Thesis (MSc)--Stellenbosch University, 2003. / ENGLISH ABSTRACT: MPLS-based recovery is intended to effect rapid and complete restoration of traffic affected by a fault in a Multiprotocol Label Switching (MPLS) network. Two MPLS-based recovery models have been proposed: lP re-routing which establishes recovery paths on demand, and protection switching which works with pre-established recovery paths. lP re-routing is robust and frugal since no resources are pre-committed but it is inherently slower than protection switching which is intended to offer high reliability to premium services where fault recovery takes place at the 100 ms time scale. This thesis presents an overview of various recovery techniques and addresses the problem of how to find an in some sense optimal set of pre-established traffic engineered recovery paths, given a network with link capacities and traffic demands. We present and motivate our choice of a nonlinear objective function and optimization method for finding traffic engineered working and recovery paths. A variant of the flow deviation method is used to find and capacitate a set of optimal label switched paths. We present and evaluate two simple methods for computing a set of pre-established traffic engineered recovery paths by using the flow deviation method. / AFRIKAANSE OPSOMMING: MPLS-gebaseerde herstel is daarop gemik om verkeer wat deur 'n fout in 'n Multiprotokol Etiketwisseling (Multiprotocol Label Switching) (MPLS) netwerk geaffekteer is, vinnig en volledig te herstel. Twee MPLS-gebaseerde herstelmodelle is voorgestel: Internetprotokol-herroetering (lP rerouting) wat herstelpaaie op aanvraag tot stand bring, en beskermingsoorskakeling (protection switching) wat met voorafbeplande herstelpaaie werk. IP-herroetering is robuust en voordelig aangesien geen netwerkbronne vooraf gereserveer word nie, maar dit is inherent stadiger as beskermingsoorskakeling wat veronderstel is om 'n hoë graad van betroubaarheid aan belangrike dienste te bied waar die herstel van foute in die 100 ms tydskaal plaasvind. Hierdie tesis verskaf 'n oorsig oor verskeie hersteltegnieke en ondersoek die probleem hoe om 'n optimale versameling van voorafbeplande herstelpaaie te vind, gegee 'n netwerk met skakelkapasiteite (link capacities) en verwagte netwerkverkeer. Ons stel voor en motiveer ons keuse van 'n nie-lineêre objekfunksie en optimeringsmetode om verkeersontwerpde (traffic engineered) aktiewe en herstelpaaie te vind. 'n Variant van die vloeideviasie (flow deviation)-metode word gebruik om 'n optimale versameling van etiketwisseling (label switched) paaie te vind en om 'n optimale hoeveelheid kapasiteit aan die paaie toe te ken. Ons stel voor en evalueer twee eenvoudige metodes om 'n versameling van optimale voorafbeplande herstelpaaie te bereken deur die vloeideviasie-metode toe te pas.

<p>Network and system management has always been of concern for telecommunication and computer system operators. The need for standardization was recognised already 20 years ago, hence several standards for network management exist today. However, the ever-increasing number of units connected to networks and the ever-increasing number of services being provided results in significant increased complexity of average network environments. This challenges current management systems. In addition to the general increase in complexity the trend among network owners and operators of merging several single service networks into larger, heterogeneous and complex full service networks challenges current management systems even further. The full service networks will require management systems more powerful than what is possible to realize basing systems purely on todays management standards. This thesis presents a distributed stochastic optimization algorithm which enables implementations of highly robust and efficient management tools. These tools may be integrated into management systems and potentially make the systems more powerful and better prepared for management of full service networks.</p><p>Emergent behavior is common in nature and easily observable in colonies of social insects and animals. Even an old oak tree can be viewed as an emergent system with its collection of interacting cells. Characteristic for any emergent system is how the overall behavior of the system emerge from many relatively simple, restricted behaviors interacting, e.g. a thousand ants building a trail, a flock of birds flying south or millions of cells making a tree grow. No centralized control exist, i.e. no single unit is in charge making global decisions. Despite distributed control, high work redundancy and stochastic behavior components, emergent systems tend to be very efficient problem solvers. In fact emergent systems tend to be both efficient, adaptive and robust which are three properties indeed desirable for a network management system. The algorithm presented in this thesis relates to a class of emergent behavior based systems known as swarm intelligence systems, i.e. the algorithm is potentially efficient, adaptive and robust.