scholarly journals Traffic Engineering for VBR Video with Long-Range Dependence

1996 ◽  
pp. 467-476 ◽  
Author(s):  
K. R. Krishnan ◽  
Gopalakrishnan Meempat
Keyword(s):  
Long Range ◽  
Traffic Engineering ◽  
Long Range Dependence ◽  
Vbr Video

Long-range dependence in VBR video streams and ATM traffic engineering

1997 ◽  
Vol 30 (1-2) ◽  
pp. 45-56 ◽  
Author(s):  
K.R. Krishnan ◽  
G. Meempat
Keyword(s):  
Long Range ◽  
Traffic Engineering ◽  
Long Range Dependence ◽  
Video Streams ◽  
Vbr Video

MODELING NETWORK TRAFFIC USING CAUCHY CORRELATION MODEL WITH LONG-RANGE DEPENDENCE

2005 ◽  
Vol 19 (17) ◽  
pp. 829-840 ◽  
Author(s):  
MING LI ◽  
S. C. LIM
Keyword(s):  
Long Range ◽  
Network Traffic ◽  
Traffic Engineering ◽  
Single Parameter ◽  
Traffic Modeling ◽  
Long Range Dependence ◽  
Correlation Model ◽  
Traffic Time Series ◽  
Fractal Properties

Much attention has been given to the long-range dependence and fractal properties in network traffic engineering, and these properties are also widely observed in many fields of science and technologies. Traffic time series is conventionally characterized by its fractal dimension D, which is a measure for roughness, and by the Hurst parameter H, which is a measure for long-range dependence, see for examples (Refs. 10–12). Each property has been traditionally modeled and explained by self-affine random functions, such as fractional Gaussian noise (FGN)1,10–13,18,22–28 and fractional Brownian motion (FBM),6,7 where a linear relationship between D and H, say D = 2 - H for one-dimensional series, links the two properties. The limitation of single parameter models (e.g., FGN) in long-range dependent (LRD) traffic modeling has been noticed as can be seen from Refs. 1, 18 and 25. Hence, models which can provide good fitting of LRD traffic for both short-term lags and long-term ones are worth studying due to the importance of accurate models of traffic in network communications.13 This letter utilizes a statistical model called the Cauchy correlation model to model LRD traffic. This model characterizes D and H separately, and it allows any combination of two within the constraint of LRD condition. It is a new power-law correlation model for LRD traffic modeling with its local and global behavior decoupling. Its flexibility in data modeling in comparison with a single parameter model of FGN is briefly discussed, and applications to LRD traffic modeling demonstrated.

scholarly journals Representations of hermite processes using local time of intersecting stationary stable regenerative sets

2020 ◽  
Vol 57 (4) ◽  
pp. 1234-1251
Author(s):  
Shuyang Bai
Keyword(s):  
Long Range ◽  
Local Time ◽  
Limit Theorems ◽  
Local Times ◽  
Long Range Dependence ◽  
Random Interval ◽  
Stationary Increments ◽  
Self Similar ◽  
Regenerative Sets

AbstractHermite processes are a class of self-similar processes with stationary increments. They often arise in limit theorems under long-range dependence. We derive new representations of Hermite processes with multiple Wiener–Itô integrals, whose integrands involve the local time of intersecting stationary stable regenerative sets. The proof relies on an approximation of regenerative sets and local times based on a scheme of random interval covering.

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