Date Added: Jan 2011
Several projects involving high-level thermal management - such as eliminating "Hot spots" or reducing cooling costs through intelligent workload placement - require ambient air temperature readings at a fine granularity. Unfortunately, current thermal instrumentation methods involve installing a set of expensive hardware sensors. Modern motherboards include multiple on-board sensors, but the values reported by these sensors are dominated by the thermal effects of the server's workload. The authors propose using machine learning methods to model the effects of server workload on on-board sensors.