Modelling and forecasting of cloud data warehousing load

Cloud data storages in their internal structure are not using their full potential functionality because of the complexity of behavior of network traffic, which affects the quality of service. The paper describes various models of network traffic and analyzes the most promising models for cloud data storages that take into account the phenomenon of self-similarity. The result of research found the frequency of cloud data warehouse traffic and that the intensity of storage load mainly depends on the incoming and outgoing traffic. Sufficiently high value of Hurst parameter indicates the potential for modelling and prediction of con-gestion cloud data storage in the long run.