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ROUTING AND WAVELENGTH ASSIGNMENT VS. WAVELENGTH CONVERTER PLACEMENT IN ALL-OPTICAL NETWORKS Bo Li and Xiaowen Chu, Hong Kong University of Science and Technology Kazem Sohraby, University of Arkansas

A BSTRACT Blocking has been the key performance index in the design of an all-optical network. Existing research demonstrates that an effective routing and wavelength assignment strategy and a proper wavelength converter placement algorithm are the two primary vehicles for improving the blocking performance. However, these two issues have largely been investigated separately in that the existing RWA algorithms have seldom considered the presence of wavelength conversion, while the wavelength converter placement algorithms have largely assumed that a static routing and random wavelength assignment algorithm is employed. The main objective of this article is to present some strong evidences that these two issues need to be considered jointly, and call for the reexamination of both RWA and wavelength converter placement.

I NTRODUCTION Wavelength-routed all-optical wavelength-division multiplex (WDM) networks are considered candidates for next-generation wide area backbone networks. Such networks consist of wavelength routers interconnected by fiber links, and each link can support a number of wavelength channels by using WDM. An end-to-end lightpath has to be established prior to the communication between any two routers [1]. To establish a lightpath, it is normally required that the same wavelength be allocated on all the links along the path. This limitation is known as the wavelength continuity constraint, which makes the wavelength-routed networks different from the traditional circuit-switched public switched telephone network (PSTN). A sequence of lightpath requests arrive over time, each lightpath having a random holding time. These should be set up dynamically by determining a route across the network connecting The work is supported in part by RGC grants under contracts AoE/E01/99.

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the source to the destination and assigning a free wavelength along the path. Existing lightpaths cannot be rerouted to accommodate new lightpath requests until they are released, so some of the lightpath requests may be blocked if there is no free wavelength along the path. One of the primary design objectives of wavelength-routed all-optical networks is to minimize the blocking probability. Wavelength conversion eliminates the wavelength continuity constraint and thus improves the blocking performance significantly [2, 3]. Since wavelength converters are still very expensive, much research focuses on sparse wavelength conversion, in which only some of the network nodes have the capability of wavelength conversion. In this article a wavelength router capable of wavelength conversion is called a wavelength convertible router (WCR). If all the network nodes are WCRs, this is referred to as full wavelength conversion. Subramaniam et al. have shown that by using sparse wavelength a relatively small number of WCRs can achieve satisfactory performance [4]. The problem of wavelength converter placement is very important in sparse wavelength conversion. That is, given a network topology, a number of WCRs, and traffic statistics, how can the WCRs be placed in order to minimize the overall blocking probability? Optimal converter placement for arbitrary mesh topologies has been shown to be NP-hard, and many heuristic algorithms have been proposed. The existing research demonstrates that an effective routing and wavelength assignment (RWA) algorithm is one of the primary vehicles for improving blocking performance [5]. For simplicity, the RWA problem can be split into two independent parts: • Find a route from the source to the destination. • Assign a wavelength (or wavelengths) to the selected route. The literature results have shown that the first-fit wavelength assignment scheme can achieve very good performance and is very simple to implement [6]. Therefore, we choose the firstfit wavelength assignment scheme in this work and mainly focus on the routing algorithms. The routing algorithms can generally be classified into

