On Measurement Endpoint Placement Using Genetic Algorithms for Network Observability

Determining an optimal placement for active measurement points in an arbitrary network topology is challenging. Software-defined infrastructure and the virtualization of network functions imply that re-optimized placement is needed frequently to keep up with dynamic changes in the infrastructure. We present a novel genetic algorithm that was defined for optimizing the placement of active measurement points in this environment. Initial results from simulations show that the method is effective and efficient in producing good solutions for four different topologies inspired from real networks. We also devised a strategy that enables faster reaction to incremental changes in the measured topology, reducing in half the execution time for two of the topologies.