Towards the formation of comprehensive SLAs between heterogeneous wireless DiffServ domains

Abstract

Traffic patterns generated by multimedia services are different from traditional Poisson traffic. It has been shown in numerous studies that multimedia network traffic exhibits self-similarity and burstiness over a large range of time-scales. The area of wireless IP traffic modeling for the purpose of providing assured QoS to the end-user is still immature and the majority of existing work is based on characterization of wireless IP traffic without any coupling of the behaviour of queueing systems under such traffic conditions. Work in this area has either been limited to simplified models of FIFO queueing systems which do not accurately reflect likely queueing system implementations or the results have been limited to simplified numerical analysis studies. In this paper, we advance the knowledge of queueing systems by example of traffic engineering of different UMTS service classes. Specifically, we examine QoS mapping using three common queueing disciplines; Priority Queuing (PQ), Low Latency Queuing (LLQ) and Custom Queueing (CQ), which are likely to be used in future all-IP based packet transport networks. The present study is based on a long-range dependent traffic model, which is second order self-similar. We consider three different classes of M. Iftikhar ( ) self-similar traffic fed into a G/M/1 queueing system and construct analytical models on the basis of non-preemptive priority, low-latency queueing and custom queueing respectively. In each case, expressions are derived for the expected waiting times and packet loss rates of different traffic classes. We have developed a comprehensive discrete-event simulator for a G/M/1 queueing system in order to understand and evaluate the QoS behaviour of self-similar traffic and carried out performance evaluations of multiple classes of input traffic in terms of expected queue length, packet delay and packet loss rate. Furthermore, we have developed a traffic generator based on the self-similar traffic model and fed the generated traffic through a CISCO router-based test bed. The results obtained from the three different queueing schemes (PQ, CQ and LLQ) are then compared with the simulation results in order to validate our analytical models.

Performance Simulation and Analysis for LTE System Using Human Behavior Queue Model

“…In addition, we refer the readers to [12,13] regarding the work that has been carried out in terms of IP networkperformance evaluation. The analysis conducted in [12,13] has two main disadvantages; first the reported queuingmodels did not employ human-in-the-loop phenomenon for network traffic input and second, they have only usedsingle class of traffic for conducting analysis by neglecting the performance affect of other subsequent traffic classes.To overcome the limitations of related work, they presented a novel analytical framework [14,15] based on realtime human behavior queuing system, that contemplates task management of human behavior traffic. In the relatedwork, they analyzed the traditional scheduling schemes such as priority and round robin.…”

Scheduling and Queue Management for Multi-class Traffic in Access Router of Mobility Protocol

“…Iftikhar et al [4] present a novel analytical model for multiple queue systems by considering two different classes of traffic that exhibit long-range dependence and selfsimilarity. Iftikhar et al [5] also use a G/M/1 queuing system and construct analytical models on the basis of non-preemptive priority queuing, low-latency queuing and custom queuing. However, these papers lack the quantitative analysis of average queuing delay, queue occupancy and packet drop probability of data and signaling packets at the access router while managing IP-mobility.…”

Quality-of-Service Analysis of Queuing Systems with Long-Range-Dependent Network Traffic and Variable Service Capacity

“…Yu et al [22] presented an analytical upper bound for the queue length distribution of individual LRD traffic flows in the generalized processor sharing systems. In [23], Iftikhar et al developed the analytical performance models for multiple queue systems subject to LRD traffic, which is scheduled by priority queuing, low latency queuing, and custom queuing, respectively. They derived the expressions of packet delay and loss rate of the multiple traffic classes.…”