IEEE Optical Communications • August 2003

: End router

two classes: static and dynamic (or adaptive). : Wavelength router without conversion capability In static routing, the routes are determined under a prior given traffic matrix. It has been : Wavelength convertible router shown that blocking probabilities grow with increasing number of hops much faster than those in the circuit-switched telephone networks due to the wavelength continuity conSegment Segment straint [7]. Therefore, a shortest path routing (SPR) algorithm is the first one to be investigated. However, the performance of the SPR FIGURE 1. A route and its segments. algorithm is very limited because the shortest path links can be heavily loaded while other links are usually lightly loaded, resulting in low link utilization. To alleviate the drawback of shortest path er, does not work well in networks with sparse and/or full routing algorithm, Harai et al. proposed the fixed-alternate wavelength conversion. The main reason is that the existing routing (FAR) algorithm [8]. The FAR algorithm can dynamic routing algorithms usually only take into account improve the blocking performance by introducing more the distribution of free wavelengths (i.e., they select a route routes for each pair of nodes. If there is no available wavewith more free wavelengths) and do not explicitly consider length on the primary route, the alternative route is selectthe length of routes. Evidently, in networks without waveed. Thus, the traffic can be distributed over more links, and length conversion, the route with more free wavelengths usuthe overall blocking performance improves. ally is of shorter length, since the probability of a longer The main drawback of static routing algorithms is that the route with more free wavelength is smaller than that of a routing decision does not consider the current network state. shorter route. However, in the presence of wavelength conIn dynamic routing, the routing decision is based on the curversion, the above property is considerably weakened. This rent network state. Least-loaded routing (LLR) is one of the will be further elaborated using numerical examples. Thereearly proposed dynamic routing algorithms [9]. The LLR algofore, the length of the routes has to be considered jointly rithm routes a connection request on the least congested path with the distribution of free wavelengths in a routing deciof a set of predetermined paths. The results show that the sion. In this section we propose a weighted least congestion LLR algorithm can improve performance significantly over routing and first-fit wavelength assignment algorithm, WLCRfixed-alternate routing algorithms. FF, in conjunction with a simple greedy wavelength convertHowever, all the above routing algorithms have seldom er placement algorithm, called Minimum Blocking Probability considered the presence of wavelength conversion. In this artiFirst (MBPF). We then show that this approach can outpercle we argue that any routing algorithm needs to take into form all existing routing and wavelength assignment algoaccount the underlying wavelength conversion. The purpose rithms by a large margin in the presence of wavelength of this article is to present convincing evidence that these two conversion (sparse or full). issues need to be considered jointly, and calls for reexaminaTHE SYSTEM MODEL AND WLCR-FF tion of both RWA and wavelength converter placement. This article is divided into two parts. First, we demonstrate We first present the system model and make some assumpthat the conventional RWA algorithms do not work well in tions: the presence of wavelength conversion since they usually only • The network consists of N nodes and J links. Each link has take into consideration the distribution of available waveW wavelengths labeled 1–W. lengths, and do not explicitly consider the lengths of routes. • The length of route R is denoted h(R). (2) (Ma) Through extensive simulation over a variety of topologies, we • {R(1) } is the set of routes precomputed for a , R a , …, R a show that a weighted least-congestion routing and first-fit wavenode pair a. These routes are the k-shortest edge-disjoint length assignment (WLCR-FF) RWA algorithm can achieve routes such that the blocking events on these routes are much better blocking performance than SPR-FF, FAR-FF, independent of each other. and LLR-FF algorithms in environments of sparse or full The number of free wavelengths on route R is denoted wavelength conversion. Second, by using simulation we show F(R). In the case of no wavelength conversion, F(R) is the that a heuristic-based converter placement algorithm called number of common free wavelengths on all the links in route Weighted Maximum Segment Length (WMSL) proposed for R. In the case of full wavelength conversion, F(R) is the smallsimple dynamic RWA (i.e., the LLR algorithm) under sparse est number of free wavelengths among all the links in route R. wavelength conversion not only outperforms existing waveIn the case of sparse wavelength conversion, route R is dividlength converter placement algorithms by a large margin, but ed into several segments, as illustrated in Fig. 1. F(R) is then also can achieve almost the same performance as that of full defined as the smallest value of free wavelengths among all wavelength conversion using the same RWA algorithm. Finalthe segments in route R. ly, we conclude the article by highlighting some future In the WLCR-FF RWA algorithm, a set of routes are preresearch opportunities. computed for each source-destination pair, which is usually the k-shortest link-disjoint paths. These routes will be recomputed if the network topology changes. If a lightpath connecTHE WLCR-FF RWA ALGORITHM tion request arrives at a node pair, it should make a decision to choose a route from the precomputed set of routes, and As discussed earlier, dynamic routing algorithms have been then assign a free wavelength to the selected route. shown to achieve much better blocking performance than Upon arrival of a connection request for node pair a, a SPR and FAR algorithms in networks without wavelength route is selected from the candidate routes. The WLCR-FF conversion. In a typical dynamic routing algorithm, a set of algorithm will make a route decision as follows. routes connecting the source-destination pair are searched in We associate a weight value W(R) for each candidate parallel, and the route with the maximum number of free route. The weight function W(R) is defined as wavelengths is selected to set up the lightpath. This, howev-


