Learning from Network Device Statistics

@article{Stadler2017LearningFN,
  title={Learning from Network Device Statistics},
  author={Rolf Stadler and Rafael Pasquini and Viktoria Fodor},
  journal={Journal of Network and Systems Management},
  year={2017},
  volume={25},
  pages={672-698}
}
We estimate end-to-end service metrics from network device statistics. Our approach is based upon statistical, supervised learning, whereby the mapping from device-level to service-level metrics is learned from observations, i.e., through monitoring the system. The approach enables end-to-end performance prediction without requiring an explicit model of the system, which is different from traditional engineering techniques that use stochastic modeling and simulation. The fact that end-to-end… CONTINUE READING

Citations

Publications citing this paper.

References

Publications referenced by this paper.
Showing 1-10 of 30 references

JNSM 2017 Traces: infrastructure statistics and service-level metrics

R. Pasquini, R. Stadler
https://github.com/rafaelpasquini/traces-jnsm-2017 • 2017
View 1 Excerpt

Learning end-to-end application QoS from openflow switch statistics

2017 IEEE Conference on Network Softwarization (NetSoft) • 2017
View 2 Excerpts

Machine Learning in Software Defined Networks: Data collection and traffic classification

2016 IEEE 24th International Conference on Network Protocols (ICNP) • 2016
View 1 Excerpt

Predicting network attack patterns in SDN using machine learning approach

2016 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) • 2016
View 1 Excerpt

Dreams: dynamic RE source allocation for Mapreduce with data skew

Z. Liu, Q. Zhang, +3 authors Z. Gong
IFIP/IEEE International Symposium on Integrated Network Management (IM). pp. 18–26. • 2015
View 1 Excerpt

Similar Papers

Loading similar papers…