IViN : An Efficient Multi‐Attributed Traffic Intensity Based Energy Aware Embedding for Online Virtual Network Requests

ABSTRACT Virtual Network Embedding (VNE) plays a crucial role in optimizing physical network (PN) resource utilization in network virtualization and delivering service benefits such as isolation, cost efficiency, flexibility, security, and Quality of Service (QoS) to end users. Despite its importance, VNE faces significant challenges, such as assigning resources to Virtual Network Requests (VNRs) to drive lower energy consumption, which can unfavorably affect network performance. VNE constitutes two corresponding subproblems: virtual machine embedding and virtual link embedding, and both problems are treated as hard. In this context, minimizing energy consumption remains vital for SPs by effectively utilizing PN resources, as it not only increases the revenue‐to‐cost ratio but also enhances the acceptance of VNRs. This work introduces a novel heuristic framework called the Multi‐Attributed Traffic Intensity Based Energy Aware Embedding for Online Virtual Network Requests (IViN) framework, designed to enhance the acceptance ratio while minimizing energy consumption. IViN considers a multi‐attribute approach from system and network features in its heuristic ranking mechanism to rank virtual machines and servers during virtual machine embedding, followed by a virtual link assignment using the shortest path approach. These attributes play a crucial role in effectively capturing the dependencies between network elements. This helps IViN achieve energy‐sensitive resource allocation and improves VNR acceptance and revenue‐to‐cost ratio. We validate the proposed approach by comparing it with existing methods through simulation experiments. The results show that IViN outperforms the baseline techniques by achieving improvements of 41%, 60%, and 34% in acceptance ratio, revenue‐to‐cost ratio, and energy consumption, respectively.