From Poisson Processes to Self-Similarity: a Survey of Network Traffic Models
Michela Becchi, mbecchi@wustl.edu
Abstract
The paper provides a survey of network traffic models. It starts from the description of the Poisson model, born in the context of telephony, and highlights the main reasons for its inadequacy to describe data traffic in LANs and WANs. It then details two models which have been conceived to overcome the Poisson model's limitations. In particular, the discussion focuses on the packet train model, validated in a Token Ring LAN, and on the self-similar model, used to capture traffic burstiness at several times scales in both Ethernet LANs and WANs. The discussion closes with some examples of usage of those models in LAN and WAN environments.
Table of Contents
- 1. Introduction
- 2. Traffic modeling: basic concepts
- 3. The Poisson Model
- 3.1 Description of the model
- 3.2 Traffic burstiness: the limitations of the Poisson model
- 4. The Packet Train Model
- 5. The Self-Similar Model
- 5.1 Spatial and time variability: from Poisson to Fractals
- 5.2 An analytical view of self similarity
- 6. Other Traffic Models
- 6.1 Renewal Traffic Models
- 6.2 Markov Traffic Models
- 6.3 Autoregressive Traffic Models
- 6.4 Transform-Expand-Sample
- 7. Application of the Models
- 7.1 Modeling LAN traffic
- 7.2 Modeling WAN traffic
- 8. Conclusions
- References
- List of Acronyms
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