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LIVE: Learning and Inference for Virtual Network Embedding

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Abstract

Network virtualization provides a promising tool for next-generation network management by allowing multiple heterogeneous virtual networks to run on a shared substrate network. A long-standing challenge in network virtualization is how to effectively map these virtual networks onto the shared substrate network, known as the virtual network embedding (VNE) problem. Most heuristic VNE algorithms find practical solutions by leveraging a greedy matching strategy in node mapping. However, greedy node mapping may lead to unnecessary bandwidth consumption and increased network fragmentation because it ignores the relationships between the mapped virtual network requests and the mapping ones. In this paper, we re-visit the VNE problem from a statistical perspective and explore the potential dependencies between every two substrate nodes. We define a well-designed dependency matrix that represents the importance of substrate nodes and the topological relationships between them, i.e., every substrate node’s degree of belief. Based on the dependency matrix generated from collecting and processing records of accepted virtual network requests, Bayesian inference is leveraged to iteratively select the most suitable substrate nodes and realize our novel statistical VNE algorithm consisting of a learning stage and an inference stage in node mapping. Due to the overall consideration of the relationships between the mapped nodes and the mapping ones, our statistical approach reduces unnecessary bandwidth consumption and achieves a better performance of embedding. Extensive simulations demonstrate that our algorithm significantly improves the long-term average revenue, acceptance ratio, and revenue/cost ratio compared to previous algorithms.

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Acknowledgments

We are grateful to the editors and anonymous reviewers for their precious comments and suggestions, which have significantly improved this paper. We also thank Prof. Raouf Boutaba and Prof. Reaz Ahmed from University of Waterloo for their selfless help in revising this paper. This work was jointly funded by: (1) National Basic Research Program of China (No. 2013CB329102); (2) National Natural Science Foundation of China (Nos. 61471063, 61421061, 61372120, 61271019); (3) Key (Keygrant) Project of Chinese Ministry of Education (No. MCM20130310); (4) Beijing Municipal Natural Science Foundation (No. 4152039); (5) Beijing Higher Education Young Elite Teacher Project (No. YETP0473) (6) Spanish Research Council (No. TIN2013-46883); (7) Regional Government of Madrid (No. S2013/ICE-2894); (8) China Scholarship Council (No. 201406470020).

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Authors and Affiliations

  1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, No. 10, Xi Tu Cheng Road, Beijing, China

    Jianxin Liao, Min Feng & Jingyu Wang

  2. China Academy of Telecommunication Research, No. 52, Hua Yuan Bei Road, Beijing, China

    Sude Qing

  3. Technical University of Madrid, Calle Ramiro de Maeztu, 7, 28040, Madrid, Spain

    Tonghong Li

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  1. Jianxin Liao
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Correspondence to Jianxin Liao.

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Liao, J., Feng, M., Qing, S. et al. LIVE: Learning and Inference for Virtual Network Embedding. J Netw Syst Manage 24, 227–256 (2016). https://doi.org/10.1007/s10922-015-9349-5

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