Publications (8)0 Total impact
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ABSTRACT: Introduction 2. Description of the problem 3. Mathematical model 4. Algorithms 5. Experimental results 6. Conclusions DRCN 2003 1. Introduction 2. Description of the problem 3. Mathematical model 4. Algorithms 5. Experimental results 6. Conclusions DRCN 2003 1. Introduction. Page 3. span Set of fibers connecting two adjacent switches. link Part of a span transferring information in one direction. wavelength An available carrier frequency. WDM channel A wavelength used on a particular link. lightpath A unidirectional end-to-end connection. span Set of fibers connecting two adjacent switches. link Part of a span transferring information in one direction. wavelength An available carrier frequency. WDM channel A wavelength used on a particular link. lightpath A unidirectional end-to-end connection. OXC OXC OXC span Set of fibers connecting two adjacent switches. link Part of a span transferring information in one direction. wavelength An available carrier frequency. WDM channel
11/2003;
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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;
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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;
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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;
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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-dimen
12/2002;
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ABSTRACT: This paper) # SP given # #max TS SP Shortest path routing policy /# Packet-switching multi/mono routing (spliting of demands) #/# Maximize / minimize #max Packet-switching tra#c congestion SA/TS Simulated Annealing / Tabu Search metaheuristics FwDv Flow Deviation packet-switched tra#c routing algorithm Problem Description Previous work Tabu Search Experiments Conclusions Title Page ## ## Go Back Full Screen Close 4. Tabu Search algorithm
11/2002;
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Josue Kuri
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ABSTRACT: Wavelength division multiplexing optical transport networks are expected to provide the capacity required to satisfy the growing demand of telecommunications traffic in a cost-effective way. These networks, based on standards and implementation agreements currently under development by the ITU-T, the IETF and the OIF, are likely to be deployed during the next 5 or 6 years. New optimization problems arise in connection with these networks for several reasons. Firstly, the cost of optical networking equipment is not still well known due mainly to the early stage of development of the relevant technologies. Secondly, the uncertainty of traffic demands, due to the competition in the telecommunications market and to the massive adoption of new data applications, render difficult the accurate dimensioning of networks. Finally, the early stage of development of optical technology results in new functional constraints that must be taken into account during the design and dimensioning of the network. We investigate optimization problems arising in the engineering of an optical transport network. Network engineering concerns the configuration of existing network resources in order to satisfy expected traffic demands. Unlike network planning and traffic engineering, network engineering problems are relevant at time scales ranging from hours to weeks. A these time scales, the dynamic evolution of the traffic load is an important factor that must be taken into account in the configuration of the network. Moreover, the periodicity of the traffic load evolution observed in operational transport networks suggest that the traffic may be modeled deterministically. We propose a dynamic deterministic traffic model called Scheduled Lightpath Demands (SLDs). An SLD is a connection demand represented by a tuple (s, d, n, alpha, omega) where s and d are the source and destination nodes of the demand, n is the number of requested connections and alpha, omega are the set-up and tear-down dates of the requested connections. The model captures the time and space distribution of a set of connection demands and, being deterministic,eases the use of combinatorial optimization techniques to solve network optimization problems. We investigate three network optimization problems involving the SLD traffic model: - We first study the Routing and Wavelength Assignment (RWA) for SLDs problem in a wavelength-switching network. The routing problem is formulated as a combinatorial optimization problem with two possible objective functions. We propose a Branch & Bound (B&B) and a Tabu Search (TS) algorithm that compute, respectively, exact and approximate solutions. Wavelength assignment is formulated as a graph coloring problem. We use an existing greedy algorithm to find approximate solutions. - We then investigate the problem of Diverse Routing and Spare Capacity Assignment (DRSCA) for SLDs in a wavelength-switching network. The problem consists of defining a pair of span-disjoint paths for each SLD so that the number of required working and spare channels is minimal. We propose a channel reuse technique to reduce the required working channels and a backup multiplexing technique to reduce the spare channels required for protection. The problem is formulated as a combinatorial optimization problem. We propose a Simulated Annealing (SA) meta-heuristic algorithm to compute approximate solutions. - Finally, we investigate the problem of Routing and grooming of SLDs (SRG) in a multi-granularity switching network. We consider a network whose nodes have a wavelength cross-connect (WXC) and a waveband cross-connect (BXC). The problem is formulated as a combinatorial optimization problem. We propose a parallel TS meta-heuristic algorithm to compute approximate solutions. We determine the conditions under which a network based on multi-granularity switches is more economical than a wavelength-switching (single-granularity) 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.
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