Energy-Aware Resource Allocation in Cloud Data Centers

Abstract

Cloud data centers consume massive energy as workloads continue to grow. This study explores an energy-aware resource allocation framework that combines workload prediction and dynamic VM consolidation to reduce power consumption without compromising performance. The model uses historical utilization patterns to forecast demand and allocates resources accordingly. Experimental results on real datasets show noticeable reductions in energy use and SLA violations. The work contributes to sustainable cloud computing by balancing efficiency and reliability.