A Re-optimization Approach for Virtual Network Embedding

• Published in: Mobile Networks and Management pp. 271 - 283
• Main Authors: Melo, Márcio, Carapinha, Jorge, Sargento, Susana, Killat, Ulrich, Timm-Giel, Andreas
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg
• Series: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
• Subjects: Assignment; Embedding; ILP; Mapping; Network Virtualization; NP-Hard; Optimization; Re-Optimization; Virtual Network
• ISBN: 9783642379345; 3642379346
• ISSN: 1867-8211; 1867-822X
• DOI: 10.1007/978-3-642-37935-2_21

Network Virtualization is claimed to be a key component of the Future Internet by enabling the coexistence of heterogeneous (virtual) networks in the same physical infrastructure, providing the dynamic creation and support of different networks with different paradigms and mechanisms in the same physical network. A major challenge in the dynamic provision of virtual networks resides in the optimal embedding solution of virtual resources into physical ones. Since this problem is known to be \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\mathcal{NP}$\end{document}-hard, previous research focused on designing heuristic-based algorithms; most of them do not consider either a simultaneous optimization of the node and the link mapping or the re-optimization of VNs, leading to non-optimal solutions. This paper proposes an extension of Virtual Network Embedding - Node-Link Formulation to support the re-optimization of existing VNs and to provide the optimal bound. It also presents an evaluation of the proposed approach when applied to a previous heuristic in the literature. Simulation experiments show significant improvements when using the VN re-optimization process: not only the bandwidth consumption have been reduced by 17.5%, but the same is true for the maximum utilization levels on the CPU and on the memory.