Hierarchical design of an attractor structure for VNT control based on attractor selection

Our research group has proposed a VNT control method that is adaptive to traffic changes. The method is based on a dynamical system, called attractor selection, which models behavior where living organisms adapt to unknown changes in their surrounding environments and recover their conditions. One of important things of our VNT control method is how to determine attractors, i.e., VNT candidates, because a VNT configured by our VNT method finally converges on one of the VNT candidates. However, since the number of VNT candidates is limited, it is crucial that the limited number of attractors have diversity so that various kinds of VNTs are searched by attractor selection. In this paper, we propose a method to decide the VNT candidates. Our approach prepares the VNT candidates whose bottleneck links (lightpaths) are different to each other. However, this approach has a problem that it takes a heavy computational time for large-scaled networks. We therefore propose a method that divides a network into clusters for which our algorithm can be applied. Evaluation results show that the VNT candidates prepared by our method can suppress maximum link utilization than the ones prepared in a random manner or by an existing heuristic algorithm.