Towards Robust Traffic Engineering in IP Networks

To deliver a reliable communication service it is essential for the network operator to manage how traffic flows in the network. The paths taken by the traffic is controlled by the routing function. Traditional ways of tuning routing in IP networks are designed to be simple to manage and are not designed to adapt to the traffic situation in the network. This can lead to congestion in parts of the network while other parts of the network is far from fully utilized. In this thesis we explore issues related to optimization of the routing function to balance load in the network. We investigate methods for efficient derivation of the traffic situation using link count measurements. The advantage of using link counts is that they are easily obtained and yield a very limited amount of data. We evaluate and show that estimation based on link counts give the operator a fast and accurate description of the traffic demands. For the evaluation we have access to a unique data set of complete traffic demands from an operational IP backbone. Furthermore, we evaluate performance of search heuristics to set weights in link-state routing protocols. For the evaluation we have access to complete traffic data from a Tier-1 IP network. Our findings confirm previous studies who use partial traffic data or synthetic traffic data. We find that optimization using estimated traffic demands has little significance to the performance of the load balancing. Finally, we device an algorithm that finds a routing setting that is robust to shifts in traffic patterns due to changes in the interdomain routing. A set of worst case scenarios caused by the interdomain routing changes is identified and used to solve a robust routing problem. The evaluation indicates that performance of the robust routing is close to optimal for a wide variety of traffic scenarios. The main contribution of this thesis is that we demonstrate that it is possible to estimate the traffic matrix with good accuracy and to develop methods that optimize the routing settings to give strong and robust network performance. Only minor changes might be necessary in order to implement our algorithms in existing networks

Cautious Weight Tuning for Link State Routing Protocols

Link state routing protocols are widely used for intradomain routing in the Internet. These protocols are simple to administer and automatically update paths between sources and destinations when the topology changes. However, finding link weights that optimize network performance for a given traffic scenario is computationally hard. The situation is even more complex when the traffic is uncertain or time-varying. We present an efficient heuristic for finding link settings that give uniformly good performance also under large changes in the traffic. The heuristic combines efficient search techniques with a novel objective function. The objective function combines network performance with a cost of deviating from desirable features of robust link weight settings. Furthermore, we discuss why link weight optimization is insensitive to errors in estimated traffic data from link load measurements. We assess performance of our method using traffic data from an operational IP backbone

Aspects of proactive traffic engineering in IP networks

To deliver a reliable communication service over the Internet it is essential for the network operator to manage the traffic situation in the network. The traffic situation is controlled by the routing function which determines what path traffic follows from source to destination. Current practices for setting routing parameters in IP networks are designed to be simple to manage. This can lead to congestion in parts of the network while other parts of the network are far from fully utilized. In this thesis we explore issues related to optimization of the routing function to balance load in the network and efficiently deliver a reliable communication service to the users. The optimization takes into account not only the traffic situation under normal operational conditions, but also traffic situations that appear under a wide variety of circumstances deviating from the nominal case. In order to balance load in the network knowledge of the traffic situations is needed. Consequently, in this thesis we investigate methods for efficient derivation of the traffic situation. The derivation is based on estimation of traffic demands from link load measurements. The advantage of using link load measurements is that they are easily obtained and consist of a limited amount of data that need to be processed. We evaluate and demonstrate how estimation based on link counts gives the operator a fast and accurate description of the traffic demands. For the evaluation we have access to a unique data set of complete traffic demands from an operational IP backbone. However, to honor service level agreements at all times the variability of the traffic needs to be accounted for in the load balancing. In addition, optimization techniques are often sensitive to errors and variations in input data. Hence, when an optimized routing setting is subjected to real traffic demands in the network, performance often deviate from what can be anticipated from the optimization. Thus, we identify and model different traffic uncertainties and describe how the routing setting can be optimized, not only for a nominal case, but for a wide range of different traffic situations that might appear in the network. Our results can be applied in MPLS enabled networks as well as in networks using link state routing protocols such as the widely used OSPF and IS-IS protocols. Only minor changes may be needed in current networks to implement our algorithms. The contributions of this thesis is that we: demonstrate that it is possible to estimate the traffic matrix with acceptable precision, and we develop methods and models for common traffic uncertainties to account for these uncertainties in the optimization of the routing configuration. In addition, we identify important properties in the structure of the traffic to successfully balance uncertain and varying traffic demands

