New models for pseudo self-similar traffic

Robert, Stephan; Boudec, Jean-Yves Le

doi:10.1016/s0166-5316(96)00055-7

Cited by 77 publications

(62 citation statements)

References 5 publications

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“…Accurate modeling of IP traffic requires matching closely not only the packet arrival process but also the packet size distribution. Surprisingly, while the arrival process has received considerable attention [1][2][3][4][5][6][7][8][9][10], very few works have addressed the packet size distribution and, more so, its joint characterization with the arrival process [11,12]. However, as it will be made clear in this paper, a joint characterization is required for accurate prediction of the queuing behavior (i.e., the packet loss ratio or average packet delay observed in a queuing system).…”

Section: Introductionmentioning

confidence: 99%

Modeling IP traffic: joint characterization of packet arrivals and packet sizes using BMAPs

2004

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This paper proposes a traffic model and a parameter fitting procedure that are capable of achieving accurate prediction of the queuing behavior for IP traffic exhibiting long-range dependence. The modeling process is a discrete-time batch Markovian arrival process (dBMAP) that jointly characterizes the packet arrival process and the packet size distribution. In the proposed dBMAP, packet arrivals occur according to a discrete-time Markov modulated Poisson process (dMMPP) and each arrival is characterized by a packet size with a general distribution that may depend on the phase of the dMMPP. The fitting procedure is designed to provide a close match of both the autocovariance and the marginal distribution of the packet arrival process, using a dMMPP; a packet size distribution is fitted individually to each state of the dMMPP. A major feature of the procedure is that the number of states of the fitted dBMAP is not fixed a priori; it is determined as part of the procedure itself. In this way, the procedure allows establishing a compromise between the accuracy of the fitting and the number of parameters, while maintaining a low computational complexity.We apply the inference procedure to several traffic traces exhibiting long-range dependence. Very good results were obtained since the fitted dBMAPs match closely the autocovariance, the marginal distribution and the queuing behavior of the measured traces. Our results also show that ignoring the packet size distribution and its correlation with the packet arrival process can lead to large errors in terms of queuing behavior.

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Section: Introductionmentioning

confidence: 99%

Modeling IP traffic: joint characterization of packet arrivals and packet sizes using BMAPs

2004

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“…For the channel occupancy (PU behavior), we use the model proposed in [35], which is shown to exhibit a selfsimilar behavior over a finite but wide enough range of time-scales. Thus the block-matrices in (1) are given as, Tables 2 and 3 show the settings of a basic scenario.…”

Section: Resultsmentioning

confidence: 99%

Discrete time analysis of cognitive radio networks with imperfect sensing and saturated source of secondary users

Alfa

Pla

Martínez-Bauset

et al. 2016

Computer Communications

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Sensing is one of the most challenging issues in cognitive radio networks. Selection of sensing parameters raises several tradeoffs between spectral efficiency, energy efficiency and interference caused to primary users (PUs). In this paper we provide representative mathematical models that can be used to analyze sensing strategies under a wide range of conditions. The activity of PUs in a licensed channel is modeled as a sequence of busy and idle periods, which are represented as alternating Markov phase renewal processes. The representation of the secondary users (SUs) behavior is also largely general: the duration of transmissions, sensing periods and the intervals between consecutive sensing periods are modeled by phase type distributions, which constitute a very versatile class of distributions. Expressions for several key performance measures in cognitive radio networks are obtained from the analysis of the model. Most notably, we derive the distribution of the length of an effective white space; the distributions of the waiting times until the SU transmits a given amount of data, through several transmission epochs or uninterruptedly; and the goodput when an interrupted SU transmission has to be restarted from the beginning due to the presence of a PU.

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“…Field studies done on the sojourn time of idle and busy times have shown that this assumption of exponentially distributed sojourn times of channel states cannot be taken for granted [38]. Therefore to find the closest match for the sojourn times a distribution fitting must be done and due to the fact that most data networks have self similarity none of the above discussed models will be close enough approximations [39].…”

Section: Prediction Of Channel Status Using An Assumed Channel Usage mentioning

confidence: 99%

“…For the simulation purpose we developed the Markovian arrival process as given in [39,45]. That particular…”

Section: Creating the Training Sequence Using The Markovian Arrival Pmentioning

confidence: 99%

Predictive Channel Access in Cognitive Radio Networks Based on Variable Order Markov Models

Devanarayana

Alfa

2011

2011 IEEE Global Telecommunications Conference - GLOBECOM 2011

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The concept of Cognitive radio enables the unlicensed users to share the spectrum with licensed users, on the condition that the licensed users have preemptive priority. The use of the channel by unlicensed users should not result in more than acceptable interference level to the licensed users, if interference occurs. The sense and react strategy by unlicensed users sometimes does not lead to acceptable level of interference while maintaining an acceptable data transfer rate for the unlicensed users.Proactive channel access has been proposed for the purpose of reducing the interference to primary users and to reduce the idle channel search delay for the secondary users. There are many methods used in the literature to model the channel state fluctuations. Based on these models the future channel states are predicted.In this thesis we introduce a predictive channel usage scheme which is capable of reducing the interference caused by the unlicensed users. Furthermore our scheme is capable of increasing the data rates the unlicensed users experience through the reduction of the idle channel identification delay. In our scheme no assumptions are made about the distribution of licensed user channel usage. We learn the traffic characteristics of the channels using a learning scheme called Probabilistic Suffix Tree algorithm.iii

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New models for pseudo self-similar traffic

Cited by 77 publications

References 5 publications

Modeling IP traffic: joint characterization of packet arrivals and packet sizes using BMAPs

Modeling IP traffic: joint characterization of packet arrivals and packet sizes using BMAPs

Discrete time analysis of cognitive radio networks with imperfect sensing and saturated source of secondary users

Predictive Channel Access in Cognitive Radio Networks Based on Variable Order Markov Models

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