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This study examines energy use in enterprise computing systems through measuring and analyzing power consumption across device types. Data from a hybrid sensor network in an academic building is used to extrapolate power share by device type, including PCs, LCDs, servers, and switches. Various measurements, utilization rates, and workloads are discussed, highlighting the importance of sampling frequency and size for accurate data interpretation.
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Measuring and analyzing the energy use of enterprise computing systems Xiuming Zhang
SystemDeployment • Powernet:a hybrid sensor network that monitors the power and utilization of the IT systems in a large academic building • Over more than two years • 250+individual computing devices • Onepowermeterperdevice
Power Share by Type • 4 types: PC, LCD, server, switch
PC • Binned into 3 categories • Laptops • Low-end • High-end • 742 in total • 456: description available. The other: assume same distribution.
LCD • Lower brightness & use dark backgrounds
Server • Monitors 32 of the 500 servers • Calculated average power: 233 W • Estimated total power of 500 servers: 117 kW
PCCPU Utilization • 95%ofthecomputershaveautilizationratelowerthan30%
NetworkSwitchUtilization • The demand never exceeded 200 Mbps
NetworkSwitchUtilization • Highly underutilized • Total network demand <1000 Mbps 100% of the time.
How does the sampling frequency affect what the data reveal? • Too small hide the anomalies
How big is the variance between two instances of same model?
Does sampling a few devices provide an accurate average measurement? shows the wide distribution of desktop power
Does sampling a few devices provide an accurate average measurement? • Large sample size desired for PC • 1,000,000 random samples of size 5, 10, and 20, drawing from 69 machines
Do short-term measurements accurately reflect long-term power draw? • One-month scale yields an acceptable error
Are Energy Star data representative? • Energy Star Standard does not consider PCs under load