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We describe algorithms and an architecture for a real-time problem determination system that uses online selection of most-informative measurements - the approach called herein active probing. Probes are end-to-end test transactions which gather information about system components. Active probing allows probes to be selected and sent on-demand, in response to one's belief about the state of the system. At each step the most informative next probe is computed and sent. As probe results are received, belief about the system state is updated using probabilistic inference. This process continues until the problem is diagnosed. We demonstrate through both analysis and simulation that the active probing scheme greatly reduces both the number of probes and the time needed for localizing the problem when compared with non-active probing schemes.