In network design, the gap between theory and practice is woefully broad. This book narrows it, comprehensively and critically examining current network design models and methods. You will learn where mathematical modeling and algorithmic optimization have been under-utilized. At the opposite extreme, you will learn where they tend to fail to contribute to the twin goals of network efficiency and cost-savings. Most of all, you will learn precisely how to tailor theoretical models to make them as useful as possible in practice. Throughout, the authors focus on the traffic demands encountered in the real world of network design. Their generic approach, however, allows problem formulations and solutions to be applied across the board to virtually any type of backbone communication or computer network. For beginners, this book is an excellent introduction. For seasoned professionals, it provides immediate solutions and a strong foundation for further advances in the use of mathematical modeling for network design. (Less)

Routing, Flow, And Capacity Design In Communication And Computer Networks

In a wavelength-division-multiplexing (WDM) optical network, the failure of network elements (e.g., fiber links and cross connects) may cause the failure of several optical channels, thereby leading to large data losses. This study examines different approaches to protect a mesh-based WDM optical network from such failures. These approaches are based on two survivability paradigms: 1) path protection/restoration and 2) link protection/restoration. The study examines the wavelength capacity requirements, and routing and wavelength assignment of primary and backup paths for path and link protection and proposes distributed protocols for path and link restoration. The study also examines the protection-switching time and the restoration time for each of these schemes, and the susceptibility of these schemes to multiple link failures. The numerical results obtained for a representative network topology with random traffic demands demonstrate that there is a tradeoff between the capacity utilization and the susceptibility to multiple link failures. We find that, on one hand, path protection provides significant capacity savings over link protection, and shared protection provides significant savings over dedicated protection; while on the other hand, path protection is more susceptible to multiple link failures than link protection, and shared protection is more susceptible to multiple link failures than dedicated protection. We formulate a model of protection-switching times for the different protection schemes based on a fully distributed control network. We propose distributed control protocols for path and link restoration. Numerical results obtained by simulating these protocols indicate that, for a representative network topology, path restoration has a better restoration efficiency than link restoration, and link restoration has a faster restoration time compared with path restoration.

/pdf/survivable-wdm-mesh-networks-2eyxkmnccu.pdf

Survivable WDM mesh networks

This investigation considers optical networks which employ wavelength cross-connects that enable the establishment of wavelength-division-multiplexed (WDM) channels, between node-pairs. In such and other networks, the failure of a network element (e.g., fiber link, cross-connect, etc.) may cause the failure of several optical channels, thereby leading to large data losses. This study examines different approaches to protect mesh based WDM optical networks from single-link failures. These approaches are based on two basic survivability paradigms: (a) path protection/restoration, and (b) link protection/restoration. In path- and link-protection schemes, backup paths and wavelengths are reserved in advance at the time of call setup. Path- and link-restoration schemes are dynamic schemes in which backup paths are discovered (from the spare capacity in the network) upon the occurrence of a failure. In part 1 of this study presented in this paper, we formulated integer linear programs to determine the capacity requirements for the above protection schemes for a static traffic demand.

Survivable WDM mesh networks. Part I-Protection

Cycle-oriented preconfiguration of spare capacity is a new idea for the design and operation of mesh-restorable networks. It offers a sought-after goal: to retain the capacity-efficiency of a mesh-restorable network, while approaching the speed of line-switched self-healing rings. We show that through a strategy of pre-failure cross-connection between the spare links of a mesh network, it is possible to achieve 100% restoration with little, if any, additional spare capacity than in a mesh network. In addition, we find that this strategy requires the operation of only two cross-connections per restoration path. Although spares are connected into cycles, the method is different than self-healing rings because each preconfigured cycle contributes to the restoration of more failure scenarios than can a ring. Additionally, two restoration paths may be obtained from each pre-formed cycle, whereas a ring only yields one restoration path for each failure it addresses. We give an optimal design formulation and results for preconfiguration of spare capacity and describe a distributed self-organizing protocol through which a network can continually approximate the optimal preconfiguration state.

/pdf/cycle-oriented-distributed-preconfiguration-ring-like-speed-so4ult7a2w.pdf

Cycle-oriented distributed preconfiguration: ring-like speed with mesh-like capacity for self-planning network restoration

The invention provides improved methods and apparatus for control using field and control devices that provide a virtual machine environment and that communicate via an IP network. By way of non-limiting example, such field device can be an “intelligent” transmitter or actuator that includes a low power processor, along with a random access memory, a read-only memory, FlashRAM, and a sensor interface. The processor can execute a real-time operating system, as well as a Java virtual machine (JVM). Java byte code executes in the JVM to configure the field device to perform typical process control functions, e.g., for proportional integral derivative (PID) control and signal conditioning. Control networks can include a plurality of such field and control devices interconnected by an IP network, such as an Ethernet.

