Optimizing Cloud Data Center Energy Efficiency via Dynamic Prediction of CPU Idle Intervals

The energy consumption of cloud data centers has been growing drastically in recent years. In particular, CPUs are the most power hungry components in the data center. On the one hand, CPUs are not energy proportional with respect to their utilization levels because a cloud server´s energy efficiency is much lower with limited CPU utilizations. On the other hand, current cloud computing applications usually exhibit significant CPU idle time composed of idle intervals of variable lengths. The power consumption in these idle intervals is significant due to the prominent leakage current in recent technology nodes. There are a few existing schemes that transition a CPU into various low-power and sleep states to reduce its idle power. But none of them is optimal due to the fact that entering a sleep state may result in negative power savings if its wake-up latency is longer than the current idle interval. Therefore, intelligent sleep state entry is a key challenge in improving data centers´ CPU energy efficiency. In this work, we propose a dynamic idle interval prediction scheme that can estimate future CPU idle interval lengths and thereby choose the most cost-effective sleep state to minimize power consumption at runtime. Experiments show that our proposed approach can significantly outperform other schemes, achieving 10% - 50% power savings compared to DVFS for a variety of CPU idle patterns. Of short and variable idle intervals. The power consumption in these idle intervals is significant due to leakage power being prominent in recent technologies. Therefore, we study a number of schemes that transition the CPU into various low power and sleep states to reduce the CPU idle power. Entering a sleep state may result in negative power savings if its wakeup latency is longer than the current idle interval. Therefore, intelligent sleep state entry is a key challenge in improving data center CPU energy usage. In this work, we propose a dynamic idle interval prediction sc- eme that can forecast the current CPU idle interval length and thereby choose the most cost-effective sleep state for achieving the minimized power consumption during runtime. Our proposed approach largely outperforms other schemes examined, achieving 10% - 50% power savings compared to DVFS when using various CPU idle patterns. Our future work includes developing the real predictor in a Cloud simulation environment to provide a detailed and flexible evaluation platform for future studies.