Algorithms for network service guarantee under minimal link usage information

One way to guaranteeing service for an application flow even if a network happens to fail is to establish a restoration path with the bandwidth that amounts to the same of the flow. If the flows can share the bandwidth for their restoration paths with others, we can reduce bandwidth consumption required for restoration. It is also required that deciding sharable bandwidth among flows should be done using controllable link information at each node. This paper proposes an algorithm to determine the sharable bandwidth among application flows given local link usage information at each node, validates the results of the algorithm and analyze the conditions required to achieve the goal by simulation.5th IFIP International Conference on Network Control & Engineering for QoS, Security and MobilityRed de Universidades con Carreras en Informática (RedUNCI

Prediction-based Decentralized Routing Algorithm

We introduce a new efficient routing algorithm called Prediction-based Decentralized Routing algorithm (PDR), which is based on the Ant Colony Optimization (ACO) meta-heuristics. In our approach, an ant uses a combination of the link state information and the predicted link load instead of the ant's trip time to determine the amount of pheromone to deposit. A Feed Forward Neural Network (FFNN) is used to build adaptive traffic predictors which capture the actual traffic behaviour. We study two performance parameters: the rejection ratio and the percentage of accepted bandwidth under two different network load conditions. We show that our algorithm reduces the rejection ratio of requests and achieves a higher throughput when compared to Shortest Path First and Widest Shortest Path algorithms

Algorithms for network service guarantee under minimal link usage information

One way to guaranteeing service for an application flow even if a network happens to fail is to establish a restoration path with the bandwidth that amounts to the same of the flow. If the flows can share the bandwidth for their restoration paths with others, we can reduce bandwidth consumption required for restoration. It is also required that deciding sharable bandwidth among flows should be done using controllable link information at each node. This paper proposes an algorithm to determine the sharable bandwidth among application flows given local link usage information at each node, validates the results of the algorithm and analyze the conditions required to achieve the goal by simulation.5th IFIP International Conference on Network Control & Engineering for QoS, Security and MobilityRed de Universidades con Carreras en Informática (RedUNCI

Distributed Flow Scheduling in an Unknown Environment

Flow scheduling tends to be one of the oldest and most stubborn problems in networking. It becomes more crucial in the next generation network, due to fast changing link states and tremendous cost to explore the global structure. In such situation, distributed algorithms often dominate. In this paper, we design a distributed virtual game to solve the flow scheduling problem and then generalize it to situations of unknown environment, where online learning schemes are utilized. In the virtual game, we use incentives to stimulate selfish users to reach a Nash Equilibrium Point which is valid based on the analysis of the `Price of Anarchy'. In the unknown-environment generalization, our ultimate goal is the minimization of cost in the long run. In order to achieve balance between exploration of routing cost and exploitation based on limited information, we model this problem based on Multi-armed Bandit Scenario and combined newly proposed DSEE with the virtual game design. Armed with these powerful tools, we find a totally distributed algorithm to ensure the logarithmic growing of regret with time, which is optimum in classic Multi-armed Bandit Problem. Theoretical proof and simulation results both affirm this claim. To our knowledge, this is the first research to combine multi-armed bandit with distributed flow scheduling.Comment: 10 pages, 3 figures, conferenc

Prediction-based decentralized routing algorithm

We introduce a new efficient routing algorithm called Prediction-based Decentralized Routing algorithm (PDR), which is based on the Ant Colony Optimization (ACO) meta-heuristics. In our approach, an ant uses a combination of the link state information and the predicted link load instead of the ant’s trip time to determine the amount of pheromone to deposit. A Feed Forward Neural Network (FFNN) is used to build adaptive traffic predictors which capture the actual traffic behaviour. We study two performance parameters: the rejection ratio and the percentage of accepted bandwidth under two different network load conditions. We show that our algorithm reduces the rejection ratio of requests and achieves a higher throughput when compared to Shortest Path First and Widest Shortest Path algorithms

A Traffic Engineering Algorithm for Provisioning Virtual Private Networks in the Enhanced Hose Model

