期刊
ELECTRONICS
卷 11, 期 10, 页码 -出版社
MDPI
DOI: 10.3390/electronics11101662
关键词
network virtualization; software-defined networking; secure virtual network embedding; security cost; security level
资金
- 111 project [B17007]
- Director Funds of Beijing Key Laboratory of Network System Architecture and Convergence [2017BKL-NSAC-ZJ-01]
- National Natural Science Foundation of China (NSFC) [61872401]
This study explores the relationship between resource value and node security level in SDN networks, presents evaluation indicators considering differences in resource value, and proposes two novel SVNE algorithms based on node ranking methods. Simulation results demonstrate the superiority of the proposed algorithms over other typical algorithms in improving resource utilization and network security in SDN environments.
Software-defined networking (SDN) and network virtualization (NV) are key technologies for future networks, which allow telecommunication service providers (TSPs) to share network resources with users in a flexible manner. Since TSPs have limited virtualized network resources, it is critical to develop effective virtual network embedding (VNE) algorithms for an SDN network to improve resource utilization. However, most existing VNE algorithms ignore the security issues of SDN networks, which may be subject to malicious attacks due to their openness feature. Therefore, it is necessary to develop secure VNE (SVNE) for SDN networks. In this paper, we researched the relationship between resource value and node security-level, and we found that there are differences in the resource value of different nodes. Based on this analysis, we define the evaluation indicators considering differences in resource value for the SVNE problem. Then, we present a mixed-integer linear program (MILP) model to minimize the cost of SVNE. As the formulated optimization problem cannot be solved conveniently, we design two node-ranking approaches to rank physical and virtual nodes, respectively, and we propose two novel SVNE algorithms based on the node ranking approaches. Finally, simulation results reveal that our proposed algorithm is superior to other typical algorithms.
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