scholarly journals Modeling Internet Traffic Generations Based on Individual Users and Activities for Telecommunication Applications

2016 ◽  
Vol 13 (3) ◽  
Author(s):  
Sara Stoudt ◽  
Pamela Badian-Pessot ◽  
Blanche Ngo Mahop ◽  
Erika Earley ◽  
Jordan Menter ◽  
...  
Keyword(s):  
Cumulative Distribution Function ◽  
Data Transfer ◽  
Internet Traffic ◽  
Cumulative Distribution ◽  
Generation Model ◽  
Traffic Data ◽  
Empirical Copula ◽  
Keywords Internet ◽  
Traffic Generation ◽  
Network Topologies

A traffic generation model is a stochastic model of the data flow in a communication network. These models are useful during the development of telecommunication technologies and for analyzing the performance and capacity of various protocols,algorithms, and network topologies. We present here two modeling approaches for simulating internet traffic. In our models, we simulate the length and interarrival times of individual packets, the discrete unit of data transfer over the internet. Our first modeling approach is based on fitting data to known theoretical distributions. The second method utilizes empirical copulae and is completely data driven. Our models were based on internet traffic data generated by different individuals performing specific tasks (e.g., web browsing, video streaming, and online gaming). When combined, these models can be used to simulate internet traffic from multiple individuals performing typical tasks. KEYWORDS: Internet Traffic Simulation; Stochastic Models; Empirical Copula; Cumulative distribution function; Wireshark

DISSONANCE — A MEASURE OF VARIABILITY FOR ORDINAL RANDOM VARIABLES

2001 ◽  
Vol 09 (01) ◽  
pp. 39-53 ◽  
Author(s):  
RONALD R. YAGER
Keyword(s):  
Distribution Function ◽  
Cumulative Distribution Function ◽  
Probability Distributions ◽  
Random Variables ◽  
Cumulative Distribution ◽  
General Properties

We look at the issue of obtaining a variance like measure associated with probability distributions over ordinal sets. We call these dissonance measures. We specify some general properties desired in these dissonance measures. The centrality of the cumulative distribution function in formulating the concept of dissonance is pointed out. We introduce some specific examples of measures of dissonance.

Estimating the cumulative distribution function for the linear combination of gamma random variables

2017 ◽  
Vol 20 (5) ◽  
pp. 939-951
Author(s):  
Amal Almarwani ◽  
Bashair Aljohani ◽  
Rasha Almutairi ◽  
Nada Albalawi ◽  
Alya O. Al Mutairi
Keyword(s):  
Distribution Function ◽  
Linear Combination ◽  
Cumulative Distribution Function ◽  
Random Variables ◽  
Cumulative Distribution

scholarly journals Strong uniform consistency rates of conditional quantile estimation in the single functional index model under random censorship

2018 ◽  
Vol 6 (1) ◽  
pp. 197-227 ◽  
Author(s):  
Nadia Kadiri ◽  
Abbes Rabhi ◽  
Amina Angelika Bouchentouf
Keyword(s):  
Cumulative Distribution Function ◽  
Complete Convergence ◽  
Asymptotic Properties ◽  
Cumulative Distribution ◽  
Index Structure ◽  
Functional Index ◽  
Conditional Quantile ◽  
Index Model ◽  
Strong Uniform Consistency ◽  
Mixing Sequence

AbstractThe main objective of this paper is to non-parametrically estimate the quantiles of a conditional distribution in the censorship model when the sample is considered as an -mixing sequence. First of all, a kernel type estimator for the conditional cumulative distribution function (cond-cdf) is introduced. Afterwards, we estimate the quantiles by inverting this estimated cond-cdf and state the asymptotic properties when the observations are linked with a single-index structure. The pointwise almost complete convergence and the uniform almost complete convergence (with rate) of the kernel estimate of this model are established. This approach can be applied in time series analysis.

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