SEDViN: Secure embedding for dynamic virtual network requests using a multi-attribute matching game

Abstract

Network virtualization (NV) has gained significant attention as it allows service providers (SP) to share substrate network (SN) resources. It is achieved by partitioning them into isolated virtual network requests (VNRs) comprising interrelated virtual machines (VMs) and virtual links (VLs). Although NV provides various advantages, such as service separation, enhanced quality-of-service, reliability, and improved SN utilization, it also presents multiple scientific challenges. In this context, one pivotal challenge encountered by the researchers is secure virtual network embedding (SVNE). The SVNE encompasses assigning SN resources to components of VNR, i.e., VMs and VLs, adhering to the security demands, which is a computationally intractable problem, as it is proven to be $\mathrm{NP}$-Hard. In this context, maximizing the acceptance and revenue-to-cost ratios remains of utmost priority for SPs as it not only increases the revenue but also effectively utilizes the large pool of SN resources. Though VNE is a well-researched problem, the existing literature has the following flaws: (i.) security features of VMs and VLs are ignored, (ii.) limited consideration of topological attributes, and (iii.) restricted to static VNRs. However, SPs need to develop an embedding framework that overcomes the abovementioned pitfalls. Therefore, this work proposes a framework Secure Embedding for Dynamic Virtual Network requests using a multi-attribute matching game (SEDViN). In SedViN, the deferred acceptance algorithm (DAA) based matching game is used for effective embedding. SEDViN operates primarily in two steps to obtain a secure embedding of dynamic VNRs. Firstly, it generates a unified ranking for VMs and servers using a combination of entropy and a technique for order of preference by similarity to the ideal solution (TOPSIS), considering network, security, and system attributes. Taking these as inputs, in the second step, VNR embedding is conducted using the deferred acceptance approach based on a one-to-many matching strategy for VM embedding and VL embedding using the shortest path algorithm. The performance of SEDViN is evaluated through simulations and compared against different baseline approaches. The simulation outcomes exhibit that SEDViN surpasses the baselines with a gain of 56% in the acceptance and 44% in the revenue-to-cost ratios.