A Self-Adaptive Wireless Network Service Embedding through SVM and MTA

Network virtualization (NV) provides a feasible mechanism for operating numerous diverse virtual networks concurrently on a shared physical infrastructure network. The key issue in NV is virtual network embedding (VNE), which efficiently and effectively maps virtualized networks (VNs) with multiple resource needs for nodes and links to the underlying physical network with limited resources. A multiple topological attributes (MTA) based embedding algorithm is proposed to address the issue of providing different virtual request ser-vices delivered in a wireless network environment, leading to an unstable utilization of physical network resources and a low access rate for subsequent requests. It is emphasized that machine learning (ML) should be integrated into the process of network slicing in order to properly classify the received wireless virtual request. In this work, virtual request services are categorized automatically using support vector machine (SVM), and resources are allocated accordingly. The proposed technique organizes nodes in the embedding process according to their priority based on multiple topological properties of virtual and physical networks. According to the findings of the simulations, the SVM-MTA algorithm enhances both the acceptance rate and the resource efficiency of the network.