This paper presents new constructions of multistage wave-mixing networks with arbitrary b/spl times/b space-switching elements, where b /spl ges/ 2. In these networks, for a size of F fiber links and W wavelengths per link, converter requirements are O(Flog/sub b/W) or O(FW/b) for rearrangeable nodes, and O(Flog/sub b/Wlog/sub b/(FW)) or O(FWlog/sub b/(FW)/b) for different types of strictly nonblocking nodes inspired from the Cantor topology. In all cases, the worst case number of cascaded conversion is O(log/sub b/W). When b=W /spl les/ F, the required number of converters, and the worst case number of cascaded conversions are respectively O(F) and O(1), and are both optimal up to a constant. The new networks generalize and improve upon previous wave-mixing networks based on 2/spl times/2 space switches.

The LDP (label distribution protocol) is used in the control plane to control an optical network. The data plane and the control plane of an optical network could be physically separate. So a failure in the control plane does not necessarily imply a data plane failure and that user communications have to be interrupted. The standard LDP, however, does not provide any mechanism to recover the knowledge stored in LDP entities about the status of the data plane after the faults are fixed. This is a reliability problem of LDP and results in the unnecessary degradation of user communications. On the other hand, in MPLS-enabled IP networks, being able to recover LDP sessions would be potentially faster and more scalable than to re-establish all affected LSPs. The proposed recovery method of LDP for the control plane failures uses label information mirrors (LIMs) in upstream downstream label switching routers (LSRs). Each LIM is a copy of the label information database (LID) in the LSR of an LDP session. We propose a systematic approach to synchronize the contents of a LIM and the corresponding LID, and show how a LIM is used to handle a control plane failure. Detailed descriptions of the recovery procedure for both control channel failures and control node failures are presented. Some significant features of the proposal are outlined.

We propose competitive policies for admission control and routing in general topology networks, with bandwidth and buffer resources. The goal of these solutions is to maximize the network revenue, when the traffic demand is not known ahead of time, and over-allocation of network resources is allowed with some probability. On top of appropriate resource reservation setup protocols, these solutions provide statistical service while optimizing the profit of the network operator.

We consider the problem of obtaining non-trivial lower bounds on the the lost revenue under any routing and admission control scheme in a multi-class loss network. First, we use the following simple idea to bound the performance of any coordinate-convex admission policy on a single link: the blocking probability of any call class is lower bounded by considering just this class in isolation and replacing the available bandwidth (a random quantity) by its mean. Then, following the methods of Kelly (1994) and Gibbens and Kelly (see IEEE Journal on Selected Areas in Communications, p.100-10, 1995), we use this single link bound to obtain linear programs which give bounds in the case of sparsely-connected networks with multiple bandwidth classes and alternate routing.

We consider single-hop wavelength-division multiplexed networks in which the transmitters take a nonzero amount of time, called tuning latency, to tune from one wavelength to another. For such networks, we show that, under certain conditions on the traffic matrix, there exist polynomial-time algorithms that produce the optimal schedule. Further, the tuning latency is masked in the length of the optimal schedule. Using Chernoff-Hoeffding bounds, we show that the condition on the traffic matrix is satisfied with high probability when the wavelength reuse factor is large, i.e., the number of nodes is large compared to the number of wavelengths. Simulation results show the dramatic improvement in the performance of the network using our algorithm as compared with other heuristics.