Reducing Cloud Expenditures and Carbon Emissions via Virtual Machine Migration and Downsizing
Nathan Huang, A. Li, Sophia Zhang, Ziliang Zong · 2023
Cloud computing has grown at an unprecedented rate in recent years, which leads to two new significant challenges. First, cloud expenditure is increasing rapidly and reducing cloud expenditure has become the top priority of enterprises relying on cloud services. Second, cloud computing consumes significant amounts of energy and generates massive carbon emissions. This paper presents a comprehensive study to reduce the cost and carbon emissions of cloud computing. Through analyzing over 2.6 million real-world Azure virtual machines (VMs) and carbon intensity data provided by WattTime, it proposes a metric to quantify cloud waste and trains a machine learning model to estimate the power consumption and carbon emissions of VMs. Furthermore, it proposes two VM scheduling algorithms, Reschedule by Threshold (RT) and Reschedule by Averages (RA), to reduce the carbon emissions of cloud workloads, and the other two algorithms, the Shutdown (SD) algorithm and the Core Reduction (CR) algorithm, to reduce both cloud cost and carbon emissions. The simulation results running on the 2019 Azure VM trace demonstrate that the proposed algorithms could reduce nearly 3.5 million pounds of CO2 emissions and help cloud users save approximately ${\$}$13.8 million dollars per month.