Tomogravity space based traffic matrix estimation in data center networks

Liu, Guiyan; Guo, Songtao; Zhao, Qianchuan; Yang, Yuanyuan

doi:10.1016/j.future.2018.03.011

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…Traffic measurement has been used by many different areas of traffic engineering such as network design, anomaly detection, and troubleshooting . Measuring end‐to‐end traffic in high load networks such as backbones and data centers is difficult due to high computational costs . Meanwhile, measuring the neighbor link loads is applicable in the same networks using simple network management protocol (SNMP) in the data‐link layer.…”

Section: Introductionmentioning

confidence: 99%

A new approach for traffic matrix estimation in high load computer networks based on graph embedding and convolutional neural network

Emami

Akbari

Javidan

et al. 2019

Trans Emerging Tel Tech

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In computer networks, transmitted traffic between origin‐destination nodes has been considered a crucial factor in traffic engineering applications. To date, measuring the traffic directly in high load networks is difficult due to high computational costs. On the other hand, accurate estimation of network traffic by means of link load measurements and routing information is currently a challenging problem. In this paper, we propose a new approach to estimate end‐to‐end traffic, inspired by graph embedding. In the proposed approach, we consider a computer network as a time‐varying graph. Our model provides explicit routing information to a convolutional neural network estimator via link load measurements and network topological structure. When explicit routing information is provided, the learning model is only expected to embed the relations between link loads into a traffic estimation vector, instead of figuring out the routing paths. The experimental results showed that the proposed approach outperforms similar estimators in terms of lower estimation error and better tracking the fluctuations.

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Section: Introductionmentioning

confidence: 99%