The knowledge of workload fluctuations is fundamental in all performance studies in which the dynantic characteristics of the resource demands must be taken into account. Among the several workload data sequences that may be considered, the arrival pattern of workload components is certainly one of the most important. An approach to the identification of the arrival rate functions through a numerical fitting technique that allows one to have concise representations of the arrival patterns during one-day periods is presented. The variability of the arrival pattern over different days is also investigated. The patterns which may be considered as "representatives" of the analyzed workload are found through the application of the clustering technique. The parametric model of the arrival process may easily be used to forecast the load of the system in the near future or to drive system simulations when several dynamic control policies are to be investigated. The proposed modeling approach has been validated, in the sense of establishing its predictive value, through the analysis of workload data for the same installation collected during different periods.