Segment-based adaptive hyper-Erlang model for long-tailed network traffic approximation

Authors:
Junfeng Wang;Jin Liu;Chundong She
Affiliations:
College of Computer Science, Sichuan University, Chengdu, China 610064;State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China 430072;Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing, China 100080
Venue:
The Journal of Supercomputing
Year:
2008

Citing 13
Cited 2

Algorithms for clustering data

Algorithms for clustering data
Fitting mixtures of exponentials to long-tail distributions to analyze network performance models

Performance Evaluation
Evidence for long-tailed distributions in the internet

IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
Stochastic Complexity in Statistical Inquiry Theory

Stochastic Complexity in Statistical Inquiry Theory
Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems

Journal of Automated Reasoning
Modeling and analysis of power-tail distributions via classical teletraffic methods

Queueing Systems: Theory and Applications
Fitting world-wide web request traces with the EM-algorithm

Performance Evaluation - Special issue: Internet performance and control of network systems
An EM-based technique for approximating long-tailed data sets with PH distributions

Performance Evaluation - Internet performance symposium (IPS 2002)
Clock synchronization for internet measurements: a clustering algorithm

Computer Networks: The International Journal of Computer and Telecommunications Networking
Variable heavy tails in internet traffic

Performance Evaluation - Special issue: Distributed systems performance
Hierarchical Dynamics, Interarrival Times, and Performance

Proceedings of the 2003 ACM/IEEE conference on Supercomputing
A general model for long-tailed network traffic approximation

The Journal of Supercomputing
Reversibility and Stochastic Networks

Reversibility and Stochastic Networks

Cluster-based fitting of phase-type distributions to empirical data

Computers & Mathematics with Applications
Cloud service: automatic construction and evolution of software process problem-solving resource space

The Journal of Supercomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Modeling the long-tailedness property of network traffic with phase-type distributions is a powerful means to facilitate the consequent performance evaluation and queuing based analysis. This paper improves the recently proposed Fixed Hyper-Erlang model (FHE) by introducing an adaptive framework (Adaptive Hyper-Erlang model, AHE) to determine the crucially performance-sensitive model parameters. The adaptive model fits long-tailed traffic data set directly with a mixed Erlang distribution in a new divide-and-conquer manner. Compared with the well-known hyperexponential based models and the Fixed Hyper-Erlang model, the Adaptive Hyper-Erlang model is more flexible and practicable in addition to its accuracy in fitting the tail behavior.