Current network service requirements are increasing the pressure on the level of flexibility and reliability that must be provided by the virtual computer and network systems that host them while enforcing close-to-optimum resource allocations to minimize both monetary and operational costs. A key requirement is the fast detection and adaptation to workload changes, which is beyond human abilities, so the operation must be automated. To do so we propose to use an autonomic resource control architecture. In this paper, we formalize the architecture and discuss how it is able to detect the CPU load changes of a networked service by analyzing heterogeneous observations, including both resource load measurements and external events, and adapt (increase or decrease) the allocated resources consequently. The key benefit of our proposal is that, in most situations, including external information into the analysis, such as seismometer measurements, allows the control architecture to detect new situations earlier than analyzing only load measurements. We demonstrate such claims with the results obtained from the execution of a proof-of-concept implementation of the architecture we propose on an emergency scenario.