Network virtualization enables the increasingly diverse network services to cohabit and share a given physical infrastructure and its resources, while potentially relying on different network architectures and protocols optimized towards the specific requirements. However, in order to ensure a predictable performance despite the shared resources, network virtualization requires a strict performance isolation and hence resource
reservations. Moreover, the creation of virtual networks should be fast and efficient. The underlying NP-hard algorithmic problem is known as the Virtual Network Embedding (VNE) problem and has been studied intensively over the last years. This paper presents NeuroViNE, a novel approach to speed up and improve a wide range of existing VNE algorithms: NeuroViNE is based on a search space reduction mechanism and preprocesses a problem instance by extracting relevant subgraphs, i.e., good combinations of substrate nodes and links. These subgraphs can then be fed to an existing algorithm for faster and more resource-efficient placements. NeuroViNE relies on a Hopfield network, and its performance benefits are investigated in extensive simulations for random networks, real substrate networks, and data center networks.
«Network virtualization enables the increasingly diverse network services to cohabit and share a given physical infrastructure and its resources, while potentially relying on different network architectures and protocols optimized towards the specific requirements. However, in order to ensure a predictable performance despite the shared resources, network virtualization requires a strict performance isolation and hence resource
reservations. Moreover, the creation of virtual networks should be f...
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