Course Wrap-up & Challenges for Future Optical Networking

1. Course Wrap-up & Challenges for Future Optical Networking Matthieu Clouqueur TRLabs&University of Alberta

2. E E 681 Course Wrap-up • Phase 1: • What is a transport network? • Concepts of reliability, availability, general methods for availability analysis • Reliability is a mission-oriented question for non-repairable systems. In transport networks we are interested in availability of service (in particular availability of end-to-end service paths) • The method used them most to evaluate a system’s availability is the cut-sets method client transport

3. P T L D M L I W D T R T C L E V T O L D N W R K P H L A B L T K S C H S T N W O R E E 681 Course Wrap-up • Simple survivable transport network architecture: Automatic Protection Switching • Optimization for Network Planning • Design of capacity efficient transport network will require optimization techniques • The general technique that will be used is “Mathematical Programming” which formulates the problem as: Minimize (or Maximize): {Objective Function} Subject to: constraint 1 constraint 2 constraint 3... Primary path Backup path

4. E E 681 Course Wrap-up • Graph Theory: • Vocabulary of general Graph Theory and vocabulary for transport network design • Routing in transport networks: • Dijkstra’s algorithm finds the shortest path between any pair of nodes in a weighted graph. • The k-shortest path algorithm (ksp) finds a set of simultaneously feasible paths between two nodes. It is a good model for mesh span-restoration. • The min-cut max-flow theorem states that “The maximum flow that is feasible between two nodes is equal to the minimum capacity of any cut in the facility graph between these two nodes” • The ksp algorithm does not necessarily achieve max-flow because of a special graph configuration called the “trap topology” • The max-flow algorithm avoids the trap topology by allowing a change in routing decision thanks to “reverse arcs” • Optimal routing can also be obtained by solving mathematical programming formulation for the max flow problem

5. E E 681 Course Wrap-up • Computational complexity: • Solving large optimization problems can become very slow (if at all possible) using Mathematical Programming • An alternative is to accept that the solution be sub-optimal (but not too far from optimality) if it can be found quickly: Such methods are called Heuristics • Meta-heuristics are heuristics that apply to many optimization problems and will be an option for solving complex network design problems: • Simulated Annealing • Genetic Algorithms • Tabu Search

6. E E 681 Course Wrap-up • Phase 2: • Rings: a very common survivable transport network architecture • Unidirectional path-switch rings (UPSR) • Bi-directional line-switched rings • Rings are a simple survivable architecture with fast restoration • It is a protection mechanism (restoration paths are pre-planned) • Rings require at least 100% spare/working redundancy

7. E E 681 Course Wrap-up • Aspects of ring design: • Ring sizing: For a given set of demands to be served by a ring, what is the minimum ring size that is required? • Ring loading: For a given ring (with known capacity), what is the maximum number of demands (or demand volume) that can be served? • More generally, the ring design problem is: • For a set of end-to-end demands what is the lowest cost ring design that can serve all demands? • This involves: Cycle generation, ring selection, determination of glass-through locations, demand routing • Some measures of ring design quality are: Demand capture, Capacity efficiency (the cost of a design is a combination of these two aspects that depends on whether we are in the access or in the long-haul) • Ring design is a very complex problem that can be formulated with mathematical programming but in practice it requires the use of heuristics (Tabu Search for example)

8. E E 681 Course Wrap-up • Special aspect of ring-design: Dual ring interconnect arrangements (DRI) • The intent is to eliminate single points of failures in end-to-end service paths served in ring-based networks • There is two ways to transit from one ring to another (two gateways) • Two types of arrangements are possible: “drop-and-continue” (also called “matched nodes”), dual feeding. • The general drawbacks of rings are: • Very capacity inefficient • Difficult to design • Not flexible in terms of adding new service paths to an existing design (problem of scalability): Establishing a new service path may require the addition of a whole new ring with very low utilization.

9. E E 681 Course Wrap-up • Mesh Networks: • They allow routing of demands on shortest path (simple and generally more capacity efficient) • They allow spare capacity sharing between all spans of the network (instead of between spans of the same ring) • Restoration of failures can be done either between the end-nodes of the failed span (span restoration) or between the origin and destination nodes of all affected service paths (path restoration) • Design of mesh networks: • Several formulations exist to do the capacity design of a mesh network: • The Herzberg Method is the basic approach for spare capacity design of a span-restorable network • Variations of the Herzberg methods include: Modularity and/or joint working and spare capacity placement for span-restoration and path-restoration. • General issues with capacity design methods derived from Herzberg’s formulation are: Size of the working- and restoration-route sets. A tradeoff has to be found between optimality of the solution and optimization run time. • Important observation: The capacity efficiency of mesh networks increases as the nodal degree increases

10. E E 681 Course Wrap-up • Other aspects of mesh networks: • Restoration paths do not need to be pre-computed, they can be found in a distributed manner upon failure (greater adaptability to changes and no need to maintain databases of network state) • The Self-healing protocol allows restoration paths to be found in a distributed manner by application of simple rules at each node based only on knowledge of the states of adjacent links. • The mesh architecture is more flexible in terms of adding new service paths (adding capacity can be done on a per span basis) • Later in the course we discovered that: • Mesh networks have the potential (with reasonable cost increase) for serving demands with various availability requirements (multiple service classes) ranging between un-protected to full restorable to any dual span-failure. • Functionally ring-based networks cannot guarantee full restorability to dual span-failures.

