Machine Learning Assisted Traffic-Aware Approach to Path Assignment in SDM-EONs.
Brazilian Symposium on Computer Networks and Distributed Systems(2022)
摘要
The introduction of new technologies and applications connected to the Internet has demonstrated the inability of current optical networks to provide resources for next-generation Internet. Although the emergence of elastic optical networks with space-division multiplexing has shown to be a promising solution to deal with the capacity problem, some of the technical requirements for the implementation of these networks remain open challenges. In this sense, this paper proposes MISSION, a Machine Learning assisted, fragmentation, and crosstalk-aware model for path allocation in Space Division Multiplexing Elastic Optical Networks (SDM-EONs). The proposed approach is capable of ordering candidate paths for allocation based on metrics such as crosstalk, fragmentation, and the number of slots. Besides, MISSION shows competitive performance, by keeping a comparatively low blocking probability and fragmentation, even under heavy loads.
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