Copula theory and its applications in computer networks

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dc.contributor.authorDong, Fang
dc.contributor.supervisorWu, Kui
dc.contributor.supervisorSrinivasan, Venkatesh
dc.date.accessioned2017-07-12T14:38:11Z
dc.date.available2017-07-12T14:38:11Z
dc.date.copyright2017en_US
dc.date.issued2017-07-12
dc.degree.departmentDepartment of Computer Scienceen_US
dc.degree.levelDoctor of Philosophy Ph.D.en_US
dc.description.abstractTraffic modeling in computer networks has been researched for decades. A good model should reflect the features of real-world network traffic. With a good model, synthetic traffic data can be generated for experimental studies; network performance can be analysed mathematically; service provisioning and scheduling can be designed aligning with traffic changes. An important part of traffic modeling is to capture the dependence, either the dependence among different traffic flows or the temporal dependence within the same traffic flow. Nevertheless, the power of dependence models, especially those that capture the functional dependence, has not been fully explored in the domain of computer networks. This thesis studies copula theory, a theory to describe dependence between random variables, and applies it for better performance evaluation and network resource provisioning. We apply copula to model both contemporaneous dependence between traffic flows and temporal dependence within the same flow. The dependence models are powerful and capture the functional dependence beyond the linear scope. With numerical examples, real-world experiments and simulations, we show that copula modeling can benefit many applications in computer networks, including, for example, tightening performance bounds in statistical network calculus, capturing full dependence structure in Markov Modulated Poisson Process (MMPP), MMPP parameter estimation, and predictive resource provisioning for cloud-based composite services.en_US
dc.description.proquestcode0984en_US
dc.description.proquestemailfdong@uvic.caen_US
dc.description.scholarlevelGraduateen_US
dc.identifier.bibliographicCitationFang Dong, Kui Wu, Venkatesh Srinivasan, and Jianping Wang. "Copula Analysis of Latent Dependency Structure for Collaborative Auto-scaling of Cloud Services", in 2016 25th International Conference on Computer Communication and Networks (ICCCN), August 2016.en_US
dc.identifier.bibliographicCitationFang Dong, Kui Wu, Venkatesh Srinivasan. "Copula-based Parameter Estimation for Markov-modulated Poisson Process", in Proceedings of IEEE/ACM International Symposium on Quality of Service (IWQoS), June 2017.en_US
dc.identifier.bibliographicCitationFang Dong, Kui Wu, Venkatesh Srinivasan. "Copula Analysis of Temporal Dependence Structure in Markov Modulated Poisson Process and Its Applications", ACM Transactions on Modeling and Performance Evaluation of Computing Systems (ToMPECS), accepted in May 2017.en_US
dc.identifier.bibliographicCitationFang Dong, Kui Wu, and Venkatesh Srinivasan. "Copula Analysis for Statistical Network Calculus", in 2015 IEEE Conference on Computer Communications (INFOCOM), April 2015.en_US
dc.identifier.urihttp://hdl.handle.net/1828/8319
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectCopula Analysisen_US
dc.subjectNetwork Calculusen_US
dc.subjectMarkov Modulated Poisson Processen_US
dc.subjectTraffic Predictionen_US
dc.subjectParameter Estimationen_US
dc.subjectCloud Service Provisioningen_US
dc.subjectContemporaneous Dependence Modelingen_US
dc.subjectTemporal Dependence Modelingen_US
dc.titleCopula theory and its applications in computer networksen_US
dc.typeThesisen_US

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