Virtual Network Embedding (VNE), which provides methods to assign multiple Virtual Networks (VN) to a single physical Substrate Network (SN), is an important task in network virtualization. The main problem in VNE is the efficiency of assigning customers’ virtual network requests to the substrate network. This problem is known to be a Non-deterministic Polynomial-time hard (NP-hard) and heuristic solutions have been developed to solve this kind of problem. The current trend toward Software-Defined Networking (SDN) has allowed new possibilities in virtual network embedding. In this work, we propose a distributed virtual network embedding for SDNs called DVSDNE using multi-agent systems. This framework could be used to run a centralized VNE algorithm in a distributed manner to scale these algorithms with respect to network size. DVSDNE uses agents to spread the load across the substrate network. Our simulation results show the effectiveness of the proposed algorithm. Results show that DVSDNE improves execution time of embedding algorithms in large scale substrate networks, while embedding results such as acceptance ratio, revenue to cost ratio, average latency to controller, and maximum latency to controller remain comparable.

It is very important to protect fingerprint templates in the fingerprint recognition systems. Fuzzy vault is a promising and applicable scheme for this purpose. It can protect biometric templates. Also it can perform secure key management. Alignment of the query fingerprint sample in the encrypted domain and the template fingerprint sample is a challenging task. In this paper, we propose an alignment-free fingerprint cryptosystem based on multiple fuzzy vaults. In the proposed method, in registration phase, multiple vaults are constructed for one fingerprint and in verification phase, if at least two of the vaults are decoded successfully by the query fingerprint, the secret will be recovered. The Experiments of the proposed fingerprint cryptosystem are conducted on FVC2002-DBla and FVC2002-DB2a data sets to evaluate the performance of the proposed fingerprint cryptosystem.