Open AccessProceedings Article
Virtual Network Mapping – An Optimization Problem
Márcio Melo,Jorge Carapinha,Susana Sargento,Luis Torres,Phuong Nga Tran,Ulrich Killat,Andreas Timm-Giel +6 more
- 14 Sep 2012
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TL;DR: In this article, an integer linear programming (ILP) formulation is proposed to solve the virtual network embedding problem, as a simultaneous optimization of virtual nodes and links placement, providing the optimal boundary for each virtual network mapping.
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Abstract: Network Virtualization is claimed to be a key component of the Future Internet by enabling the coexistence of heterogeneous (virtual) networks on 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 on the efficient embedding of virtual resources into physical ones. Since this problem is known to be \(\mathcal{NP}\)-hard, previous research focused on designing heuristic-based algorithms; most of them do not consider a simultaneous optimization of the node and the link mapping, leading to non-optimal solutions. This paper proposes an integer linear programming formulation to solve the virtual network embedding problem, as a simultaneous optimization of virtual nodes and links placement, providing the optimal boundary for each virtual network mapping. A link − node formulation is used and the multi-commodity flow constrain is applied. In addition, a heuristic algorithm for virtual network embedding is also proposed and compared against the optimal formulation. The performance of the integer linear programming formulation and of the heuristic is evaluated by means of simulation. Simulation experiments show significant improvements of the virtual network acceptance ratio, in average additional 10% of the virtual network requests are accepted when using the integer linear programming formulation, which corresponds, in average, to more 7 virtual networks accommodated on the physical network.
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Citations
Dedicated Protection for Survivable Virtual Network Embedding
Shihabur Rahman Chowdhury,Reaz Ahmed,Mashrur Alam Khan,Nashid Shahriar,Raouf Boutaba,Jeebak Mitra,Feng Zeng +6 more
TL;DR: This paper proposes Dedicated Protection for Virtual Network Embedding (DRONE), a suite of solutions to the 1 + 1-ProViNE problem, which includes an integer linear programming formulation for optimal solution (OPT-DRONE) and a heuristic (FAST-DRONES) to tackle the computational complexity of the optimal solution.
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Optimal mapping of virtual networks considering reactive reconfiguration
Phuong Nga Tran,Leonardo Casucci,Andreas Timm-Giel +2 more
- 01 Nov 2012
TL;DR: A so-called reactive reconfiguration mechanism, which reacts to any rejection of new coming virtual network requests, and minimizes the number of necessary changes to reduce the service disruption.
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Connectivity-aware virtual network embedding
Nashid Shahriar,Reaz Ahmed,Shihabur Rahman Chowdhury,Md. Mashrur Alam Khan,Raouf Boutaba,Jeebak Mitra,Feng Zeng +6 more
- 23 Feb 2016
TL;DR: Two solutions to the Connectivity-aware Virtual Network Embedding problem, which will enable a VN operator to perform failure recovery without depending on the SN provider, similar to the IP restoration mechanisms in IP-over-WDM networks are presented.
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Adaptive multi-objective artificial immune system based virtual network embedding
TL;DR: This paper first formulate the virtual network embedding problem into a multi-objective integer linear programming, then designs an artificial immune system based algorithm to solve this programming and shows that this algorithm outperforms the state-of-the-art algorithms in terms of the revenue and the energy consumption.
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Virtual Network Embedding With Guaranteed Connectivity Under Multiple Substrate Link Failures
Nashid Shahriar,Reaz Ahmed,Shihabur Rahman Chowdhury,Mashrur Alam Khan,Raouf Boutaba,Jeebak Mitra,Feng Zeng +6 more
TL;DR: Two novel solutions to coViNE leveraging conflicting set are proposed, namely CoViNE-ILP and CoVi NE-fast, which uses a heuristic algorithm to address sub-problems (i) and (ii), while an ILP is used for sub-problem (iii).
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