On the Efficient Dynamic Routing in SpectrallySpatially Flexible Optical Networks In this paper we focus on the efficient dynamic routing in spectrallyspatially flexible optical networks (SSFON). To this end, we propose novel adaptive spectralspatial allocation (ASSA) algorithm supported by high load spectralspatial allocation (HLSSA) mechanism. The main principle of the ASSAHLSSA is to allow for demand allocation using spectralspatial channels only in the case of high network load while prioritizing channels using lowest possible number of spatial modes. Then, we evaluate efficiency of the proposed approach by means of extensive numerical experiments. The results show its high efficiency especially for sets of relatively big demands and in a survivable network (in that case protected by dedicated path protection). According to the experiments, the methods allow to reduce bandwidth blocking probability up to 5.5% comparing to the allocation without their application.

Fragmentation Metrics in SpectrallySpatially Flexible Optical Networks Spectrallyspatially flexible optical networks (SSFONs) are proposed as a solution for future traffic requirements in optical backbone networks. As SSFONs operate within flexgrid, the provisioning of lightpaths spanning multiple frequency slots results in spectrum fragmentation, especially in presence of dynamic traffic. Fragmentation, in turn, may lead to blocking of dynamic requests due to the lack of sufficientlylarge free spectral windows. In this paper, to reach a better understanding of fragmentation in SSFON, we extend several metrics used in (singlecore) elastic optical networks to measure the fragmentation in SSFONs. Next, we apply these metrics to a dynamicrouting algorithm with the goal of minimizing bandwidth blocking. Finally, we analyze the impact of spatial continuity constraint (SCC) on the network fragmentation. Simulations run on two representative network topologies show that the root mean square factor metric yields the best performance in terms of blocking when compared to other analyzed metrics.