Abstract
Tools for estimating end-to-end available bandwidth (AB) send out a train of packets and observe how inter-packet gaps change over a given network path. In ultra-high speed networks, the fine inter-packet gaps are fairly susceptible to noise introduced by transient queuing and bursty cross-traffic. Past work uses smoothing heuristics to alleviate the impact of noise, but at the cost of requiring large packet trains. In this paper, we consider a machine-learning approach for learning the AB from noisy inter-packet gaps. We conduct extensive experimental evaluations on a 10 Gbps testbed, and find that supervised learning can help realize ultra-high speed bandwidth estimation with more accuracy and smaller packet trains than the state of the art. Further, we find that when training is based on: (i) more bursty cross-traffic, (ii) extreme configurations of interrupt coalescence, a machine learning framework is fairly robust to the cross-traffic, NIC platform, and configuration of NIC parameters.
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Notes
- 1.
We focus on 10 Gbps speed in this paper, and use jumbo frames of MTU=9000B.
- 2.
- 3.
Probing range is given by: \(\frac{r_{N}}{r_{1}}-1\).
- 4.
Our evaluations revealed that models trained with ElasticNet and SVM result in considerable inaccuracy. For brevity, we don’t present their results.
- 5.
In our Python implementation with scikit-learn [22] library, we use its automatic parameter tuning feature for all ML methods, and use 5-fold cross-validation to validate our results.
- 6.
Note that replayed traffic retains the burstiness of original traffic aggregate, but does not retain responsiveness of individual TCP flows. However, the focus of this paper is to evaluate denoising techniques for accurate AB estimation —this metric is not impacted by the responsiveness of cross traffic, but only by its burstiness.
- 7.
Each weak model in RandomForest is learned on a different subset of training data. The final prediction is the average result of all models. AdaBoost and GradientBoost follow a boosting approach, where each model is built to emphasize the training instances that previous models do not handle well. The boosting methods are known to be more robust than RandomForest [25], when the data has few outliers.
- 8.
Since models are trained off-line, the training overhead is not of concern.
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Yin, Q., Kaur, J. (2016). Can Machine Learning Benefit Bandwidth Estimation at Ultra-high Speeds?. In: Karagiannis, T., Dimitropoulos, X. (eds) Passive and Active Measurement. PAM 2016. Lecture Notes in Computer Science(), vol 9631. Springer, Cham. https://doi.org/10.1007/978-3-319-30505-9_30
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