IP Networks Quality of Service: Overview and Open Issues Marlenne Angulo1,2, Deni Torres-Roman1, David Muñoz-Rodriguez3, and Marco Turrubiartes1,2 1 CINVESTAV Research Center, Guadalajara México {mangulo,dtorres,mturrubi}@gdl.cinvestav.mx 2 Universidad Autónoma de Baja California, Mexicali México 3 ITESM, Monterrey México
[email protected]
Abstract. Mechanisms to provide Quality of Service (QoS) into Internet have a collection of aspects to consider improving network performance. However, this work focuses only on four of these aspects, as follows: Traffic Models, Queue scheduling, Congestion control and QoS routing. Considering any of the three above mentioned approaches, it is necessary the use of traffic models which capture the real network traffic behavior. This paper introduces some QoS concepts, as well as open-issues in the mentioned areas.
1 Introduction Most of Internet user applications require superior network performance; some applications even require guaranteed service. This makes Quality of Service (QoS) an area that fulfills users’ requirements. The QoS areas research, develop and implement mechanisms to meet users’ needs. However, QoS is not a new concept in some telecommunications fields such as telephony. The QoS development involves mechanisms of network performance evaluation. This allows the provider (e.g. the Internet Service Provider) to verify the QoS degree at the end-user level. There is a group of suggested mechanisms to provide QoS into Internet, and a collection of aspects to consider. However, this work focuses only on four of these aspects, as follows: • Traffic Models (current models). Besides well implemented algorithms, it is necessary the use of real traffic models in order to obtain valid simulation results. • Queue scheduling. Queue and traffic behavior affect the packet delay variation. It leads to network performance variability in terms of delay, affecting real time applications. • Congestion control. It presents an introduction to TCP-friendly congestion control protocol. The use of this class of transport protocol should be increased in order to avoid the unfair bandwidth competition of UDP versus TCP data. • QoS routing. This subject is important in order to achieve a real guaranteed Quality of Service, instead of attempting a differentiation in service or .
T. Böhme et al. (Eds.): IICS 2004, LNCS 3473, pp. 28–37, 2006. © Springer-Verlag Berlin Heidelberg 2006
IP Networks Quality of Service: Overview and Open Issues
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This paper introduces some QoS concepts, as well as open-issues in the sub-areas: Traffic models, Queue scheduling, Congestion control and QoS routing. The remaining of this article is organized as follows: section two introduces concepts and current models in the above mentioned sub-areas. Section three discusses open issues, followed by section four in which the conclusions of this paper are presented. Finally, references are presented.
2 Literature Review 2.1 Traffic Models Historically, distributions such as Poisson and Exponential have been used to model telecommunication traffic. However, those models do not suit with some kinds of traffic, specifically data networks. In articles [1][2], the failure of Poisson model to capture variability in time scales of Ethernet traffic is presented. Using traffic models close to reality discards the possibility of overestimating performance in analysis or simulation results due to ideal traffic conditions. Self-similar Processes Mathematical analysis shows that the Internet traffic has self-similar properties at the packet level [1]. Moreover, an article published by Feldman shows empirical evidence of fractal characteristics on traffic at the application level [3]. The self-similar term was defined by Mandelbrot to characterize those processes that are scaled in time and do not lose their statistical properties. Definition: A process X(t) is self-similar if it satisfies the following equation:
{
X (at ), t ∈ ℜ
}
d H ={a X (t ), t ∈ ℜ} ∀a, H > 0 ∈ ℜ
(1)
d
Where X(at) and a H X (t ) have identical distributions( = ), H is the Hurst parameter denoting the self-similarity index [4]. For Internet traffic H takes values into the interval (0.5, 1). Long Range Dependence Phenomenon (LRD) The self similar property of Internet traffic manifests itself in the autocorrelation function ρ (k ) . The sum of all autocorrelations from any given time instant is always significant, even if individual correlations are small. The Internet traffic holds an LRD phenomenon. It means that Internet traffic characteristics, at time t, will influence itself in the long term [5]. LRD is defined as the property of some processes in which the sum of the autocorrelation values approaches infinity (see Eq. 2). ∞
∑ ρ (k ) = ∞
(2)
k =0
Where its autocorrelation function is
ρ (k ) ~ L1 ( k ) k − β
k →0
0 < β
