Impact of self-similarity on wireless data network performance

Jiang, Ming; Nikolić, Mladen; Hardy, Stephen; Trajković, Ljiljana

doi:10.1109/icc.2001.936986

Cited by 57 publications

(39 citation statements)

References 12 publications

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“…Considering that future cellular networks will be able to carry IP traffic (particularly in indoor environments such as wireless LANs), it seems reasonable to have the same traffic characteristics for wired and wireless IP traffic. For example, Jiang et al [12] showed that cellular digital packet data traffic exhibits long-range dependencies. Finally, the reverse of signal to noise ratio defined as…”

Section: B Mmse Predictor Evaluationmentioning

confidence: 99%

Prediction-based admission control for DiffServ wireless Internet

Ghaderi

Capka

Boutaba

2003

2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484)

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Abstract-Future wireless Internet will consist of different wireless technologies that should operate together in a consistent way to provide seamless quality of service to wireless users. In this paper, a wireless cellular architecture overlaid with DiffServ domains is considered. We propose a flexible hierarchical framework for admission control based on this architecture which aims to keep the handoff dropping probability below a target level while maximizing the network utilization. The novelty of our proposal is that (1) our prediction-based admission control scheme considers not only intra-domain but also interdomain handoffs, while (2) it is based on on-line bandwidth requirement prediction, and (3) benefits from different priorities among different service classes to improve the network utilization by accommodating high-priority handoffs at the expense of dropping low-priority calls. Simulation results show that our scheme outperforms the basic trunk reservation scheme with domain and cell-level reservations.

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Section: B Mmse Predictor Evaluationmentioning

confidence: 99%

Prediction-based admission control for DiffServ wireless Internet

Ghaderi

Capka

Boutaba

2003

2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484)

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“…It is also observed that the aggregated Internet traffic flows have similar patterns in different time scales, referred to as self-similarity [2]. Similarly, the existence of self-similarity in wireless network traffic flows has been observed [3] and has attracted attention in the research community [4]. The self-similarity characteristics result in that traffic engineering techniques, based on the traditional method, underestimate the required resources (such as transmission rate) to achieve quality-of-service (QoS) satisfaction.…”

Section: Introductionmentioning

confidence: 78%

Queue Analysis for Wireless Packet Data Traffic

Teymori

Zhuang

2005

NETWORKING 2005. Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile A

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Abstract. Recent research based on Internet traffic measurements shows that the traffic flows have a fractal nature (i.e., self-similarity property), which causes the underestimation of the network engineering parameters when using the Poisson model. Preliminary field measurements demonstrate that packet data traffic in wireless communications also exhibits self-similarity. In this research, we investigate the queuing behavior of a self-similar traffic flow for wireless packet data applications. The traffic is modelled by an on-off source with heavy-tailed on periods. We derive a new relationship among the asymptotic distribution of the queue length, traffic specifications, wireless channel characteristics, and transmission rate. Computer simulation results are given to demonstrate the accuracy of the theoretical analysis.

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“…With the increasing prevalence of high speed wireless systems capable of fast transmissions of large files (such as multimedia), there is a considerable literature analyzing wireless systems with heavy tails or long range dependence. Evidence of long range dependence and heavy tails in several wireless systems is reported in Kalden and Ibrahim (2004), Lee and Fapojuwo (2005), Jiang et al (2001), and others. In Teymori and Zhuang (2007), an ON-OFF source arrival with heavy-tailed ON periods is considered in studying queueing behavior.…”

Section: Introductionmentioning

confidence: 99%

Heavy Traffic Approximations of a Queue with Varying Service Rates and General Arrivals

2012

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Heavy traffic limit theorems are established for a class of single server queueing models including those with heavy-tailed or long-range dependent arrivals and time-varying service rates. The models are motivated by wireless queueing systems for which there is an increasing evidence of the presence of heavy-tailed or long range dependent arrivals, and where the service rates vary with the changes in the wireless medium. Since changes in the wireless medium occur slowly, the service rate is assumed to vary slower than that of the arrivals.The main focus of the paper is to obtain the different possible limit forms that can arise depending on the relationship between established scalings for both the arrival and departure processes. The limit forms obtained here are driven by either Brownian motion (when the contribution from the departure process dominates the limit) or the limits of properly scaled arrivals (when the contribution from the arrival process dominates the limit), typical examples being stable Lévy motion or fractional Brownian motion. In particular, for the case where arrival process is given by the infinite source Poisson process, this relationship, which determines the type of the limiting queue-length process, is a simple condition involving the heavy tail exponent, arrival rate and channel variation parameter in the wireless medium model. The limit forms are also affected by the assumption of a slower varying service rate.To establish these limit results, two approaches are studied. In one approach, when the limit is driven by Brownian motion, the perturbed test function method is extended to incorporate reflection. In contrast, the second approach allows for non-Markovian driving processes in the limit. Both approaches involve averaging in the drift term arising from random service rates at the departures. In the second approach, this averaging is carried out directly and pathwise, thus sidestepping the assumption of driving Brownian motion used in the perturbed test function method. In the case of the infinite source Poisson model, when the limit is stable Lévy motion, it is also argued how the limit form can be deduced using infinitesimal operators.

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Impact of self-similarity on wireless data network performance

Cited by 57 publications

References 12 publications

Prediction-based admission control for DiffServ wireless Internet

Prediction-based admission control for DiffServ wireless Internet

Queue Analysis for Wireless Packet Data Traffic

Heavy Traffic Approximations of a Queue with Varying Service Rates and General Arrivals

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