A method of network monitoring with reduced measured data

Grytsenko, Oleksii; Sayenko, Volodymir

doi:10.1109/infocommst.2017.8246442

Cited by 1 publication

(1 citation statement)

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“…As the network expands, large amounts of data will be generated from the ubiquitous monitoring devices across the network. This may cause additional operational load of the network control plane due to finite flow entries [10], [11]. The aforementioned data is not only restricted to QoT estimation, but also to offline machine/deep learning training and cross-layer network optimization.…”

Section: Introductionmentioning

confidence: 99%

Self-Learning Monitoring On-Demand Strategy for Optical Networks

Meng¹,

Mavromatis²,

Bi³

et al. 2018

J. Opt. Commun. Netw.

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In current dynamic optical networks with cascaded filters and amplifiers, optical signal-to-noise ratio (OSNR) can vary significantly from channel to channel. Under such uncertainty, OSNR prediction for unestablished channels becomes indispensable but remains a big challenge. For protective network planning purposes such as margin threshold setting or wavelength assignment, it is desirable to evaluate the worst OSNR performance of each network link. Such exploration will unavoidably employ active monitoring probes which may cause interruptions to the network. An efficient active monitoring strategy that optimizes the choice of probes or monitoring trials is needed. We propose a "self-learning" monitoring strategy integrated at intermediate nodes. Our method can intelligently select the channel to be monitored in order to search for the target global maxima of OSNR degradation for a specific link. Our monitoring scheme detects intermediate node OSNR in the linear regime. It is shown that our method can predict the target OSNR value with only 0.5dB error while reducing the required monitoring data by up to 91% compared with conventional methods.

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Section: Introductionmentioning

confidence: 99%