The objective of this project is to provide traffic models based on new services characteristics. Specifically, we focus on modelling the traffic between origin-destination node pairs (also known as OD pairs) in a telecom network. Two use cases are distinguished: i) traffic generation in the context of simulation, and ii) traffic modelling for prediction in the context of big-data backed telecom cloud systems. To this aim, several machine learning and statistical models and technics are studied and combined in order to find the best approach for every use case. To evaluate the applicability of selected models, we integrated them in an OMNeT++ network simulator whose implementation follows the Big Data analytics architecture for the telecom cloud.