Methods and apparatus for control using control devices that provide a virtual machine environment and that communicate via an ip network

The total transmission capacity required by a transport network to satisfy demand and protect it from failures contributes significantly to its cost, especially in long-haul networks. Previously, the spare capacity of a network with a given set of working span sizes has been optimized to facilitate span restoration. Path restorable networks can, however, be even more efficient by defining the restoration problem from an end to end rerouting viewpoint. We provide a method for capacity optimization of path restorable networks which is applicable to both synchronous transfer mode (STM) and asynchronous transfer mode (ATM) virtual path (VP)-based restoration. Lower bounds on spare capacity requirements in span and path restorable networks are first compared, followed by an integer program formulation based on flow constraints which solves the spare and/or working capacity placement problem in either span or path restorable networks. The benefits of path and span restoration, and of jointly optimizing working path routing and spare capacity placement, are then analyzed.

Optimal capacity placement for path restoration in STM or ATM mesh-survivable networks

Distributed path restoration based on optical cross-connects can provide highly capacity-efficient real-time restoration for WDM-based optical networking. However, to obtain an assured restoration level with the theoretically very low amounts of spare capacity that path restoration allows, one must solve, or closely approximate a solution to, the integer multicommodity maximum flow (MCMF) problem, MCMF is, however a hard combinatorial optimization problem due to what is called the "mutual capacity" aspects of the problem: which of many competing origin-destination pairs should be allowed paths over the finite spares on each span? Integer MCMF is further complicated by the nonunimodular nature of the problem, i.e., fractional flows are forbidden but would arise if solved by linear programming. This paper presents a heuristic principle that tests well against integer programming solutions of MCMF routing. The heuristic is first characterized in a centralized program, then adapted for use in a distributed path restoration protocol. In all test cases, the protocol obtains over 97% of the paths found in an optimal MCMF solution in the same network. Via OPNET simulation it is also predicted that the protocol will run in well under 2 seconds which means it could be used directly in real-time, or in distributed prefailure self-planning, for restoration. The significance is that network operators could aggressively optimize their spare capacity, toward theoretical minimums, while still assuring 100% restorability.

http://www.ece.ualberta.ca/~grover/pdf/Path_rest_%20DRA_JSAC.pdf

A highly efficient path-restoration protocol for management of optical network transport integrity

A distributed method for creating telecommunications paths in a network, particularly after a span failure The network includes plural distinct nodes interconnected by plural distinct spans, each span having working links and spare links Each node has a digital cross-connect switch for making and breaking connections between adjacent spans forming span pairs at a node At least one of the end nodes of a path to be created broadcasts statelets Each intermediate node between the end nodes broadcasts incoming statelets in a manner that favours use of restoration paths that eliminate the fewest other paths Statelets that have traversed spans with greater spare capacity, considering the number of statelets competing to be broadcast along the spans, are preferentially broadcast

Path restoration of networks

The total capacity required by a transport network to satisfy demand and protect it from failures contributes significantly to its cost. Previously the spare capacity of a network with a given set of working span sizes has been optimized to facilitate span restoration. Path restorable networks can, however, be even more efficient by defining the restoration problem from an end to end re-routing viewpoint. We now provide a method for capacity optimization of path restorable networks and quantify the capacity benefits of path over span restoration. The further benefits of jointly optimizing working path routing and spare capacity placement in path restorable networks is also quantified by extending the capacity optimization method presented.

Optimal capacity placement for path restoration in mesh survivable networks

A distributed method creates telecommunications paths in a network, particularly after a span failure. The network includes plural distinct nodes interconnected by plural distinct spans, each span having working links and spare links. Each node has a digital cross-connect switch for making and breaking connections between adjacent spans forming span pairs at a node. At least one of the end nodes of a path to be created broadcasts statelets (Fig 4). Each intermediate node between the end nodes broadcasts incoming statelets in a manner that favours use of restoration paths that eliminate the fewest other paths. Statelets that have traversed spans with greater spare capacity, considering the number of statelets competing to be broadcast along the spans, are preferentially broadcast.

Rainer R. Iraschko

Papers

Optimal capacity placement for path restoration in STM or ATM mesh-survivable networks

A highly efficient path-restoration protocol for management of optical network transport integrity

Path restoration of networks

Optimal capacity placement for path restoration in mesh survivable networks

Restoring failed telecoms. links using software agents