Jean‐François Labourdette

Lightpath re-optimization in mesh optical networks

Intelligent mesh optical networks deployed today offer unparalleled capacity, flexibility, availability, and, inevitably, new challenges to master all these qualities in the most efficient and practical manner. More specifically, demands are routed according to the state of the network available at the moment. As the network and the traffic evolve, the lightpaths of the existing demands becomes sub-optimal. In this paper we study two algorithms to re-optimize lightpaths in resilient mesh optical networks. One is a complete re-optimization algorithm that re-routes both primary and backup paths, and the second is a partial re-optimization algorithm that re-routes the backup paths only. We show that on average, these algorithms allow bandwidth savings of 3% to 5% of the total capacity in scenarios where the backup path only is re-routed, and substantially larger bandwidth savings when both the working and backup paths are re-routed. We also prove that trying all possible demand permutations with an online algorithm does not guarantee optimality, and in certain cases does not achieve it, while for the same scenario optimality is achieved through re-optimization. This observation motivates the needs for a re-optimization approach that does not just simply look at different sequences, and we propose and experiment with such an approach. Re-optimization has actually been performed in a nationwide live optical mesh network and the resulting savings are reported in this paper, validating reality and the usefulness of re-optimization in real networks.

Stochastic approaches to compute shared mesh restored lightpaths in optical network architectures

We assess the benefits of using statistical techniques to ascertain the shareability of protection channels when computing shared mesh restored lightpaths. Current deterministic approaches require a detailed level of information proportional to the number of active lightpaths, and do not scale well as traffic demands and network grow. With the proposed approach, we show that less information, independent of the amount of traffic demand, is sufficient to determine the shareability of protection channels with remarkable accuracy. Experiments also demonstrate that our approach yields faster computation times with no significant penalty in terms of capacity usage.

