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A structural approach for modelling the hierarchical dynamic process of Web workload in a large-scale campus network
Affiliation:1. School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510275, China;2. School of Engineering and Information Technology, University of New South Wales at the Australian Defence Force Academy (UNSW@ADFA), Canberra, ACT 2600, Australia;3. Department of Engineering Technology, Missouri Western State University, St. Joseph, MO 64507, USA;4. Network and Information Technology Center, Sun Yat-Sen University, Guangzhou 510275, China;1. Shyama Prasad Mukherji College, University of Delhi, West Punjabi Bagh, New Delhi 110026, India;2. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India;1. Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC;2. Department of Surgery, Wake Forest School of Medicine, Winston-Salem, NC;3. Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC;4. Virginia Tech-Wake Forest University Center for Injury Biomechanics, Winston-Salem, NC;1. AUDI AG, D-85045 Ingolstadt, Germany;2. Catholic University of Eichstätt-Ingolstadt, Ostenstraße 25, D-85072 Eichstätt, Germany;1. School of Rural and Surveying Engineering, Laboratory of Transportation Engineering, 9 Heroon Politechniou Street, 15780 Zografou, Athens, Greece.;2. School of Civil Engineering, Department of Transportation Planning and Engineering, National Technical University of Athens, 5 Iroon Polytechniou Street, 15773 Athens, Greece;3. Transport Research Laboratory, Wokingham RG40 3GA, United Kingdom;1. Special Vehicle Operation, Jaguar Land Rover, Fen End, CV8 1NQ, United Kingdom;2. Institute for Transport Studies, University of Leeds, LS2 9JT, United Kingdom
Abstract: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.
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