Leveraging Deep Learning to Achieve Knowledge-based Autonomous Service Provisioning in Broker-based Multi-Domain SD-EONs with Proactive and Intelligent Predictions of Multi-Domain Traffic

Leveraging Deep Learning to Achieve Knowledge-based Autonomous Service Provisioning in Broker-based Multi-Domain SD-EONs with Proactive and Intelligent Predictions of Multi-Domain Traffic

This paper demonstrates a knowledge-based autonomous service provisioning framework for multi-domain SD-EONs supported by a broker plane equipped with a deep-learning based traffic estimator. Simulation results show that the proposed framework achieves ∼ 91% traffic prediction accuracy and ∼ 9× bloc...

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Bibliographic Details
Published in:2017 European Conference on Optical Communication (ECOC) pp. 1 - 3
Main Authors: Chen, X., Guo, J., Zhu, Z., Castro, A., Proietti, R., Lu, H., Shamsabardeh, M., Yoo, S. J. B.
Format: Conference Proceeding
Language:English
Published: IEEE 01-09-2017
Subjects:
Computer architecture
Heuristic algorithms
Knowledge based systems
Machine learning
Modulation
Resource management
Topology
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Summary:This paper demonstrates a knowledge-based autonomous service provisioning framework for multi-domain SD-EONs supported by a broker plane equipped with a deep-learning based traffic estimator. Simulation results show that the proposed framework achieves ∼ 91% traffic prediction accuracy and ∼ 9× blocking reduction compared to conventional solutions.
DOI:10.1109/ECOC.2017.8346218