Path Routing in Mesh Optical Networks

List of Figures. List of Tables. Foreword. Preface. 1 Optical Networking. 1.1 Evolution of Optical Network Architectures. 1.1.1 Transparent Networks. 1.1.2 Opaque Networks. 1.1.3 Translucent Networks. 1.2 Layered Network Architecture. 1.2.1 Optical Layer. 1.2.2 Logical Layer. 1.2.3 Service/Application Layer. 1.3 Multi-Tier Optical Layer. 1.3.1 One-Tier Network Architecture. 1.3.2 Two-Tier Network Architecture. 1.3.3 Network Scalability. 1.4 The Current State of Optical Networks. 1.5 Organization of the Book. 2 Recovery in Optical Networks. 2.1 Introduction. 2.2 Failure Recovery. 2.3 Fault Recovery Classifications. 2.4 Protection of Point-to-Point Systems. 2.4.1 (1 + 1) Protection. 2.4.2 (1 : 1) Protection. 2.4.3 (M :N) Protection. 2.5 Ring-Based Protection. 2.5.1 Failure Recovery in SONET Networks with Ring Topologies. 2.5.2 Ring-Based Failure Recovery in Optical Networks with Mesh Topologies. 2.6 Path-Based Protection. 2.6.1 Dedicated Backup Path Protection (DBPP) in Mesh Networks. 2.6.2 Shared Back Path Protection (SBPP) in Mesh Networks. 2.7 Link/Span-Based Protection. 2.8 Segment-Based Protection. 2.9 Island-Based Protection. 2.10 Mesh Network Restoration. 2.10.1 Centralized Restoration Techniques. 2.10.2 Distributed Restoration Techniques. 2.11 Multi-Layer Recovery. 2.12 Recovery Triggers and Signaling Mechanisms. 2.13 Conclusion. 3 Mesh Routing and Recovery Framework. 3.1 Introduction. 3.2 Mesh Protection and Recovery Techniques. 3.2.1 Link-Based Protection. 3.2.2 Path-Based Protection. 3.2.3 Segment-Based Protection. 3.3 Concept of Shared Risk Groups. 3.3.1 Shared Link Risk Groups. 3.3.2 Shared Node Risk Groups. 3.3.3 Shared Equipment Risk Groups. 3.4 Centralized vs Distributed Routing. 3.4.1 Centralized Routing. 3.4.2 Distributed Routing. 3.4.3 Centralized vs Distributed Routing Performance Results. 3.5 Conclusion. 4 Path Routing and Protection. 4.1 Introduction. 4.2 Routing in Path-Protected Mesh Networks. 4.3 Protection in Path-Protected Mesh Networks. 4.3.1 Dedicated Backup Path-Protected Lightpaths. 4.3.2 Shared Backup Path-Protected Lightpaths. 4.3.3 Preemptible Lightpaths. 4.3.4 Diverse Unprotected Lightpaths with Dual-Homing. 4.3.5 Multiple Simultaneous Backup Path-Protected Lightpaths. 4.3.6 Relaxing the Protection Guarantees. 4.3.7 Impact of Multi-Port Card Diversity Constraints. 4.4 Experiments and Capacity Performance Results. 4.4.1 Performance Results for Path-Based Protection Techniques. 4.4.2 Experiments with Multi-Port Card Diversity. 4.5 Recovery Time Analysis. 4.6 Recovery Time and Capacity Trade-Offs. 4.7 Conclusion. 5 Path Routing - Part 1: Complexity. 5.1 Introduction. 5.2 Network Topology Abstraction. 5.2.1 Service Definition. 5.2.2 Operational Models: Online vs Offline Routing. 5.3 Shortest-Path Routing. 5.3.1 Dijkstras Algorithm. 5.3.2 Dijkstras Algorithm Generalization to K-Shortest Paths. 5.3.3 Shortest-Path Routing with Constraints. 5.4 Diverse-Path Routing. 5.4.1 SRG Types. 5.4.2 Diverse-Path Routing with Default SRGs. 5.4.3 Diverse-Path Routing with Fork SRGs. 5.4.4 Diverse-Path Routing with General SRGs. 5.5 Shared Backup Path Protection Routing. 5.5.1 Protection Guarantees and Rules of Sharing. 5.5.2 Complexity of Shared Backup Path Protection Routing. 5.6 Routing ILP. 5.6.1 ILP Description. 5.6.2 Implementation Experience. 5.7 Conclusion. 5.8 Appendix. 5.8.1 Complexity of Diverse-Path Routing with General SRGs. 5.8.2 Complexity of SBPP Routing. 6 Path Routing - Part 2: Heuristics. 6.1 Introduction. 6.1.1 Operational Models: Centralized vs Distributed Routing. 6.1.2 Topology Modeling Example. 6.2 Motivating Problems. 6.2.1 Heuristic Techniques. 6.3 K-Shortest Path Routing. 6.3.1 Yens K-Shortest Path Algorithm. 6.3.2 Constrained Shortest-Path Routing. 6.4 Diverse-Path Routing. 6.4.1 Best-Effort Path Diversity. 6.5 Shared Backup Path Protection Routing. 6.5.1 Sharing-Independent Routing Heuristic. 6.5.2 Sharing-Dependent Routing Heuristic. 6.6 Routing Preemptible Services. 6.7 General Constrained Routing Framework. 6.7.1 Implementation Experience. 6.8 Conclusion. 7 Enhanced Routing Model for SBPP Services. 7.1 Introduction. 7.2 Routing Metric. 7.3 Routing Algorithm. 7.4 Experiments. 7.4.1 Effect of . 7.4.2 Effect of alpha. 7.5 Conclusion. 8 Controlling Sharing for SBPP Services. 8.1 Introduction. 8.2 Express Links. 8.2.1 Routing with Express Links. 8.2.2 Analysis and Results. 8.2.3 Express Links-Conclusion. 8.3 Limiting Sharing. 8.3.1 Example. 8.3.2 Solution Alternatives. 8.3.3 Analysis of Capping. 8.3.4 Analysis of Load-Balancing. 8.3.5 Limiting Sharing-Conclusion. 8.4 Analysis of Active Reprovisioning. 8.4.1 Evaluation of Active Reprovisioning. 8.4.2 Active Reprovisioning-Conclusion. 8.5 Conclusion. 9 Path Computation with Partial Information. 9.1 Introduction. 9.2 Complexity of the Deterministic Approach. 9.2.1 Complexity of the Failure Dependent Strategy. 9.2.2 Complexity of the Failure Independent Strategy. 9.3 Probabilistic Approach. 9.3.1 A Problem of Combinations. 9.3.2 Analogy with SRG Arrangement into a Set of Backup Channels. 9.4 Probabilistic Routing Algorithm with Partial Information. 9.5 Locally Optimized Channel Selection. 9.5.1 Shared Mesh Protection Provisioning Using Vertex Coloring. 9.5.2 Implementation and Applications. 9.6 Required Extensions to Routing Protocols. 9.7 Experiments and Performance Results. 9.7.1 Accuracy and Distributions of Probability Functions. 9.7.2 Comparison of Deterministic vs ProbabilisticWeight Functions on Real Networks. 9.7.3 Benefits of Locally Optimized Lightpath Provisioning. 9.7.4 Summary. 9.8 Conclusion. 10 Path Reoptimization. 10.1 Introduction. 10.2 Routing Algorithm. 10.2.1 Cost model. 10.2.2 Online Routing Algorithm. 10.3 Reoptimization Algorithm. 10.4 The Complexity of Reoptimization. 10.4.1 No Prior Placement of Protection Channels or Primary Paths. 10.4.2 Prior Placement of Protection Channels or Primary Paths. 10.5 Experiments. 10.5.1 Calibration. 10.5.2 Real Networks. 10.5.3 Static Network Infrastructure. 10.5.4 Growing Network Infrastructure. 10.5.5 Network Dynamics. 10.6 Conclusion. 11 Dimensioning of Path-Protected Mesh Networks. 11.1 Introduction. 11.2 Network and Traffic Modeling. 11.3 Mesh Network Characteristics. 11.3.1 Path Length Analysis. 11.3.2 Protection-to-Working Capacity Ratio Analysis. 11.3.3 Sharing Analysis. 11.4 Asymptotic Behavior of the Protection-to-Working Capacity Ratio. 11.4.1 Examples. 11.4.2 General Results. 11.5 Dimensioning Mesh Optical Networks. 11.5.1 Node Model and Traffic Conservation Equations. 11.5.2 Dimensioning Examples and Results. 11.6 The Network Global Expectation Model. 11.7 Accuracy of Analytical Estimates. 11.8 Recovery Time Performance. 11.9 Conclusion. 12 Service Availability in Path-Protected Mesh Networks. 12.1 Introduction. 12.2 Network Service Availability. 12.2.1 Motivation. 12.2.2 Focus on Dual-Failure Scenarios. 12.2.3 Reliability and Availability. 12.3 Service Availability in Path-Protected Mesh Networks. 12.3.1 Dual-Failure Recoverability. 12.3.2 A Markov Model Approach to Service Availability. 12.3.3 Modeling Sharing of Backup Channels. 12.3.4 Impact of Channel Protection. 12.3.5 Impact of Reprovisioning. 12.4 Availability in Single and Multiple Domains. 12.4.1 Network Recovery Architecture-Single Domain. 12.4.2 Network Recovery Architecture-Multiple Domains. 12.4.3 Results and Discussion. 12.4.4 A Simple Model. 12.5 Availability in Ring and Path-Protected Networks. 12.5.1 Ring Availability Analysis. 12.5.2 Results and Discussion. 12.5.3 The Simple Model Again. 12.6 Conclusion. Bibliography. Index.