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F( R )

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h( R) After the calculation of all the weight values, the route with the maximum weight value to set up the lightpath is selected. If no wavelength is available on any of the routes (i.e., F(R) = 0 for all the routes), the connection request is blocked. Once a lightpath is set up, the first-fit wavelength assignment scheme will be employed on each segment in the selected route; for each segment, the free wavelength with the smallest label will be assigned to all the links in that segment. The key observation is that when a route decision is made, two important factors should be taken into account: • The number of free wavelengths • The lengths of the routes It is straightforward that the route with more free wavelengths should be selected, and at the same time the length of that route should not be too long. If there is no wavelength conversion, these two factors are correlated: a route with shorter length is very likely to have more free wavelengths than routes with longer length. However, if the network has the capability of wavelength conversion, the correlation between the number of free wavelengths and the route lengths is weak: a route with longer length is likely to have more free wavelengths

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than routes with shorter length. If we still prefer the route with more free wavelengths, it is possible that too many routes with long length will be selected, resulting in a high blocking probability. In principle, the weight function should be directly proportional to the number of free wavelengths and inversely proportional to the length of the route.

MBPF WAVELENGTH CONVERTER PLACEMENT ALGORITHM An exhaustive approach of enumerating all possible converter placements and choosing the best one is not efficient for large networks. Furthermore, the converter placement for a mesh topology has been shown to be NP-hard. Here we propose a heuristic algorithm of wavelength converter placement for an arbitrary mesh network that employs the WLCR-FF RWA algorithm. The algorithm places the WCRs one by one in a greedy fashion, and a node is selected for placing a WCR if the overall blocking probability can be minimized in each step. The algorithm is thus called Minimum Blocking Probability First, or the MBPF algorithm. The MBPF algorithm works as follows: 1 Denote the end-to-end node pair a, and let Ma be the number of disjoint routes for node pair a. Find all the routes (2) (Ma) R(1) for each node pair. a , Ra , …, Ra 2 The term candidate node means the node is not a WCR yet. For each candidate node, we first assume that a WCR has been placed and then calculate the 3 4 5 corresponding overall blocking probability using an analytical model presented in [10]. After 8 9 10 calculation of all candidate nodes, we place a WCR at the node that can result in the mini13 14 15 mum overall blocking probability. 3 If there are still WCRs left, 18 19 20 repeat step 2. The MBPF algorithm needs to solve the analytical model O(MN) 23 24 25 times. This is very efficient compared to the exhaustive searching of all the  N    M

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FIGURE 2. Network topologies: a) a ring network; b) a mesh-torus network; c) the 14-node NSFNET network.