11. E E 681 Course Wrap-up • Ring or Mesh: which architecture to choose? • The decision factors (based on cost): • Access: Rings seem to remain the solution of choice for access • Metro: Ring or mesh • Long-haul: With long distances, capacity efficiency becomes more and more important and mesh networks become the preferable solution unless… … the network looks like this: Average nodal degree d = 2.3 The nodal degree is not high enough for mesh to be efficient (see study by John Doucette looking at the effects of varying graph connectivity)

12. E E 681 Course Wrap-up • Ring or Mesh: Do we need to choose? • Not necessarily: For example, Ring-Mesh Hybrid Networks • A ring-mesh hybrid design can contain well loaded rings (clipping off the forcers of the mesh network) and an efficient resulting mesh Due to the ring cost factor (less than 1) the total network can be cheaper than the original network

13. E E 681 Course Wrap-up • Ring or Mesh: Do we need to choose? • p-cycles: Let’s keep the best of both! • A slight modification of the ring protection principle (p-cycles will protect on-cycle spans as well as straddling spans) can improve greatly the capacity efficiency of rings to bring it close to mesh efficiency and retains the restoration speed.

14. E E 681 Course Wrap-up • So many ways to make a network “restorable” but how much does that improve the availability of service? • By “Restorable” we usually mean restorable to any single span-failure. We therefore need to investigate multiple span or span-node failure combinations to determine how much the availability of service has been improved. • Analysis of multiple failures in ring-based networks leads to closed-form models for the service path availability. • Analysis of multiple failures in mesh restorable networks requires a case-by case analysis of each multiple failure. • In both cases, guaranteeing restorability to single span-failures brings a huge improvement to the availability of service paths (a numerical example for span-restoration showed a reduction from 13 hours/year down to a 2.4 min/year) • What about service paths with very high availability requirements? • Current research on service availability is showing that mesh networks (unlike ring-based networks) can naturally protect 20-30% of very high availability (dual-failure restorable) demands for minimal to no cost increase.

15. E E 681 Course Wrap-up • Serving multiple classes of service • The class of service that a given service path requires can be characterized by two factors: Restoration speed required, average availability required. Average availability Banking transactions Tele-surgery ? Span-restorable mesh Voice (telephone) Live Video E-mail Ftp Unprotected traffic Rings, 1+1 APS Restoration speed required

16. E E 681 Course Wrap-up • Other current research issue: Robustness of transport networks to demand uncertainty • A network operator does not have exact knowledge of what the demand will be in 6 months, 1 year, 2 years, … • Based on an estimate of what the demand will be at some point in the future, we want to know what probability we have of being able to serve the actual demand at that time or what percentage of the actual demand we will be able to serve. • The different restoration mechanisms may be more or less robust to demand uncertainty. • The way capacity provisioning is done could be adapted to maximize the robustness of the network to future demands.

17. E E 681 Transport Networking in the Future • Future (data) optical transport networks (OTN) • Optical: • New optical equipment is capable of wavelength switching (Optical Cross-Connects, OXC) or adding/dropping (Optical Add/Drop Multiplexers, OADM) • From a simple point-to-point system, the optical layer will become an intelligent optical transport network (OTN) capable of enhanced optical layer management and distributed network intelligence • Data: • With increasing proportion of data traffic, capacity efficiency is not possible anymore if bandwidth is dedicated • Networks have to be data based. • However these networks need to be able to provide “STM-like” service

18. E E 681 Transport Networking in the Future Challenges of future Optical Transport Networks*: • Service transparency • Optical transport networks need to be able to provide service for different types of clients: SONET, ATM, IP,… • Enhanced optical layer management • Network operators will have to provide reliable service to the customers thanks to improved signal quality monitoring in the optical network • Real-time optical channel provisioning • Ability to establish service paths quickly • Optical layer restoration with performance guarantees • Each optical channel must have restorability parameters (protected channels, un-protected channels, restoration time required…) Other issue: • Multi-layer restoration will become a very important issue • for example fast restoration can be provided at the optical layer for certain connections and the above layers like the IP layer can do the rest) *A. Rodriguez-Moral et al., “Optical data networking: protocol, technologies, and architectures for next generation optical transport networks and optical internetworks,” Journal of Lightwave Technology, vol. 18, no. 12, December 2000.

19. E E 681 Transport Networking in the Future • How to do data transport networking? • IP is a packet protocol but with no traffic engineering (no guarantees on service availability, restoration time, average delay,…) • A promising solution to data transport networking: GMPLS • Based on MPLS, a packet switching protocol that provides traffic engineering • GMPLS provides management of the data plane (as MPLS) and also of other types of traffic: TDM traffic, lambda-switched traffic and fiber-switched traffic. • GMPLS provides: • Resource discovery • Routing control • Connection Management