Enhanced algorithm cost model to control tradeoffs in provisioning shared mesh restored lightpaths

In this write-up we propose an algorithm-centered metric to vary the weight put on the solutions cost and on the average backup lengths while selecting a primary-backup pair from a set of candidate routes. We assess the effect of our metric on these two contradicting objectives and show that it offers the leverage to achieve the desired compromise. We first present the cost model, we then describe the algorithm used in our experiments to illustrate the effect of this cost model, and we finally conclude with the results of our experiments.

Fast approximate dimensioning and performance analysis of mesh optical networks

This paper presents a collection of approximation formulas that allow a network planner to quickly estimate the size of a mesh optical network with limited inputs. In particular, it provides a set of equations that relate number of sites, average fiber connectivity, demand load and capacity for various mesh protection architectures. These results can be used to easily and quickly estimate the amount of traffic that can be carried over a given network, or, conversely, given the traffic to be supported, to assess the characteristics of the topology required (in terms of number of nodes, connectivity). Finally, this analysis can be used to estimate the restoration performance that can be expected without resorting to extensive simulation studies.

Invited: Routing Strategies for Capacity-Efficient and Fast-Restorable Mesh Optical Networks

Wavelength division multiplexed (WDM)-based mesh network infrastructures that route optical connections using intelligent optical cross-connects (OXCs) are emerging as the technology of choice to implement the next generation core optical networks. In these architectures a single OXC is capable of switching tens of terabits of traffic per second. With such data transfer rates at stake, it becomes increasingly challenging for carriers to (1) efficiently and cost-effectively operate and manage their infrastructure, and (2) cope with network failures while guaranteeing prescribed service level agreements (SLAs) to their customers. Proper routing of primary and backup paths is a critical component of the routing and restoration architecture required to meeting these challenges. In this paper we review some of the various strategies and approaches proposed so far to intelligently route connections while at the same time providing guaranteed protection against various types of network failures. We explore the tradeoffs associated with these approaches, and investigate in particular different, sometimes competing aspects, such as cost/capacity required, level of protection (link vs. node failure), restoration time, and complexity of route computation.

