In this paper, we propose an approach and different tools to evaluate the performance and assess the effectiveness of a model in the field of dynamic cubing. Experimental evaluation, on one hand allows observing the behavior and the performance of the solution, while on the other hand it lets us compare the results with those of the other competing solutions. Our proposal includes an experimental workflow based on a set of configuration parameters to characterize the inputs (data sets, queries sets and algorithm input parameters) and a set of metrics to analyze and qualify the output (performance and behavior metrics) of the solution. We have identified a number of useful tools necessary to develop an experimental evaluation strategy. These monitoring tools allow elaborating the execution scenarios, collecting output metrics and storing and analyzing them online in real-time as well as later in off-line mode. Using a use-case model, we show that the framework and the proposed environment help carrying out a rigorous experimental evaluation of a dynamic cubing solution.