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Modeling Autocorrelation Functions of SelfSimilar Teletraffic
 in Communication Networks Based on Optimal Approximation in Hilbert Space, Applied Mathematical Modelling
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Simulation Of SelfSimilar Traffic And A TCP Traffic Simulator
 Proc., IEEE ICA3PP’2000
"... This paper presents a simulation method of selfsimilar traffic and a type of TCP traffic ..."
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This paper presents a simulation method of selfsimilar traffic and a type of TCP traffic
A Method for Modeling Autocorrelation Functions of Asymptotically LRD Traffic and Its Verification
 Its Verification,” Conf. Proc., ICCT2000, 16 th IFIP World Computer Congress, IEEE Press, 2125, Aug., 2000
, 2000
"... Introduction Recent researches have shown that the behaviors of the traffic on LAN and WAN are well modeled by secondorder selfsimilar processes with longrange dependence (LRD) [12]. Secondorder selfsimilar processes are classified into two classes [12]. One is exactly secondorder selfsim ..."
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Introduction Recent researches have shown that the behaviors of the traffic on LAN and WAN are well modeled by secondorder selfsimilar processes with longrange dependence (LRD) [12]. Secondorder selfsimilar processes are classified into two classes [12]. One is exactly secondorder selfsimilar model and the other asymptotically secondorder selfsimilar model. [1] pointed out that exactly secondorder selfsimilar model is not enough to model real traffic. Hence, asymptotically secondorder selfsimilar processes are considered in the paper. Throughout the paper, the term LRD processes means secondorder selfsimilar processes unless otherwise stated. LRD processes are defined by autocorrelation functions (ACFs). As ACFs can be used to study queuing systems [3], it is significant to study how to find a function that best fits the autocorrelation sequence of a realtraffic trace (target ACF). Because the ACFs of LRD processes are characterized by a singl
Bounded tag fair queueing for broadband packet switching networks
"... Fair Queueing (FQ) is an attractive packet scheduling mechanism, which can establish firewall among packet flows. Thus, the quality of service (QoS) of each flow can be guaranteed. To implement the mechanism, three important performance issues are concerned: fairness, bounded delay, and efficiency. ..."
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Fair Queueing (FQ) is an attractive packet scheduling mechanism, which can establish firewall among packet flows. Thus, the quality of service (QoS) of each flow can be guaranteed. To implement the mechanism, three important performance issues are concerned: fairness, bounded delay, and efficiency. We propose a scheme, named BTFQ, which satisfies the three criteria. The scheme BTFQ 1 is also proposed to improve the fairness property of BTFQ and is designed to combine with a lowcost, highspeed hardware for reducing computation time. For comparison, we classify some wellknown packet scheduling schemes into six classes according to their fairness properties. BTFQ and BTFQ 1 are excellent in bounded delay and efficiency. Their fairness properties, although a little bit weak, are but still very good if the traffic load is under 95%, according to the simulation results. Because of the tradeoff among the performance issues, BTFQ and BTFQ 1
1 Estimation of TimeVarying Autoregressive Symmetric Alpha Stable Processes by Particle Filters
"... Abstract—In this work, we propose a novel method to model timevarying autoregressive impulsive signals, which possess Symmetric Alpha Stable distributions. The proposed method is composed of a particle filter, which is capable of estimating the unknown, timevarying autoregressive coefficients and a ..."
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Abstract—In this work, we propose a novel method to model timevarying autoregressive impulsive signals, which possess Symmetric Alpha Stable distributions. The proposed method is composed of a particle filter, which is capable of estimating the unknown, timevarying autoregressive coefficients and a Hybrid Monte Carlo method that is used for estimating the unknown statistical parameters of the Symmetric Alpha Stable Process. The performance of the proposed method is tested for different parameter values where the time variation of the autoregressive coefficients is taken to be as sinusoidal or random jumps. The successful performance of the developed method serves as a promising contribution in the modeling of impulsive signals, which are frequently seen in many areas, such as teletraffic in computer communications, radar and sonar applications and mobile communications.