Role of optical network and spare router strategy in resilient IP backbone architecture

At the heart of IP backbone networks are the core IP routers with throughput of hundreds of Gb/s. These routers with interfaces operating at the per-wavelength bit rates are directly connected via point-to-point WDM optical-transport systems. For acceptable service reliability even for best effort services typically two interconnected routers are used for redundancy in each backbone node. It has been established that the majority of the traffic in a node is transit traffic and a significant cost reduction can be achieved by siphoning off the transit traffic from the IP layer into the optical layer. In this paper we discuss the current trend in the IP backbone network which is poised to take over other premium services, in addition to best effort IP services, as an integrated transport platform. We discuss several network architecture options with the critical attribute being that it must be as resilient as the current SONET transport network. We propose an innovative architecture option in which a resilient network is built with current router technology. In another option we assume that the router layer can be as resilient as the current SONET layer with the emerging resilient router technology. We perform an economic evaluation and discuss the reliability of these network architectures.

Distributed computation of shared backup path in mesh optical networks using probabilistic methods

We assess the benefits of using statistical techniques to ascertain the shareability of protection channels when computing shared-mesh restored lightpaths in optical mesh networks. These optical networks support wavelength conversion everywhere as a byproduct of the electronic nature of the switching in the optical-electronic-optical optical cross connect used. Current deterministic approaches require a detailed level of information proportional to the number of active lightpaths. Although this is not an issue for good sized networks in the foreseeable future, these approaches are not practicable for distributed route computation involving larger networks. On the other hand, distributed approaches that do not make use of shareability information require a significant amount of additional capacity compared to a centralized approach with access to complete shareability information. With the proposed approach we show that even with less information, independent of the amount of traffic demand, it is possible to predict the shareability of protection channels with remarkable accuracy. In addition, we propose a local distributed channel assignment scheme that is used in conjunction with our distributed route computation proposal to assign shared channels when provisioning the backup path. This channel assignment scheme can also be used to further optimize capacity usage in individual links upon certain events or at regular intervals. Experiments are provided that demonstrate that our approach yields faster computation times with no significant penalty in terms of capacity usage than a centralized approach using complete information.

Pre-emptive reprovisioning in mesh optical networks

Pre-emptive reprovisioning is a method to perform reprovisioning of a backup path in advance of a second failure, to reduce the time to recover service from seconds (reprovisioning) to milliseconds (restoration). We evaluate the tradeoff between benefits and operational complexity.

Distributed route computation and provisioning in shared mesh optical networks

Optical mesh network infrastructure has emerged as the technology of choice for next-generation transport networks. At the same time, distributed, IP-based, control architecture has been proposed for intelligent optical networks, as a means to automate operations, enhance interoperability, and facilitate the deployment of new applications. While distributed control in general enhances scalability and flexibility, it has also been observed that the requisite network topology and link-state information summarization may result in suboptimal path computation, especially for shared mesh restored paths. This paper presents a distributed control plane for optical mesh networks, focusing on distributed path computation and provisioning mechanisms. It discusses the tradeoffs between the path computation efficiency for shared mesh restored paths and the amount of network topology and link-state information that is disseminated via routing protocols. We show that with appropriately aggregated link resource availability and sharing information, the proposed distributed path computation algorithms are able to determine the shareability of restoration links with remarkable accuracy. A local channel assignment scheme, which is used in conjunction with the distributed path computation algorithms to assign shared channels when provisioning restoration paths, is also proposed. The additional information that signaling messages are required to carry in order to perform the local channel assignment at each node along the restoration path is discussed. Furthermore, we specify the extensions to the open shortest-path first (OSPF) routing protocol in support of shared mesh restoration. We analyze the performance of the proposed distributed path computation algorithms and the local channel assignment scheme, as well as the overhead of OSPF extensions. In particular, we study the tradeoffs between network capacity utilization, restoration path computation complexity, OSPF extension overhead, and memory requirements for storing the modified link-state database.