
Abstract Elastic optical networks are extremely effective networks for high speed optical communication owing to excessive data rate requirements in future years. Applying the space division multiplexing (SDM) technology with multi-core fibers (MCFs) provides possibility to increase the network capacity and data transmission rate as a consequence. One of the main issues in these types of networks is bandwidth fragmentation due to dynamic spectrum allocation and releasing. Management of this problem would lead to optimal use of network bandwidth. As a result, more connection demands can be accommodated. In this paper, two novel algorithms of multi-path routing are presented to improve fragmentation, where both algorithms are fragmentation-aware. The first algorithm, named Fragmentation-aware Best Splits (FABS), exploits fragmentation criteria as well as a new split technique called Best Split. In addition to these features, the second algorithm called Crosstalk-aware Best Split (CABS) considers inter-core crosstalk (XT) which is a significant issue in SDM. By employing these two algorithms and considering distance-adaptive modulation, our performance evaluation results illustrate a remarkable improvement in reducing blocking rate and enhancement of spectrum utilization.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 29 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
