SCRUB: a novel energy-efficient virtual machines selection and migration scheme in cloud data centers

Abstract

The extensive deployment of large cloud data centers has led to substantial energy consumption. Energy conservation is a critical concern for cloud service providers seeking to lower their operating costs within their data centers. To address this energy consumption challenge, effective approaches such as VM consolidation and VM migration are essential. These approaches must carefully balance the trade-off between energy consumption and Service Level Agreement Violations (SLAV). In this paper, we propose an energy-efficient VM selection algorithm for VM consolidation and call it the Simultaneous CPU–Ram Utilization Balancer (SCRUB) policy. This algorithm takes into account CPU and RAM utilization while trying to maintain a balance between energy consumption and SLAV. To evaluate the performance of our proposed method, we implemented it using the Cloudsim simulation toolkit and conducted simulations using real-world workload traces from PlanetLab and Google over three different days. The results show that the SCRUB VM selection policy has led to improvements in various metrics, including reduced energy consumption and a decreased number of VM migrations compared to existing VM selection policies. Specifically, it achieved a 16.98% decrease in energy consumption and a 46.42% reduction in the number of migrations for the PlanetLab dataset, and a 10.95% decrease in energy consumption and a 43.96% decline in the number of migrations for the Google dataset compared to the baseline algorithm MMT.