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

Combined Intra- and Inter-domain Traffic Engineering using Hot-Potato Aware Link Weights Optimization

A well-known approach to intradomain traffic engineering consists in finding the set of link weights that minimizes a network-wide objective function for a given intradomain traffic matrix. This approach is inadequate because it ignores a potential impact on interdomain routing. Indeed, the resulting set of link weights may trigger BGP to change the BGP next hop for some destination prefixes, to enforce hot-potato routing policies. In turn, this results in changes in the intradomain traffic matrix that have not been anticipated by the link weights optimizer, possibly leading to degraded network performance. We propose a BGP-aware link weights optimization method that takes these effects into account, and even turns them into an advantage. This method uses the interdomain traffic matrix and other available BGP data, to extend the intradomain topology with external virtual nodes and links, on which all the well-tuned heuristics of a classical link weights optimizer can be applied. A key innovative asset of our method is its ability to also optimize the traffic on the interdomain peering links. We show, using an operational network as a case study, that our approach does so efficiently at almost no extra computational cost.Comment: 12 pages, Short version to be published in ACM SIGMETRICS 2008, International Conference on Measurement and Modeling of Computer Systems, June 2-6, 2008, Annapolis, Maryland, US

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

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

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

Optimal Networks from Error Correcting Codes

To address growth challenges facing large Data Centers and supercomputing clusters a new construction is presented for scalable, high throughput, low latency networks. The resulting networks require 1.5-5 times fewer switches, 2-6 times fewer cables, have 1.2-2 times lower latency and correspondingly lower congestion and packet losses than the best present or proposed networks providing the same number of ports at the same total bisection. These advantage ratios increase with network size. The key new ingredient is the exact equivalence discovered between the problem of maximizing network bisection for large classes of practically interesting Cayley graphs and the problem of maximizing codeword distance for linear error correcting codes. Resulting translation recipe converts existent optimal error correcting codes into optimal throughput networks.Comment: 14 pages, accepted at ANCS 2013 conferenc

Simple and stable dynamic traffic engineering for provider scale ethernet

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia InformáticaThe high speeds and decreasing costs of Ethernet solutions has motivated providers’ interest in using Ethernet as the link layer technology in their backbone and aggregation networks. Provider scale Ethernet offers further advantages, providing not only an easy to manage solution for multicast traffic, but also transparent interconnection between clients’ LANs. These Ethernet deployments face altogether different design issues, requiring support for a significantly higher number of hosts. This support relies on hierarquization, separating address and virtual network spaces of customers and providers. In addition, large scale Ethernet solutions need to grant forwarding optimality. This can be achieved using traffic engineering approaches. Traffic engineering defines the set of engineering methods and techniques used to optimize the flow of network traffic. Static traffic engineering approaches enjoy widespread use in provider networks, but their performance is greatly penalized by sudden load variations. On the other hand, dynamic traffic engineering is tailored to adapt to load changes. However, providers are skeptical to adopt dynamic approaches as these induce problems such as routing instability, and as a result, network performance decreases. This dissertation presents a Simple and Stable Dynamic Traffic Engineering framework (SSD-TE), which addresses these concerns in a provider scale Ethernet scenario. The validation results show that SSD-TE achieves better or equal performance to static traffic engineering approaches, whilst remaining both stable and responsive to load variations

Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN

Network modeling is a critical component for building self-driving Software-Defined Networks, particularly to find optimal routing schemes that meet the goals set by administrators. However, existing modeling techniques do not meet the requirements to provide accurate estimations of relevant performance metrics such as delay and jitter. In this paper we propose a novel Graph Neural Network (GNN) model able to understand the complex relationship between topology, routing and input traffic to produce accurate estimates of the per-source/destination pair mean delay and jitter. GNN are tailored to learn and model information structured as graphs and as a result, our model is able to generalize over arbitrary topologies, routing schemes and variable traffic intensity. In the paper we show that our model provides accurate estimates of delay and jitter (worst case $R^2=0.86$) when testing against topologies, routing and traffic not seen during training. In addition, we present the potential of the model for network operation by presenting several use-cases that show its effective use in per-source/destination pair delay/jitter routing optimization and its generalization capabilities by reasoning in topologies and routing schemes not seen during training.Comment: 12 page

Robust routing under dynamic traffic demands

In order to provide service reliability with reasonable quality, it is essential for the network operator to manage the traffic flows in the core network. Managing traffic in the network is performed as routing function. In the traditional traffic management, network operator can tune routing parameters to simply manage the traffic. But traditional routing methods are not designed to handle the sudden fluctuations in the traffic. As a result, this may apparently lead to the traffic congestions in some parts of the core network, leaving other part underutilized. In this thesis we explore issues related to the routing robustness in the face of traffic demand variations. We investigate different routing methods for efficient routing using maximum link utilization (MLU) as a performance metric. The primary advantage of using link utilization is its ease to compute the network performance on real network data and synthetic data. Overloaded links might result in Quality of Service degradation (e.g. larger packet delay, packet losses etc.), so MLU might be a useful measure of network performance. For the experimentation, we have used unique data from the real operational network available in the public domain and the random data for large network topology instances. Furthermore, we propose a simple routing algorithm called Robust Routing Technique (RRT) to implement a robust routing mechanism. This mechanism allows network operator to satisfy the networking goals such as load balancing, routing robustness to the range of traffic demand matrices, link failures or to the traffic changes caused by uncertain traffic demands. Simulation experiments with real network topologies and random topologies demonstrate that our routing solution is simple (for routing) and flexible (for forwarding). K-Shortest path implementation in RRT can be extended for Multi Protocol Label Switching. Finally, we evaluate the performance of robust routing under dynamic traffic demands. We formulate the problem as a multi commodity flow problem using linear programming. We use congestion ratio to define the robust routing performance. We provide a variant to the existing robust routing mechanisms by modelling traffic demand due to Distributed Denial of service attacks or worms. Simulation results are compared with the popular OSPF traffic engineering algorithm to provide effectiveness to the proposed routing scheme. Simulation results are compared with the popular OSPF traffic engineering algorithm to provide effectiveness to the proposed routing scheme