Abstract: A Virtual Private Network is a logical network established on top of a public packet switched network. To guarantee that quality of service requirements, specified by customers, can be met, the network service provider needs to reserve enough resources on the network and allocate/manage them in an optimal way. Traffic engineering algorithms can be used by the Network Service Provider to establish multiple Virtual Private Networks in an optimal way, while meeting customers' Quality of Service requirements. For delay sensitive network applications, it is critical to meet both bandwidth and delay requirements. In contrast to traditional Virtual Private Network Quality of Service models (customer-pipe model and hose model), which focused only on bandwidth requirements, a new model called the enhanced hose model has been proposed, which considers both bandwidth and delay requirements. However, to the best of our knowledge, thus far, traffic engineering problems associated with establishing multiple enhanced hose model Virtual Private Networks have not been investigated. In this paper, we proposed a novel Virtual Private Network traffic engineering algorithm, called the minimum bandwidth-delay cost tree algorithm to address these problems. According to experimental simulations conducted and reported in our paper, the minimum bandwidth-delay cost tree algorithm can indeed achieved better performance (lower rejection ratios) compared to previous algorithms

A prediction-based model for consistent adaptive routing in back-bone networks at extreme situations

To reduce congestion, numerous routing solutions have been proposed for backbone networks, but how to select paths that stay consistently optimal for a long time in extremely congested situations, avoiding the unnecessary path reroutings, has not yet been investigated much. To solve that issue, a model that can measure the consistency of path latency difference is needed. In this paper, we make a humble step towards a consistent differential path latency model and by predicting base on that model, a metric Path Swap Indicator (PSI) is proposed. By learning the history latency of all optional paths, PSI is able to predict the onset of an obvious and steady channel deterioration and make the decision to switch paths. The effect of PSI is evaluated from the following aspects: (1) the consistency of the path selected, by measuring the time interval between PSI changes; (2) the accuracy of the channel congestion situation prediction; and (3) the improvement of the congestion situation. Experiments were carried out on a testbed using real-life Abilene traffic datasets collected at different times and locations. Results show that the proposed PSI can stay consistent for over 1000 s on average, and more than 3000 s at the longest in our experiment, while at the same time achieving a congestion situation improvement of more than 300% on average, and more than 200% at the least. It is evident that the proposed PSI metric is able to provide a consistent channel congestion prediction with satisfiable channel improvement at the same time. The results also demonstrate how different parameter values impact the result, both in terms of prediction consistency and the congestion improvement

Joint Optimization of Intra- and Inter-Autonomous System Traffic Engineering

Traffic Engineering (TE) is used to optimize IP operational network performance. The existing literature generally considers intra- and inter-AS (Autonomous System) TE independently. However, the overall network performance may not be truly optimized when these aspects are considered separately. This is due to the interaction between intra- and inter-AS TE, where a solution of intra-AS TE may not be a good input to inter-AS TE and vice versa. To remedy this situation, we propose considering intra-AS aspects during inter-AS TE and vice versa. We propose a joint optimization of intra- and inter-AS TE to further improve the overall network performance by simultaneously finding the best egress points for the inter-AS traffic and the best routing scheme for the intra-AS traffic. Three strategies are presented to attack the problem, namely sequential, nested and integrated optimization. Our simulation study shows that, compared to sequential and nested optimization, integrated optimization can significantly improve the overall network performance by accommodating 30%-60% more traffic demands

Modeling the controlled delivery power grid

Competitive energy markets, stricter regulation, and the integration of distributed renewable energy sources are forcing companies to reengineer energy production and distribution. The Controlled Delivery Power Grid is proposed as a novel approach to transport energy from generators to consumers. In this approach, energy distribution is performed in an asynchronous and distributed fashion. Much like the Internet, energy is delivered as addressable packets, which allow a controlled delivery of energy. As a proof-of-concept of the controllable delivery grid, two experimental test beds, one with integrated energy storage and another with no energy storage, were designed and built to evaluate the efficiency of a power distribution and scheduling scheme. Both test beds use a request-grant protocol where energy is supplied in discrete quantities. The performance of the system is measured in terms of the ability to satisfy requests from consumers. The results show high satisfaction ratios for distribution capacities that are smaller than the maximum demand. The distribution of energy is modelled with graph theory and as an Integer Linear Programming problem to minimize transmission losses and determine routes for energy flows in a network with distributed sources and consumers. The obtained results are compared with a heuristic approach based on the Dijkstra\u27s shortest path algorithm, which is proposed as a feasible approach to routing the transmission of packetized energy