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    This study introduces two new algorithms, MaVEn-M and MaVEn-S, for the virtual network embedding (VNE) problem. These methods improve profitability for infrastructure providers by optimizing virtual network placement on physical networks.

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    Area of Science:

    • Computer Science
    • Network Engineering

    Background:

    • Current Internet architecture has limitations.
    • Network virtualization allows multiple virtual networks (VNs) on physical infrastructure.
    • The virtual network embedding (VNE) problem is computationally complex (-hard).

    Purpose of the Study:

    • Propose novel algorithms for efficient VNE.
    • Maximize infrastructure provider profit.
    • Enhance the performance of virtual network embedding.

    Main Methods:

    • Developed two VNE algorithms: MaVEn-M (multicommodity flow) and MaVEn-S (shortest-path).
    • Formalized virtual node mapping using Markov decision process (MDP).
    • Employed Monte Carlo tree search for devising node mapping policies.
    • Utilized a discrete event VNE simulator for performance evaluation.

    Main Results:

    • MaVEn-M and MaVEn-S demonstrate superior profitability compared to existing algorithms.
    • Introduced 'profitability' as a key performance metric (acceptance and revenue-to-cost ratios).
    • Algorithm performance improves with additional computation time.

    Conclusions:

    • The proposed MaVEn algorithms offer more profitable solutions for VNE.
    • Flexibility in execution time allows adaptation to traffic loads.
    • These algorithms represent a significant advancement in virtual network embedding optimization.