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NUMERICAL RESULTS AND ANALYSIS OF THE WLCR-FF ALGORITHM Simulation Platform and Network Topologies — We built a discrete-eventdriven simulation platform using C++ for our research. The simulation platform supports all the mentioned RWA algorithms and wavelength conversion types. We then conducted extensive simulations to investigate the performance of the proposed WLCR-FF algorithm over two typical network


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Performance Analysis of the Ring Topology — Figure 3a depicts the blocking performance of different RWA algorithms in an 8node ring network without wavelength conversion. We observe that the FAR-FF, LLR-FF, and WLCR-FF algorithms work much better than the SPR-FF algorithm. This can be explained as follows: when the traffic load is low, the main reason for the blocking event is that there is no common free wavelength among the links along the route. When we provide two candidate routes for a node pair, the blocking events of these two routes can be considered to be independent. Hence, the blocking probability can be reduced. Another observation is that the performance of WLCR-FF is very close to that of LLR-FF, which is better than the FAR-FF algorithm. Dynamic RWA algorithms can improve the blocking performance because more wavelengths are free for future connections, and at the same time not too many long routes are used. Figure 3b shows the network blocking probability vs. the total traffic when there are four WCRs in the ring network. According to the MBPF converter placement algorithm, these WCRs are placed at nodes (1, 3, 5, 7). An important observation is that the blocking probability of the LLR-FF algorithm increases rapidly when the traffic load increases. The performance of the LLR-FF algorithm is even worse than the FARFF algorithm. However, the WLCR-FF algorithm can still achieve better performance than the FAR-FF algorithm. The drawback of the LLR-FF algorithm in the environment of sparse wavelength conversion is that they make a route decision based on the information of free wavelengths, not the length of each route. For most node pairs in a ring topology, one route is very short and another is very long. The LLR-FF algorithm is likely to use too many long routes and thus consume too many resources. On the other hand, the WLCR-FF algorithm takes into account the length of each route and avoids using too many long routes. Thus, the WLCR-FF can achieve better blocking performance. The performances of different RWA algorithms in the case of full conversion are shown in Fig. 3c. In the full conversion case, there is no wavelength continuity constraint. For the same reason, the LLR-FF algorithm uses too many long routes and therefore increases the blocking probability. We can observe that the performance of LLR-FF is worse than that of SPR-FF when the total traffic is higher than 100 Erlangs. The WLCR-FF algorithm works very well under full wavelength conversion.

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topologies: 8-node ring topology (Fig. 2a) and 25-node meshtorus topology (Fig. 2b). In our simulations, the lightpath connection requests arrive at the network following a Poisson process, and the connection holding time is exponentially distributed. We further assume that all the source-destination node pairs have the same traffic load measured in Erlangs. Each fiber link is assumed to carry 40 wavelength channels, which is one of the channel spacing standards defined by the International Telecommunication Union (ITU). We provide two edge-disjoint shortest paths for each source-destination pair. The two routes are edge-disjoint so that the blocking events on the two routes can be independent. In each simulation we generate 1 million connection requests and collect the number of blocked requests. For each topology, we compare the performance of the WLCR-FF algorithm to the SPR-FF, FAR-FF, and LLR-FF algorithms under three different environments: no wavelength conversion, sparse wavelength conversion, and full wavelength conversion. In the case of sparse wavelength conversion, the proposed MBPF converter placement algorithm is used to place a limited number of WCRs in the network.

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FIGURE 3. Blocking probability vs. traffic load in an eight-node ring network: a) no wavelength conversion; b) sparse wavelength conversion; c) full wavelength conversion.

We observe from Fig. 3 that wavelength conversion does not help much in a ring topology. Another observation is that the performance of sparse wavelength conversion with MBPF wavelength converter placement algorithm is very close to the performance of full wavelength conversion in a ring topology. Performance of the Mesh-Torus Topology — The performances of different RWA algorithms in a mesh-torus network in the environments of no wavelength conversion, sparse wavelength

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FIGURE 4. Blocking probability vs. traffic load in a 25-node mesh-torus network: a) no wavelengh conversion; b) sparse wavelength conversion; c) full wavelength conversion. conversion, and full wavelength conversion are depicted in Figs. 4a, 4b, and 4c, respectively. We omit the curve of SPRFF in these three figures because the blocking probability of SPR-FF algorithms is too large compared to other algorithms. From these figures, we observe that both LLR-FF and WLCR-FF improve blocking performance significantly over the FAR-FF algorithm. This is because a mesh-torus network