Cautious Weight Tuning for Link State Routing Protocols

Abstract Link state routing protocols are widely used for intradomain routing in the Internet. These protocols are simple to administer and automatically update paths between sources and destinations when the topology changes. However, finding link weights that optimize network performance for a given traffic scenario is computationally hard. The situation is even more complex when the traffic is uncertain or time-varying. We present an efficient heuristic for finding link settings that give uniformly good performance also under large changes in the traffic. The heuristic combines efficient search techniques with a novel objective function. The objective function combines network performance with a cost of deviating from desirable features of robust link weight settings. Furthermore, we discuss why link weight optimization is insensitive to errors in estimated traffic data from link load measurements. We assess performance of our method using traffic data from an operational IP backbone

Network overload avoidance by traffic engineering and content caching

The Internet traffic volume continues to grow at a great rate, now driven by video and TV distribution. For network operators it is important to avoid congestion in the network, and to meet service level agreements with their customers. This thesis presents work on two methods operators can use to reduce links loads in their networks: traffic engineering and content caching. This thesis studies access patterns for TV and video and the potential for caching. The investigation is done both using simulation and by analysis of logs from a large TV-on-Demand system over four months. The results show that there is a small set of programs that account for a large fraction of the requests and that a comparatively small local cache can be used to significantly reduce the peak link loads during prime time. The investigation also demonstrates how the popularity of programs changes over time and shows that the access pattern in a TV-on-Demand system very much depends on the content type. For traffic engineering the objective is to avoid congestion in the network and to make better use of available resources by adapting the routing to the current traffic situation. The main challenge for traffic engineering in IP networks is to cope with the dynamics of Internet traffic demands. This thesis proposes L-balanced routings that route the traffic on the shortest paths possible but make sure that no link is utilised to more than a given level L. L-balanced routing gives efficient routing of traffic and controlled spare capacity to handle unpredictable changes in traffic. We present an L-balanced routing algorithm and a heuristic search method for finding L-balanced weight settings for the legacy routing protocols OSPF and IS-IS. We show that the search and the resulting weight settings work well in real network scenarios

Statistical analysis of network traffic for anomaly detection and quality of service provisioning

Network-wide traffic analysis and monitoring in large-scale networks is a challenging and expensive task. In this thesis work we have proposed to analyze the traffic of a large-scale IP network from aggregated traffic measurements, reducing measurement overheads and simplifying implementation issues. We have provided contributions in three different networking fields related to network-wide traffic analysis and monitoring in large-scale IP networks. The first contribution regards Traffic Matrix (TM) modeling and estimation, where we have proposed new statistical models and new estimation methods to analyze the Origin-Destination (OD) flows of a large-scale TM from easily available link traffic measurements. The second contribution regards the detection and localization of volume anomalies in the TM, where we have introduced novel methods with solid optimality properties that outperform current well-known techniques for network-wide anomaly detection proposed so far in the literature. The last contribution regards the optimization of the routing configuration in large-scale IP networks, particularly when the traffic is highly variable and difficult to predict. Using the notions of Robust Routing Optimization we have proposed new approaches for Quality of Service provisioning under highly variable and uncertain traffic scenarios. In order to provide strong evidence on the relevance of our contributions, all the methods proposed in this thesis work were validated using real traffic data from different operational networks. Additionally, their performance was compared against well-known works in each field, showing outperforming results in most cases. Taking together the ensemble of developed TM models, the optimal network-wide anomaly detection and localization methods, and the routing optimization algorithms, this thesis work offers a complete solution for network operators to efficiently monitor large-scale IP networks from aggregated traffic measurements and to provide accurate QoS-based performance, even in the event of volume traffic anomalie

