Analysis and Modeling of QoS Parameters in VoIP Traffic

2011 ◽  
pp. 1-22
Author(s):  
Homero Toral-Cruz ◽  
Deni Torres-Román ◽  
Leopoldo Estrada-Vargas
Keyword(s):  
Markov Chains ◽  
Packet Loss ◽  
Transition Probabilities ◽  
Haar Wavelet ◽  
Ip Networks ◽  
Packet Loss Rate ◽  
The Other ◽  
Hurst Parameter ◽  
Qos Parameters ◽  
Self Similar

Our studies have revealed that VoIP jitter can be modeled by self-similar processes, and through a decomposition based on Haar wavelet it is shown a possible reason of the presence of long range dependence (LRD) in VoIP jitter. On the other hand, we used a description of VoIP packet loss based on microscopic and macroscopic packet loss behaviors, where these behaviors can be modeled by 2-state and 4-state Markov chains, respectively. Besides, the distributions of the number of consecutive received and lost packets (namely gap and burst, respectively) are modeled from the transition probabilities of 2-state and 4-state Markov chains. Based on the above mentioned description, we presented a methodology for simulating packet loss and proposed a new model that allows to relate the Hurst parameter (H) with the packet loss rate (PLR). These models can be used by other researchers as input to problems related to the design of VoIP applications, performance evaluation of IP networks, and the implementation of QoS mechanisms on convergent networks.

A Solution for Evaluating the QoS of Voice over IP

2011 ◽  
pp. 378-396
Author(s):  
Homero Toral-Cruz ◽  
Deni Torres-Román ◽  
Leopoldo Estrada-Vargas
Keyword(s):  
Packet Loss ◽  
Empirical Relationship ◽  
Internet Protocol ◽  
Voice Over Ip ◽  
Packet Loss Rate ◽  
Mathematical Background ◽  
Voice Traffic ◽  
Power Law Function ◽  
Self Similar

In this chapter, we present a solution for evaluating the Quality of Services (QoS) of Voice over Internet Protocol (VoIP). First, we present an introduction to the main concepts and mathematical background relating to QoS and Internet Protocol (IP) traffic nature, which subsequently are used in the measurements, analysis, and modeling of VoIP traffic. Secondly, we analyze network measurements and the result of the simulation in order to characterize the VoIP traffic nature. As results of this analysis, it is shown that VoIP jitter can be modeled by alpha-stable distributions and self-similar processes, with either Short or Long Range Dependence (i.e., SRD or LRD). Thirdly, we investigate the packet loss effects on the VoIP jitter, and present a methodology for simulating packet loss on VoIP jitter. Finally, we found an empirical relationship between the Hurst parameter (H) and the Packet Loss Rate (PLR); this relationship is based on voice traffic measurements and can be modeled by means of a power-law function with three fitted parameters.

Restoration Technique to Optimize Recovery Time for Efficient OSPF Network

2016 ◽  
pp. 64-88
Author(s):  
Pertik Garg ◽  
Ashu Gupta
Keyword(s):  
Packet Loss ◽  
Recovery Time ◽  
High Speed ◽  
Loss Rate ◽  
Ip Networks ◽  
Packet Loss Rate ◽  
Convergence Time ◽  
Topology Change ◽  
Routing Table ◽  
Restoration Technique

Some high speed IP networks, which involve interior gateway protocols, such as OSPF, are not capable of finding the new routes to bypass the effect like failure in time. At the point when the failure occurs the network must converge it before the traffic has the capacity to go to and from the network segment that caused a connection disconnect. The duration of the convergence period of these protocols vary from hundred of milliseconds to 10 seconds, which creates unsteadiness and results high packet loss rate. This issue may be determined by proposing an algorithm that can rapidly react to the topology change and reduce the convergence time by providing back up path which is already stored in routing table before the failover occurs.

Level phase independence for GI/M/1-type Markov chains

2000 ◽  
Vol 37 (4) ◽  
pp. 984-998 ◽  
Author(s):  
Guy Latouche ◽  
P. G. Taylor
Keyword(s):  
Phase Transitions ◽  
Markov Chains ◽  
State Space ◽  
Transition Probabilities ◽  
The Other ◽  
One Dimension ◽  
Death Process ◽  
Two Dimensional ◽  
Birth And Death Process ◽  
Special Case

GI/M/1-type Markov chains make up a class of two-dimensional Markov chains. One dimension is usually called the level, and the other is often called the phase. Transitions from states in level k are restricted to states in levels less than or equal to k+1. For given transition probabilities in the interior of the state space, we show that it is always possible to define the boundary transition probabilities in such a way that the level and phase are independent under the stationary distribution. We motivate our analysis by first considering the quasi-birth-and-death process special case in which transitions from any state are restricted to states in the same, or adjacent, levels.

Level phase independence for GI/M/1-type Markov chains

2000 ◽  
Vol 37 (04) ◽  
pp. 984-998
Author(s):  
Guy Latouche ◽  
P. G. Taylor
Keyword(s):  
Phase Transitions ◽  
Markov Chains ◽  
State Space ◽  
Transition Probabilities ◽  
The Other ◽  
One Dimension ◽  
Death Process ◽  
Two Dimensional ◽  
Birth And Death Process ◽  
Special Case

GI/M/1-type Markov chains make up a class of two-dimensional Markov chains. One dimension is usually called the level, and the other is often called the phase. Transitions from states in level k are restricted to states in levels less than or equal to k+1. For given transition probabilities in the interior of the state space, we show that it is always possible to define the boundary transition probabilities in such a way that the level and phase are independent under the stationary distribution. We motivate our analysis by first considering the quasi-birth-and-death process special case in which transitions from any state are restricted to states in the same, or adjacent, levels.

scholarly journals Robust Static Output-Feedback Control for MJLS with Non-Homogeneous Markov Chains: A Comparative Study Considering a Wireless Sensor Network with Time-Varying PER

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6420
Author(s):  
Arthur R. C. Serafini ◽  
Leonardo Delforno ◽  
Jonathan M. Palma ◽  
Frank H. Behrens ◽  
Cecília F. Morais
Keyword(s):  
Markov Chains ◽  
Packet Loss ◽  
Communication Channel ◽  
Transition Probabilities ◽  
Wireless Sensor ◽  
Linear Matrix ◽  
Time Varying ◽  
Markov Jump ◽  
Markov Jump Linear Systems

This paper deals with the problem of control through a semi-reliable communication channel, such as wireless sensor networks (WSN). Particularly, the case investigated is the one where the packet loss rate of the network is time-varying due to, for instance, variation in the distance between the nodes. Considering this practical motivation, the control system is modeled using a formulation based on discrete-time Markov jump linear systems (MJLS) with non-homogeneous Markov chains (time-varying transition probabilities). New control design conditions based on parameter-dependent linear matrix inequalities are proposed in order to solve this problem. The purpose is to demonstrate that this strategy is suitable to handle the networked control problem by comparing the temporal behavior of the closed-loop system with the Markovian controller and a standard proportional-integral-derivative (PID) controller. The case study presented in the paper considers the problem of the remote control of a Vertical Take-Off and Landing (VTOL) vehicle through a wireless communication channel. The network packet loss model employed in the case study is based on data collected on a wireless network workbench, which was previously developed and validated by the authors.

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