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THE WMSL WAVELENGTH CONVERTER PLACEMENT ALGORITHM FOR LLR-FF ALGORITHM The primary objective of wavelength converter placement is to minimize average blocking probability. This problem has been shown to be NP-hard for mesh topologies. Significant studies have been carried out; however, existing studies only evaluate the performance of the converter placement algorithm under the assumption that a specific RWA algorithm (usually the static routing and random wavelength assignment algorithm) is used. It has been shown that LLR-FF can improve blocking performance significantly. Therefore, it is desirable to find a converter placement mechanism that works well under the LLR-FF algorithm. In this section we first extend the LLR-FF algorithm to the case of sparse wavelength conversion. We then propose a heuristic algorithm named Weighted Maximum Segment Length (WMSL) for converter placement in a mesh network that employs the LLR-FF RWA algorithm.

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is much denser than a ring network. When the FAR-FF algorithm is used, most of the traffic will be distributed to the shortest route between each pair node, resulting in some links seldom being utilized. Dynamic RWA algorithms distribute the traffic evenly to all the links, and more wavelengths are available for future connections. Thus, they reduce the blocking probability significantly. From Figs. 4b and 4c, we also observe that WLCR-FF can achieve better blocking performance than LLR-FF in the cases of sparse and full conversion. In the case of sparse conversion, two wavelength converter placement algorithms are investigated: • Random • MBPF placement Under both placement schemes, the blocking probability is reduced by 15–25 percent if WLCR-FF is used instead of LLR-FF. This is because WLCR-FF makes a better trade-off between the number of available wavelengths and the lengths of the routes. Furthermore, it shows that the MBPF algorithm performs much better than random placement. Another important observation is that wavelength conversion is very helpful in mesh-torus networks. For example, for a blocking probability of less than 1 percent, the 25-node mesh-torus network can carry a total traffic of 500 Erlangs without wavelength conversion. With 10 WCRs, the carried traffic increases to 600 Erlangs. With full wavelength conversion, the network can carry a total traffic of 660 Erlangs.

THE LLR-FF RWA ALGORITHM WITH SPARSE WAVELENGTH CONVERSION This subsection presents the LLR-FF RWA algorithm with sparse wavelength conversion. We assume that M WCRs have been placed in the network. When a connection request for node pair a arrives, we select a route and assign wavelength(s) to it. The states of the number of free wavelengths on all M a routes between node pair a are examined at the same time. The route with the largest number of free wavelengths is selected for connection setup. If no wavelength is available on any of the routes, the connection request is blocked. If two or more routes have the same largest number of free wavelengths, the route with the smallest label is selected. Once a connection is set up, the first-fit wavelength assignment scheme will be employed on


each segment on the selected route; that is, for each segment, the available wavelength with the smallest label will be assigned to all the links in that segment.

WMSL ALGORITHM FOR WAVELENGTH CONVERTER PLACEMENT Here we propose a heuristic algorithm for wavelength converter placement in an arbitrary mesh network that employs the LLR-FF RWA algorithm. The WMSL algorithm places the WCRs one by one. Each time we want to find the most important node from the candidate nodes such that if we put a WCR on this node, the average blocking probability can be reduced the most. However, it is difficult to construct an accurate and efficient analytical model for the LLR-FF RWA algorithm with sparse wavelength conversion. Our solution is to assign a weight value to each candidate node that can approximately represent the importance of the node. It has been shown that route length is the most important factor affecting blocking probability when there is no wavelength conversion [1]. WCRs can improve blocking performance mainly because the WCRs divide a route into several segments and thus mitigate the effect of the wavelength continuity constraint. Since the offered traffic to each node pair may be different, we also need to take into account the traffic offered to each route. Considering all the above factors, we propose the WMSL algorithm as follows: (2) (M a ) 1 Find the routes R(1) for each node pair a a , R a , …, R a according to the least loaded routing algorithm. 2 Assume that the traffic load offered to node pair a is ea and it is approximately evenly distributed to all the available (2) routes. Suppose Ra is any route in the set of R(1) a , R a , …, R a(Ma), and αR a is the offered traffic to route R a; then we have αRa =

