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Self-similar Traffic Analysis at Network Layer Level. Part I: Fundamentals

EasyChair Preprint no. 5235

12 pagesDate: March 30, 2021


Traffic streams, sources as well as aggregated traffic flows, often exhibit long-range-dependent (LRD) properties. This
paper presents the theoretical foundations to justify that the behavior of traffic in a high-speed computer network can be
modeled from a self-similar perspective by limiting its scope of analysis at the network layer, given that the most relevant
properties of self-similar processes are consistent for use in the formulation of traffic models when performing this
specific task.

Keyphrases: Long Range Dependent, Network Layer, self-similarity, traffic flows, traffic models

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Ginno Millán},
  title = {Self-similar Traffic Analysis at Network Layer Level. Part I: Fundamentals},
  howpublished = {EasyChair Preprint no. 5235},

  year = {EasyChair, 2021}}
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