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Abstract

Abstract. In this paper, we study regenerator placement and traf®c engineering of restorable paths in generalized multiprotocol label switching (GMPLS) networks. Regenerators are necessary in optical networks in order to cope with transmission impairments. We study a network architecture where regenerators are placed only at selected nodes for decreasing cost of regeneration. We propose two heuristic algorithms for optimum placement of these regenerators. Performances of these algorithms in terms of required number of regenerators and computational complexity are evaluated. In this network architecture with sparse regeneration, off-line computation of working and restoration paths is studied for traf®c engineering with path rerouting as the restoration scheme. We study two approaches for selecting working and restoration paths from a set of candidate paths and formulate each method as an integer linear programming (ILP) problem. A traf®c uncertainty model is developed in order to compare these methods based on their robustness with respect to changing traf®c patterns. Traf®c engineering methods are compared based on number of additional demands resulting from traf®c uncertainties that can be carried over the network. Proposed heuristic regenerator placement algorithms are also evaluated from a traf®c engineering point of view.

A Simple Approach to Dynamic Optimisation of Flexible Optical Networks with Practical Application

“…Optimising the location of opto-electro-optical (OEO) regenerators (hereafter referred to as relays) in optical networks (ONs) has, for much of the history of ONs, been considered a static problem [1]. Such devices have, until recently, been expensive to both install and operate.…”

“…Although (1) simplifies the problem, (2) significantly increases the challenge. For completeness, relevant optimisation problems and solutions from other fields (in networking and graph theory) include the Minimum Dominating Set (MDS) [14] and the Minimum Connected Dominating Set (MCDS) [15], Minimum Spanning Tree (MST) [16], Maximum Leaf Spanning Tree (MLSP) [17], and Shortest Path (SPP) [18] problems.…”