Active Network technology envisions deployment of virtual execution environments within network elements, so that nonhomogeneous processing can be applied to network traffic. For management purposes, each node must have a meaningful understanding of resource requirements in terms of bandwidth, memory, and processing. To express the processing requirements in a platform-independent manner, we are developing a model of CPU time usage, which comes in two parts: a node model and an application model. In order to generate instances of the model, one needs to gather some metrics relative to the platform, that is, to calibrate a node. We have investigated what factors this process of calibration should account for, and especially how background load on a node affects our ability to obtain accurate calibrations for the CPU time used by node operating system calls and by virtual execution environments. We have shown that a background load, either computation intensive or input/output intensive, has little influence on the calibration. On the contrary, a memory consuming background load introduces an overhead in some measurements. The paper draws the conclusion that the calibration of a node can be done whatever the background load, provided that the memory consuming loads can be suppressed if necessary.