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Center for Nonlinear Studies |
Memory consumption is a critical problem for large-scale network simulations. Particularly, the large memory footprint needed for maintaining routing tables can severely obturate scalability. We present an approach of composing largescale network models using sharable model fragments to achieve significant reduction in the amount of memory required for storing forwarding tables in simulation. Our approach, called spherical routing, conducts static routing within spheres according to user-defined policies. Our routing scheme pre-calculates the forwarding table for each routing sphere, and allows spheres with identical sub-structures to share forwarding tables. Through extensive experiments we demonstrate that our approach can achieve several orders of magnitude in memory reduction for large-scale network models.
Host: Stephan Eidenbenz