Análisis estadístico del tráfico de red para la detección de anomalías y la calidad del servicio

Network-wide traffic analysis and monitoring in large-scale networks is a challenging and expensive task. In this thesis work we have proposed to analyze the traffic of a large-scale IP network from aggregated traffic measurements, reducing measurement overheads and simplifying implementation issues. We have provided contributions in three different networking fields related to network-wide traffic analysis and monitoring in large-scale IP networks. The first contribution regards Traffic Matrix (TM) modeling and estimation, where we have proposed new statistical models and new estimation methods to analyze the Origin-Destination (OD) flows of a large-scale TM from easily available link traffic measurements. The second contribution regards the detection and localization of volume anomalies in the TM, where we have introduced novel methods with solid optimality properties that outperform current well-known techniques for network-wide anomaly detection proposed so far in the literature. The last contribution regards the optimization of the routing configuration in large-scale IP networks, particularly when the traffic is highly variable and difficult to predict. Using the notions of Robust Routing Optimization we have proposed new approaches for Quality of Service provisioning under highly variable and uncertain traffic scenarios. In order to provide strong evidence on the relevance of our contributions, all the methods proposed in this thesis work were validated using real traffic data from different operational networks. Additionally, their performance was compared against well-known works in each field, showing outperforming results in most cases. Taking together the ensemble of developed TM models, the optimal network-wide anomaly detection and localization methods, and the routing optimization algorithms, this thesis work offers a complete solution for network operators to efficiently monitor large-scale IP networks from aggregated traffic measurements and to provide accurate QoS-based performance, even in the event of volume traffic anomalies.El monitoreo y el análisis del tráfico de red en redes de gran escala es una tarea costosa y desafiante. En este trabajo de tesis nos hemos propuesto analizar el tráfico de una red IP de gran escala a partir de mediciones de tráfico agregado, reducciendo gastos de monitoreo y simplificando problemas de implementación. Hemos obtenido resultados importantes en tres áreas diferentes relacionadas con el monitoreo y el análisis del tráfico de red en redes IP a gran escala. El primer resultado concierne el modelado y la estimación de la matriz de tráfico (TM), donde hemos propuesto nuevos modelos estadísticos y nuevos métodos de estimación para analizar la flujos Origen-Destino (OD) de una TM a gran escala, a partir de mediciones de volumen en los enlaces de red, fácilmente obtenibles en los sistemas de monitoreo de red de gran escala disponibles en la actualidad. El segundo aporte corresponde con la detección y localización automática de anomalías de volumen en la TM, donde hemos introducido nuevos métodos con sólidas propiedades de optimalidad y cuyo desempeño supera el de las técnicas actualmente propuestas en la literatura para detección de anomalías de red. La última contribución considera la optimización de la configuración del enrutamiento en redes IP a gran escala, especialmente cuando el tráfico en la red es altamente variable y difícil de predecir. Utilizando las nociones de optimización robusta del enrutamiento en la red, hemos propuesto nuevos enfoques para la provisión de calidad de servicio en escenarios donde el tráfico de red es altamente variable e incierto. Con el fin de proporcionar pruebas sólidas sobre la relevancia de nuestras contribuciones, todas los métodos propuestos en este trabajo de tesis han sido evaluados y validados utilizando mediciones de tráfico real en distintas redes operativas. Al mismo tiempo, su desempeño ha sido comparado contra el obtenido por técnicas bien conocidas en cada área, mostrando mejores resultados en la mayoría de los casos. Tomando el conjunto de técnicas desarrolladas respecto del modelado de la TM, la detección y localización óptima de anomalías de red, y los algoritmos de optimización robusta del enrutamineto en la red, este trabajo de tesis ofrece una solución completa para el monitoreo eficiente de redes IP de gran escala a partir de medidas de tráfico agregado, así como también un mecanismo automático para proporcionar niveles de calidad de servicio en caso de anomalías de tráfico