A structural approach for modelling the hierarchical dynamic process of Web workload in a large-scale campus network

A new structural approach based on hidden Markov model is proposed to describe the hierarchical nature of dynamic process of Web workload. The proposed approach includes two latent Markov chains and one observable process. One of the latent Markov chains is called macro-state process which is used to describe the large-scale trends of Web workload. The remaining latent Markov chain is called sub-state process which is used to describe the small-scale fluctuations that are happening within the duration of a given macro-state. An efficient parameter re-estimation algorithm and a workload simulation algorithm are derived for the proposed discrete model. Experiments based on a real workload of a large-scale campus network are implemented to validate the proposed model.