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Optical network failure management (ONFM) is a promising application of machine learning (ML) to optical networking. Typical ML-based ONFM approaches exploit historical monitored data, retrieved in a specific domain (e.g., a link or a network), to train supervised ML models and learn failure characteristics (a signature) that will be helpful upon future failure occurrence in that domain. Unfortunately, in operational networks, data availability often constitutes a practical limitation to the deployment of ML-based ONFM solutions, due to scarce availability of labeled data comprehensively modeling all possible failure types. One could purposely inject failures to collect training data, but this is time consuming and not desirable by operators. A possible solution is transfer learning (TL), i.e., training ML models on a source domain (SD), e.g., a laboratory testbed, and then deploying trained models on a target domain (TD), e.g., an operator network, possibly fine-tuning the learned models by re-training with few TD data. Moreover, in those cases when TL re-training is not successful (e.g., due to the intrinsic difference in SD and TD), another solution is domain adaptation, which consists of combining unlabeled SD and TD data before model training. We investigate domain adaptation and TL for failure detection and failure-cause identification across different lightpaths leveraging real optical SNR data. We find that for the considered scenarios, up to 20% points of accuracy increase can be obtained with domain adaptation for failure detection, while for failure-cause identification, only combining domain adaptation with model re-training provides significant benefit, reaching 4%–5% points of accuracy increase in the considered cases.
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On the Practicality of Learning Models for Network Telemetry
Today’s data plane network telemetry systems en- able network operators to capture fine-grained data streams of many different network traffic features (e.g., loss or flow arrival rate) at line rate. This capability facilitates data-driven approaches to network management and motivates leveraging either statistical or machine learning models (e.g., for forecasting network data streams) for automating various network management tasks. However, current studies on network automation- related problems are in general not concerned with issues that arise when deploying these models in practice (e.g., (re)training overhead). In this paper, we examine various training-related aspects that affect the accuracy and overhead (and thus feasibility) of both LSTM and SARIMA, two popular types of models used for forecasting real-world network data streams in telemetry systems. In particular, we study the impact of the size, choice, and recency of the training data on accuracy and overhead and explore using separate models for different segments of a data stream (e.g., per-hour models). Using two real-world data streams, we show that (i) per-hour LSTM models exhibit high accuracy after training with only 24 hours of data, (ii) the accuracy of LSTM models does not depend on the recency of the training data (i.e., no frequent (re)training is required), (iii) SARIMA models can have comparable or lower accuracy than LSTM models, and (iv) certain segments of the data streams are inherently more challenging to forecast than others. While the specific findings reported in this paper depend on the considered data streams and specified models, we argue that irrespective of the data streams at hand, a similar examination of training-related aspects is needed before deploying any statistical or machine learning model in practice.
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- Award ID(s):
- 1850297
- PAR ID:
- 10166357
- Date Published:
- Journal Name:
- Proceedings of Network Traffic Measurement and Analysis Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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