Abstract
A fine-grained flexible frequency grid for elastic optical transmission
and space division multiplexing in conjunction with spectrally
efficient modulations is an excellent solution to the coming capacity
crunch. In space division multiplexed elastic optical networks
(SDM-EONs), the routing, modulation, core, and spectrum assignment
(RMCSA) problem is an important lightpath resource assignment problem.
Intercore cross talk (XT) reduces the quality of parallel
transmissions on separate cores, and the RMCSA algorithm must ensure
that XT requirements are satisfied while optimizing network
performance. There is an indirect trade-off between spectrum
utilization and XT tolerance; while higher modulations are more
spectrum efficient, they are also less tolerant of XT since they
permit fewer connections on neighboring cores on the overlapping
spectra. Numerous XT-aware RMCSA algorithms restrict the number of
litcores, cores on which overlapping spectra are occupied, to
guarantee XT constraints are met. In this paper, we present a machine
learning (ML) aided threshold optimization strategy that enhances the
performance of any RMCSA algorithm for
any network model. We show that our strategy applied to a few
algorithms from the literature improves the bandwidth blocking
probability by up to three orders of magnitude. We also present the
RMCSA algorithm called spectrum-wastage-avoidance-based resource
allocation (SWARM), which is based on the idea of spectrum wastage due
to spectrum requirements and XT constraints. We note that SWARM not
only outperforms other RMCSA algorithms, but also its ML-optimized
variant outperforms other ML-optimized RMCSA algorithms.
Funder
National Science Foundation
Subject
Computer Networks and Communications
Cited by
3 articles.
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