1 ea . Ma

3 Calculate the weight value W(ν) for each candidate node ν. We define the weight function W(ν) as follows: W ( ν) =

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α Ra ( SRa All Ra that transit through node ν

− SRa ( ν)),

where SRa is the maximum segment length of Ra, and SRa (ν) is the maximum segment length of Ra after a WCR is placed on node ν. We place a WCR at the node with the maximum weight value calculated above. 4 If there are WCRs left, repeat step 3.

NUMERICAL RESULTS AND ANALYSIS OF THE WMSL ALGORITHM Extensive simulations have been carried out to investigate the performance of the proposed WMSL algorithm on the 14node NSFNET network. The simulation platform and parameters are the same as before. The performance of the proposed WMSL algorithm is compared to the cases of no wavelength and full wavelength conversion. We also compare it with the average performance of random converter placement and total outgoing traffic (TOT) converter placement algorithms. The TOT algorithm places WCRs at nodes that have the highest outgoing traffic. It has been shown to perform almost as well as optimal placement in networks that employ static routing and random wavelength assignment (RWA).



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In the 14-node NSFNET network, simulations are carried out using two or five WCRs. The cases of no conversion and full conversion are also investigated. Figure 5 shows the blocking probability for the NSFNET network. We find that wavelength conversion can improve blocking performance significantly at low traffic. With traffic increase, the benefit of wavelength conversion is less noticeable. This can be explained by the fact that when traffic is low, connection requests are blocked mainly because of the wavelength continuity constraint, so wavelength conversion improves performance significantly. However, when traffic is heavy, connection requests are blocked mainly because of lack of wavelength resources. In Fig. 5, we observe that the average performance of the random wavelength converter placement scheme is negligible. However, the TOT, MBPF, and WMSL algorithms can achieve much better performance. This validates the importance of the problem of wavelength converter placement. When there are two WCRs, WMSL and MBPF algorithms result in the same placement scheme, which has better performance than TOT. When there are five WCRs, we observe that WMSL outperforms TOT and MBPF. Another observation is that only five WCRs (about 35 percent of all the nodes) can achieve performance very close to that of full wavelength conversion. From the above simulation analysis, we conclude that sparse wavelength conversion improves blocking performance significantly in mesh networks if the WCRs are placed appropriately. The proposed WMSL algorithm can achieve better performance with the LLR-FF RWA algorithm.

C ONCLUSION In this article we present arguments that routing and wavelength assignment (RWA) has to take into consideration the underlying wavelength converter placement, and thus calls for reexamination of both RWA and wavelength converter placement. The argument is verified by two studies: • We show that the proposed weighted least congestion routing and first-fit wavelength assignment (WLCR-FF) algorithm outperforms all existing RWA algorithms (static, fixed-alternative, and least-loaded routing) in sparse or full wavelength conversion environments. • We show that the proposed weighted maximum segment length (WMSL) algorithm for a dynamic RWA algorithm can outperform all existing wavelength converter placement algorithms in terms of call blocking performance by a large margin.

