for the proposed model. These results underscore the practicality and efficiency of our strategy in optimizing cloud resource management while addressing energy efficiency challenges in data center operations.