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For a given fiber network and a given set of client demands, the transparent optical network design problem is the task of assigning routing paths and wavelengths for a set of lightpaths able to groom all client demands. We address this design problem minimizing the impact of a given set of critical nodes. The problem is tackled in two steps: first...
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... computational results were based on the problem instances used in [9] considering the German Backbone Network (Fig. 1) and |T | = 80 wavelengths. All instances consider lightpaths characterized by δ 1 = 4, δ 2 = 10, l 1 = 2500 km and l 2 = 2000 km. The length of a path is the sum of the fiber lengths plus 160 km per intermediate node, resulting in a set P with almost 2300 paths and around 60% of them within l 2 . Lightpaths of type 1 have a cost c p1 = ...
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An approach is proposed in flex-grid optical networks that employ path-based protection and it is combined with the problem of traffic grooming under dynamic traffic scenario. This approach is suitable for multicast traffic where each of the traffic demands are provisioned by a light-tree based approach. This proposed approach is compared with non-...
Citations
... For a given network topology, if some nodes are considered critical due to some reason, the network design should take it into consideration, as in [3] where the approach proposed in [1] is adapted to the design of a transparent optical network minimizing the failure impact of a given set of critical nodes. ...
... Algorithm 2 is an iterative process (lines [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] where each iteration is composed of two phases: an adding phase, where links are added to set E ′ in order to improve the CND value of the resulting topology until the available budget is not enough to add more links; and a removing phase, where all links in E ′ are reevaluated and removed if their elimination does not decrease the CND optimal value of the resulting topology. In each iteration, set E ′ add (and set E ′ rem ) represents the set of links added to (and removed from) the network in the current iteration. ...
... Similarly to Algorithm 1, the MILP ′ model is initialized without the path constraints (line 2). Then, in order to accelerate the row generation process, the path constraints associated to each set of critical nodes K ∈ are added to MILP ′ model (lines [3][4][5][6][7][8][9][10][11]. Next, the optimal solution of the exact CND model is solved as in Algorithm 1. ...
This paper addresses two related problems in the context of transparent optical networks. In the network design problem, the aim is to identify a set of fiber links to connect a given set of nodes. In the network upgrade problem, the aim is to identify a set of new fiber links to add to a given network topology. For a given fiber length budget, the aim in both problems is to maximize the network resilience to the simultaneous failure of its critical nodes. The resilience is evaluated by the average 2‐terminal reliability (A2TR) against a set of critical node failures and the critical nodes are the ones that minimize the A2TR of the network. So, the design/upgrade problem is a bi‐level max‐min optimization problem. Recently, a multi‐start greedy randomized heuristic was proposed for both problems. Here, we propose an alternative method based on a greedy deterministic algorithm and we provide computational results showing that the new method obtains better solutions. The results show that the resiliency difference between existing network topologies and the best network design solutions is very high but this difference can be significantly reduced by network upgrades with small fiber length budgets.
... First, we address the resilience evaluation of the EON to multiple node failures. If the critical nodes are given, the RMSA can maximize the total demand still supported when all critical nodes fail, as in [16] where the approach in [17] is adapted to such case. Here, the critical nodes are not given. ...
... If some network nodes are considered critical due to some reason, then, the optical network design must take into consideration this fact. An example is [10] where the network design approach proposed in [11] is adapted to the design of a transparent optical network minimizing the impact of the simultaneous failure of a given set of critical nodes. In that work, the critical node set is given while here the aim is to determine the set of critical nodes of a given transparent optical network. ...
For a given graph representing a transparent optical network, a given weight associated to each node pair and a given positive integer c, the Critical Node Detection problem variant addressed here is the determination of the set of c nodes that, if removed from the graph, minimizes the total weight of the node pairs that remain connected. In the context of transparent optical networks, a node pair is considered connected only if the surviving network provides it with a shortest path not higher than a given positive value T representing the optical transparent reach of the network. Moreover, the length of a path depends both on the length of its links and on its number of intermediate nodes. A path-based Integer Linear Programming model is presented together with a row generation approach to solve it. We present computational results for a real-world network topology with 50 nodes and 88 links and for \(c=2\) up to 6. The optimal results are compared with node centrality based heuristics showing that such approaches provide solutions which are far from optimal.
... For a given topology, if some nodes are considered critical due to some reason, the network design should take this into consideration, as in [4] where the approach proposed in [5] is adapted to the design of a transparent optical network minimizing the failure impact of a given set of critical nodes. Here, we consider the resiliency metric defined by the average 2terminal reliability (A2TR) and, for a given network topology, we evaluate this metric against a set of critical node failures. ...
Consider the resilience of a network defined by the average 2-terminal reliability (A2TR) against a set of critical node failures. Consider an existing transparent optical network with a total fibre length L. The first goal of this paper is to assess the resiliency gap between the existing topology and a new network topology designed to maximize its resilience with the same fibre budget L. The resiliency gap gives us a measure of how good the resilience of existing network topologies are. Consider now that an existing network is upgraded with new links aiming to maximize its resiliency improvement with a fibre budget L′. The second goal of this paper is to assess how much the resiliency gap can be reduced between a good upgraded solution and a network topology designed to maximize its resiliency with the same fibre budget L + L′. The gap reduction gives us a measure of how close to the best resilience the upgraded solutions can get for different values of L′.To reach these goals, we first describe how the Critical Node Detection problem is defined and solved in the context of transparent optical networks. Then, we propose a multi-start greedy randomized method to generate network topologies, with a given fibre length budget, that are resilient to critical node failures. This method is also adapted to the upgrade of an existing network topology. At the end, we run the proposed methods on network topologies with public available information. The computational results show that the resiliency gap of existing topologies is significantly large but network upgrades with L′ = 10%L can significantly reduce the resiliency gaps provided that such upgrades are aimed at maximizing the network resilience to multiple node failures.
