HMM-Based Monitoring of Packet Channels

Performance of real-time applications on network communication channels are strongly related to losses and temporal delays. Several studies showed that these network features may be correlated and exhibit a certain degree of memory such as bursty losses and delays. The memory and the statistical dependence between losses and temporal delays suggest that the channel may be well modelled by a Hidden Markov Model (HMM) with appropriate hidden variables that capture the current state of the network. In this paper we discuss on the effectiveness of using an HMM to model jointly loss and delay behavior of real communication channel. Excellent performance in modelling typical channel behavior in a set of real packet links are observed. The system parameters are found via a modified version of the EM algorithm. Hidden state analysis shows how the state variables characterize channel dynamics. State-sequence estimation is obtained by use of the Viterbi algorithm. Real-time modelling of the channel is the first step to implement adaptive communication strategies.

[1]  Louis A. Liporace,et al.  Maximum likelihood estimation for multivariate observations of Markov sources , 1982, IEEE Trans. Inf. Theory.

[2]  Jeff A. Bilmes,et al.  A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models , 1998 .

[3]  E. O. Elliott.  Estimates of error rates for codes on burst-noise channels , 1963 .

[4]  Francesco Palmieri,et al.  Bayesian Modelling for Packet Channels , 2003, WIRN.

[5]  Ibrahim Matta,et al.  End-to-End Inference of Loss Nature in a Hybrid Wired/Wireless Environment , 2002 .

[6]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[7]  G. Iannello,et al.  A hidden Markov model for Internet channels , 2003, Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology (IEEE Cat. No.03EX795).

[8]  Jean-Chrysostome Bolot,et al.  Characterizing End-to-End Packet Delay and Loss in the Internet , 1993, J. High Speed Networks.

[9]  L. B. Milstein,et al.  On the accuracy of a first-order Markov model for data transmission on fading channels , 1995, Proceedings of ICUPC '95 - 4th IEEE International Conference on Universal Personal Communications.

[10]  Wenyu Jiang,et al.  Modeling of Packet Loss and Delay and Their Effect on Real-Time Multimedia Service Quality , 2000 .

[11]  Kavé Salamatian,et al.  Hidden Markov modeling for network communication channels , 2001, SIGMETRICS '01.

[12]  Antonio Pescapè,et al.  Analysis and experimentation of Internet Traffic Generator , 2003 .

[13]  Biing-Hwang Juang,et al.  Maximum likelihood estimation for multivariate mixture observations of markov chains , 1986, IEEE Trans. Inf. Theory.

[14]  Vern Paxson,et al.  End-to-end Internet packet dynamics , 1997, SIGCOMM '97.

[15]  E. Gilbert.  Capacity of a burst-noise channel , 1960 .