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REFERENCES [1] I. Chlamtac, A. Ganz, and G. Karmi, “Lightpath Communications: An Approach to High Bandwidth Optical WANs,” IEEE Trans, Commun., vol. 40, no. 7, July 1992, pp. 1171–82. [2] M. Kovacevic and A. Acampora, “Benefits of Wavelength Translation in All-Optical Clear-Channel Networks,” IEEE JSAC, vol. 14, no. 5, June 1996, pp. 868–80. [3] B. Ramamurthy and B. Mukherjee, “Wavelength Conversion in WDM Networking,” IEEE JSAC, vol. 16, no. 7, Sept. 1998, pp. 1061–73. [4] S. Subramaniam, M. Azizoglu, and A. K. Somani, “All-Optical Networks with Sparse Wavelength Conversion,” IEEE/ACM Trans. Net., vol. 4, no. 4, Aug. 1996, pp. 544–57. [5] R. Ramaswami and K. Sivarajan, “Routing and Wavelength Assignment in All-Optical Networks,” IEEE/ACM Trans. Net., vol. 3, no. 5, Oct. 1995, pp. 489–500. [6] Y. Zhu, G. N. Rouskas, and H. G. Perros, “A Comparison of Allocation Policies in Wavelength Routing Networks,” Photonic Net. Commun. J., vol. 2, no. 3, Aug. 2000, pp. 265–93. [7] R. A. Barry and P. A. Humblet, “Models of Blocking Probability in AllOptical Networks with and without Wavelength Changers,” IEEE JSAC, vol. 14, no. 5, June 1996, pp. 858–67. [8] H. Harai, M. Murata, and H. Miyahara, “Performance of Alternate Routing Methods in All-Optical Switching Networks,” Proc. IEEE INFOCOM ’97, Apr. 1997, pp. 517–25. [9] K. Chan and T. P. Yum, “Analysis of Least Congested Path Routing in WDM Lightwave Networks,” Proc. IEEE INFOCOM ’94, 1994, pp. 962–69. [10] X.-W. Chu and B. Li, “A Dynamic RWA Algorithm in a WavelengthRouted All-Optical Network with Wavelength Converters,” Proc. IEEE INFOCOM ’03, Apr. 2003.

BIOGRAPHIES BO LI [S’89, M’92, SM’99] ([email protected]) received B.S. (summa cum laude) and M.S. degrees in computer science from Tsinghua University, Beijing, P. R. China, in 1987 and 1989, respectively, and a Ph.D. degree in computer engineering from the University of Massachusetts at Amherst, in 1993.

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Between 1994 and 1996 he worked on high-performance routers and ATM switches with IBM Networking System Division, Research Triangle Park, North Carolina. Since January 1996 he has been with the Computer Science Department, Hong Kong University of Science and Technology. He is also an adjunct researcher at Microsoft Research Asia. His current research interests are in mulimedia communications, wavelength-routed networks, resource management in wireless cellular systems, and service overlay networks. He has published over 60 papers in referreed journals. He has served as editor or guest editor for 14 journals, primarily for IEEE and ACM, and been involved in the organization of over 30 conferences. He is currently serving as the Technical Program Committee Co-Chair for IEEE INFOCOM ’04. X IAWEN C HU [StM] ([email protected]) received a B.S. degree (summa cum laude) in computer science from Tsinghua University, Beijing, China, in 1999. He is currently working on his Ph.D. degree in computer science at Hong Kong University of Science and Technology. His research interests include WDM networks and high-speed routing and switching. KAZEM SOHRABY ([email protected]) is a professer and head of the Computer Science and Computer Engineering Department, College of Engineering, University of Arkansas in Fayetteville. Prior to that he was with Bell Laboratories, Holmdel, New Jersey. His areas of interest include computer networking, signaling, switching, performance analysis, and traffic theory. He has over 20 applications and granted patents on computer protocols, wireless and optical systems, circuit and packet switching, and optical Internet. He has several publications, including a book on the performance and control of computer communications networks. He is a Distinguished Lecturer of IEEE Communications Society, and serves as its President’s representative on the Committee on Communications and Information Policy. He served on the Education Committee of ComSoc, is on the editorial boards of several publications, and has served as reviewer and panelist with the National Science Foundation, the U.S. Army, and the Natural Sciences and Engineering Research Council of Canada. He received B.S., M.S., and Ph.D. degrees in electrical engineering, and an M.B.A., from the Wharton School, University of Pennsylvania.