</p><p>On the contrary to other related swarm intelligence algorithms, the algorithm presented has a thorough formal foundation. This enables a better understanding of the algorithm’s potentials and limitations, and hence enables better adaptation of the algorithm to new problem areas without loss of efficiency, adaptability or robustness. The formal foundations are based on work by Reuven Rubinstein on cross entropy driven optimization. The transition from Ruinstein’s centralized and synchronous algorithm to a distributed and asynchronous algorithm is described, and the distributed algorithm’s ability to solve complex problems (NP-complete) efficiently is demonstrated.</p><p>Four examples of how the distributed algorithm may be applied in a network management context are presented. A system for finding near optimal patterns of primary/backup paths together with a system for finding cyclic protection paths in mesh networks demonstrate the algorithm’s ability to act as a tool helping management system to ensure quality of service. The algorithm’s potential as a management policy implementation mechanism is also demonstrated. The algorithm’s adaptability is shown to enable resolution of policy conflicts in a soft manner causing as little loss as possible. Finally, the algorithm’s ability to find near optimal paths (i.e. sequences) of resources in networks of large scale is demonstrated.</p>

Network and system management has always been of concern for telecommunication and computer system operators. The need for standardization was recognised already 20 years ago, hence several standards for network management exist today. However, the ever-increasing number of units connected to networks and the ever-increasing number of services being provided results in significant increased complexity of average network environments. This challenges current management systems. In addition to the general increase in complexity the trend among network owners and operators of merging several single service networks into larger, heterogeneous and complex full service networks challenges current management systems even further. The full service networks will require management systems more powerful than what is possible to realize basing systems purely on todays management standards. This thesis presents a distributed stochastic optimization algorithm which enables implementations of highly robust and efficient management tools. These tools may be integrated into management systems and potentially make the systems more powerful and better prepared for management of full service networks. Emergent behavior is common in nature and easily observable in colonies of social insects and animals. Even an old oak tree can be viewed as an emergent system with its collection of interacting cells. Characteristic for any emergent system is how the overall behavior of the system emerge from many relatively simple, restricted behaviors interacting, e.g. a thousand ants building a trail, a flock of birds flying south or millions of cells making a tree grow. No centralized control exist, i.e. no single unit is in charge making global decisions. Despite distributed control, high work redundancy and stochastic behavior components, emergent systems tend to be very efficient problem solvers. In fact emergent systems tend to be both efficient, adaptive and robust which are three properties indeed desirable for a network management system. The algorithm presented in this thesis relates to a class of emergent behavior based systems known as swarm intelligence systems, i.e. the algorithm is potentially efficient, adaptive and robust. On the contrary to other related swarm intelligence algorithms, the algorithm presented has a thorough formal foundation. This enables a better understanding of the algorithm’s potentials and limitations, and hence enables better adaptation of the algorithm to new problem areas without loss of efficiency, adaptability or robustness. The formal foundations are based on work by Reuven Rubinstein on cross entropy driven optimization. The transition from Ruinstein’s centralized and synchronous algorithm to a distributed and asynchronous algorithm is described, and the distributed algorithm’s ability to solve complex problems (NP-complete) efficiently is demonstrated. Four examples of how the distributed algorithm may be applied in a network management context are presented. A system for finding near optimal patterns of primary/backup paths together with a system for finding cyclic protection paths in mesh networks demonstrate the algorithm’s ability to act as a tool helping management system to ensure quality of service. The algorithm’s potential as a management policy implementation mechanism is also demonstrated. The algorithm’s adaptability is shown to enable resolution of policy conflicts in a soft manner causing as little loss as possible. Finally, the algorithm’s ability to find near optimal paths (i.e. sequences) of resources in networks of large scale is demonstrated.