Fine-granularity inference and estimations to network traffic for SDN.
Dingde Jiang1,2, Liuwei Huo2, Ya Li2
1School of Astronautics and Aeronautic, University of Electronic Science and Technology of China, Chengdu, China.
Plos One
|May 3, 2018
Summary
This study introduces a novel method to accurately estimate network traffic matrices in fine time granularity using fractal and cubic spline interpolation. The approach effectively reconstructs traffic data for better network management in Software Defined Networks (SDN).
Area of Science:
- Computer Science
- Network Engineering
Background:
- End-to-end network traffic matrix estimation is crucial for network management and Software Defined Networks (SDN).
- Accurate traffic matrix inference, especially at fine time granularities in high-speed networks, presents a significant challenge.
Purpose of the Study:
- To investigate methods for estimating and recovering the end-to-end network traffic matrix with fine time granularity from sampled traffic traces.
- To address the challenges of inferring traffic matrices in high-speed Software Defined Networks.
Main Methods:
- Utilized fractal interpolation to reconstruct finer-granularity network traffic data.
- Employed cubic spline interpolation to obtain smooth reconstruction values.
- Implemented a weighted-geometric-average process combining two interpolation results for enhanced accuracy.
Main Results:
- The proposed approaches demonstrated feasibility and effectiveness in simulations.
- Achieved accurate estimation of the end-to-end network traffic matrix in fine time granularity.
Conclusions:
- The developed methods offer a viable solution for recovering high-resolution network traffic matrices.
- This work contributes to improved network management and performance in SDN environments.
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