The increase in computing capacity caused a rapid and sudden increase in the Operational Expenses (OPEX) of data centers. OPEX reduction is a big concern and a key target in modern data centers. In this study, the sca...The increase in computing capacity caused a rapid and sudden increase in the Operational Expenses (OPEX) of data centers. OPEX reduction is a big concern and a key target in modern data centers. In this study, the scalability of the Dynamic Voltage and Frequency Scaling (DVFS) power management technique is studied under multiple different workloads. The environment of this study is a 3-Tier data center. We conducted multiple experiments to find the impact of using DVFS on energy reduction under two scheduling techniques, namely: Round Robin and Green. We observed that the amount of energy reduction varies according to data center load. When the data center load increases, the energy reduction decreases. Experiments using Green scheduler showed around 83% decrease in power consumption when DVFS is enabled and DC is lightly loaded. In case the DC is fully loaded, in which case the servers’ CPUs are constantly busy with no idle time, the effect of DVFS decreases and stabilizes to less than 10%. Experiments using Round Robin scheduler showed less energy saving by DVFS, specifically, around 25% in light DC load and less than 5% in heavy DC load. In order to find the effect of task weight on energy consumption, a set of experiments were conducted through applying thin and fat tasks. A thin task has much less instructions compared to fat tasks. We observed, through the simulation, that the difference in power reduction between both types of tasks when using DVFS is less than 1%.展开更多
文摘The increase in computing capacity caused a rapid and sudden increase in the Operational Expenses (OPEX) of data centers. OPEX reduction is a big concern and a key target in modern data centers. In this study, the scalability of the Dynamic Voltage and Frequency Scaling (DVFS) power management technique is studied under multiple different workloads. The environment of this study is a 3-Tier data center. We conducted multiple experiments to find the impact of using DVFS on energy reduction under two scheduling techniques, namely: Round Robin and Green. We observed that the amount of energy reduction varies according to data center load. When the data center load increases, the energy reduction decreases. Experiments using Green scheduler showed around 83% decrease in power consumption when DVFS is enabled and DC is lightly loaded. In case the DC is fully loaded, in which case the servers’ CPUs are constantly busy with no idle time, the effect of DVFS decreases and stabilizes to less than 10%. Experiments using Round Robin scheduler showed less energy saving by DVFS, specifically, around 25% in light DC load and less than 5% in heavy DC load. In order to find the effect of task weight on energy consumption, a set of experiments were conducted through applying thin and fat tasks. A thin task has much less instructions compared to fat tasks. We observed, through the simulation, that the difference in power reduction between both types of tasks when using DVFS is less than 1%.
