TY - GEN
T1 - Copula-based parameter estimation for Markov-modulated Poisson Process
AU - Dong, Fang
AU - Wu, Kui
AU - Srinivasan, Venkatesh
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2017/7/5
Y1 - 2017/7/5
N2 - Markov-modulated Poisson Process (MMPP) has been extensively studied in random process theory and widely used as a network traffic model. Most methods for estimating MMPP parameters are based on the exact arrival times. Nevertheless, in many applications it is costly to record the exact time of each arrival. Instead, we only record the number of arrivals in fixed-length time slots, which is called arrival count. Since arrival count data does not maintain detailed arrival times, it is non trivial to develop effective methods for MMPP parameter estimation with arrival counts only. Very few existing works deal with this challenge. This paper tackles the above challenge with copula analysis. The theoretical marginal distribution and copula of arrival counts in MMPP are applied to develop a new estimation method, MarCpa, which is a two-step estimation method involving marginal matching followed by copula matching. Our evaluation results demonstrate that the proposed method is fast and accurate. © 2017 IEEE.
AB - Markov-modulated Poisson Process (MMPP) has been extensively studied in random process theory and widely used as a network traffic model. Most methods for estimating MMPP parameters are based on the exact arrival times. Nevertheless, in many applications it is costly to record the exact time of each arrival. Instead, we only record the number of arrivals in fixed-length time slots, which is called arrival count. Since arrival count data does not maintain detailed arrival times, it is non trivial to develop effective methods for MMPP parameter estimation with arrival counts only. Very few existing works deal with this challenge. This paper tackles the above challenge with copula analysis. The theoretical marginal distribution and copula of arrival counts in MMPP are applied to develop a new estimation method, MarCpa, which is a two-step estimation method involving marginal matching followed by copula matching. Our evaluation results demonstrate that the proposed method is fast and accurate. © 2017 IEEE.
KW - Arrival counts
KW - Copula
KW - Markov-modulated poisson process
KW - Parameter estimation
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85027855958&origin=recordpage
U2 - 10.1109/IWQoS.2017.7969116
DO - 10.1109/IWQoS.2017.7969116
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781509019830
T3 - 2017 IEEE/ACM 25th International Symposium on Quality of Service, IWQoS 2017
BT - 2017 IEEE/ACM 25th International Symposium on Quality of Service, IWQoS 2017
PB - IEEE
T2 - 25th IEEE/ACM International Symposium on Quality of Service, IWQoS 2017
Y2 - 14 June 2017 through 16 June 2017
ER -