Publications (7)0 Total impact
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Article: IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 21, NO. 8, OCTOBER 2003 1231 Routing and Wavelength Assignment of Scheduled
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ABSTRACT: In this paper, we present algorithms that compute the routing and wavelength assignment (RWA) for scheduled lightpath demands in a wavelength-switching mesh network without wavelength conversion functionality. Scheduled lightpath demands are connection demands for which the setup and teardown times are known in advance. We formulate separately the routing problem and the wavelength assignment problem as spatio-temporal combinatorial optimization problems. For the former, we propose a branch and bound algorithm for exact resolution and an alternative tabu search algorithm for approximate resolution. A generalized graph coloring approach is used to solve the wavelength assignment problem. We compared the proposed algorithms to an RWA algorithm that sequentially computes the route and wavelength assignment for the scheduled lightpath demands.11/2003; -
Article: Routing and wavelength assignment of scheduled lightpath demands.
IEEE Journal on Selected Areas in Communications. 01/2003; 21:1231-1240. -
Article: Unknown
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ABSTRACT: blem. . We developed both exact and approximate resolution algorithms for the combinatorial optimization problem. Routing problem: mathematical model G = (V, E, w) is an edge-weighted undirected graph with vertex set V = {v 1 , v 2 , . . . , v N }, edge set E = {e 1 , e 2 , . . . , e L and weight function w : E R + . # = {# 1 , # 2 , . . . , # M is the set of M SLDs, where # i = (s i , d i , n i , # i , # i ) is a tuple representing the SLD number i; s i , d i V are the source and destination nodes, n i is the number of requested lightpaths, and # i and # i are the set-up and tear-down dates. (G, #) is a pair representing an instance of the RWA problem. N = |V |, L = |E|, K max are, respectively, the number of vertices and edges in G and the maximum number of possible alternate paths for each demand. Routing problem: mathematical model P k,i , 1 k K max , 1 i represents the k alternate path in G from s i to d i . # #,# = (P # 1 ,1 P # 2 ,2 . . . P #12/2002; -
Article: Routing And Wavelength Assignment Of Scheduled
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ABSTRACT: We present RWA algorithms for a new class of tra#c model where, besides the source, destination and size (number of lightpaths) of the tra#c demands, their set-up and tear-down dates are known. They are called Scheduled Lightpath Demands (SLDs). We model the RWA problem as a spatio-temporal combinatorial optimization problem and provide two solution algorithms. The time disjointness that could exist among SLDs is taken into account in order to maximize the utilization of resources and hence, minimize the amount of globally required resources. We compare our algorithms to an online RWA algorithm and show that taking into account the time disjointness of demands can lead to a gain of resources of 20 % in average.12/2002; -
Article: Routing Foreseeable Lightpath Demands Using a Tabu Search Meta-heuristic
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ABSTRACT: In this paper we investigate the problem of routing a set of lightpath demands for which the set-up and tear-down dates are known. We call this type of requests Foreseeable Lightpath Demands or FLDs. In a transport network, FLDs correspond, for example, to clients' requests for pre-provisioned bandwidth capacity such as fixed-bandwidth pipes for bulk data transfers during the night, extra VPN bandwidth used during peak office working time, etc. Since in some cases the FLDs are not all simultaneous in time, it is possible to reuse physical resources to realize time-disjoint demands. We propose a routing algorithm that takes into account this property to minimize the number of required WDM channels in the physical links of the network. The gain (in terms of saved WDM channels) provided by the algorithm, when compared to a shortest path routing strategy, depends both on the spatial and temporal structure of the set of traffic demands and on the structure of the physical network. The routing problem is formulated as a spatio-temporal combinatorial optimization problem. A Tabu Search meta-heuristic algorithm is developed to solve this problem. I.12/2002; -
Article: Routing and Wavelength Assignment of Scheduled Lightpath Demands in a WDM Optical Transport Network Josue Kuri, Nicolas Puech, Maurice Gagnaire, Emmanuel Dotaro
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ABSTRACT: and approximate solutions. Routing subproblem: mathematical model G = (V, E, w) is an edge-weighted undirected graph with vertex set V = {v 1 , v 2 , . . . , v N }, edge set E = {e 1 , e 2 , . . . , e L and weight function w : E R + . # = {# 1 , # 2 , . . . , # M is the set of M SLDs, where # i = (s i , d i , n i , # i , # i ) is a tuple representing the SLD number i; s i , d i V are the source and destination nodes, n i is the number of requested lightpaths, and # i and # i are the set-up and tear-down dates. (G, #) is a pair representing an instance of the RWA problem. N = |V |, L = |E|, K max are, respectively, the number of vertices and edges in G and the maximum number of possible alternate paths for each demand. Routing subproblem: mathematical model P k,i , 1 k K max , 1 i represents the k th alternate path in G from s i to d i . # #,# = (P # 1 ,1 P # 2 ,2 . . . P # ,M ), # is called an admissible routing solution for #. # is an M-dimen12/2002; -
Article: ROUTING AND WAVELENGTH ASSIGNMENT OF SCHEDULED LIGHTPATH DEMANDS IN A WDM OPTICAL TRANSPORT NETWORK
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ABSTRACT: We present RWA algorithms for a new class of traffic model where, besides the source, destination and size (number of lightpaths) of the traffic demands, their set-up and tear-down dates are known. They are called Scheduled Lightpath Demands (SLDs). We model the RWA problem as a spatio-temporal combinatorial opti-mization problem and provide two solution algorithms. The time disjointness that could exist among SLDs is taken into account in order to maximize the utilization of resources and hence, minimize the amount of globally required resources. We compare our algorithms to an on-line RWA algorithm and show that taking into account the time disjointness of demands can lead to a gain of resources of 20 % in average.3119.
