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Energy aware optimization of mobile metro-core network under predictable aggregated traffic patterns

The high predictability in human mobility patterns is a valuable information to optimize resource allocation and increase energy efficiency by effectively adapting to expected traffic load variations. In this work, we addressed the impact of human mobility patterns of mobile users in a metro area over the aggregated traffic offered to the mobile metro-core networks. Exploiting predictable trends recognized in aggregated traffic, we propose two optimization methods to reduce the energy consumption in the optical layer of metro-core networks. Our results display energy savings of more than 20% by introducing load adaptive network operation.