Loop-Free Backpressure Routing Using Link-Reversal Algorithms

The backpressure routing policy is known to be a throughput optimal policy that supports any feasible traffic demand in data networks, but may have poor delay performance when packets traverse loops in the network. In this paper, we study loop-free backpressure routing policies that forward packets along directed acyclic graphs (DAGs) to avoid the looping problem. These policies use link reversal algorithms to improve the DAGs in order to support any achievable traffic demand. For a network with a single commodity, we show that a DAG that supports a given traffic demand can be found after a finite number of iterations of the link-reversal process. We use this to develop a joint link-reversal and backpressure routing policy, called the loop free backpressure (LFBP) algorithm. This algorithm forwards packets on the DAG, while the DAG is dynamically updated based on the growth of the queue backlogs. We show by simulations that such a DAG-based policy improves the delay over the classical backpressure routing policy. We also propose a multicommodity version of the LFBP algorithm, and via simulation we show that its delay performance is better than that of backpressure.National Science Foundation (U.S.) (Grant CNS-1116209)United States. Office of Naval Research (Grant N00014-12-1-0064

Delay-aware Backpressure Routing Using Graph Neural Networks

We propose a throughput-optimal biased backpressure (BP) algorithm for routing, where the bias is learned through a graph neural network that seeks to minimize end-to-end delay. Classical BP routing provides a simple yet powerful distributed solution for resource allocation in wireless multi-hop networks but has poor delay performance. A low-cost approach to improve this delay performance is to favor shorter paths by incorporating pre-defined biases in the BP computation, such as a bias based on the shortest path (hop) distance to the destination. In this work, we improve upon the widely-used metric of hop distance (and its variants) for the shortest path bias by introducing a bias based on the link duty cycle, which we predict using a graph convolutional neural network. Numerical results show that our approach can improve the delay performance compared to classical BP and existing BP alternatives based on pre-defined bias while being adaptive to interference density. In terms of complexity, our distributed implementation only introduces a one-time overhead (linear in the number of devices in the network) compared to classical BP, and a constant overhead compared to the lowest-complexity existing bias-based BP algorithms.Comment: 5 pages, 5 figures, submitted to IEEE ICASSP 202

LifeTime-aware Backpressure - a new delay-enhanced Backpressure-based routing protocol

Dynamic Backpressure is a highly desirable family of routing protocols known for their attractive mathematical proprieties. However, these protocols suffer from a high end-to-end delay making them inefficient for real-time traffic with strict endto-end delay requirements. In this paper, we address this issue by proposing a new adjustable and fully distributed Backpressurebased scheme with low queue management complexity, named LifeTime-Aware BackPressure (LTA-BP). The novelty in the proposed scheme consists in introducing the urgency level as a new metric for service differentiation among the competing traffic flows in the network. Our scheme not just significantly improves the quality of service provided for real-time traffic with stringent end-to-end delay constraints, but interestingly protects also the flows with softer delay requirements from being totally starved. The proposed scheme has been evaluated and compared against other state of the art routing protocol, using computer simulation, and the obtained results show its superiority in terms of the achieved end-to-end delay and throughput

Modeling and optimizing network infrastructure for autonomous vehicles

Autonomous vehicle (AV) technology has matured sufficiently to be in testing on public roads. However, traffic models of AVs are still in development. Most previous work has studied AV technologies in micro-simulation. The purpose of this dissertation is to model and optimize AV technologies for large city networks to predict how AVs might affect city traffic patterns and travel behaviors. To accomplish these goals, we construct a dynamic network loading model for AVs, consisting of link and node models of AV technologies, which is used to calculate time-dependent travel times in dynamic traffic assignment. We then study several applications of the dynamic network loading to predict how AVs might affect travel demand and traffic congestion. AVs admit reduced perception-reaction times through technologies such as (cooperative) adaptive cruise control, which can reduce following headways and increase capacity. Previous work has studied these in micro-simulation, but we construct a mesoscopic simulation model for analyses on large networks. To study scenarios with both autonomous and conventional vehicles, we modify the kinematic wave theory to include multiple classes of flow. The flow-density relationship also changes in space and time with the class proportions. We present multiclass cell transmission model and prove that it is a Godunov approximation to the multiclass kinematic wave theory. We also develop a car-following model to predict the fundamental diagram at arbitrary proportions of AVs. Complete market penetration scenarios admit dynamic lane reversal -- changing lane direction at high frequencies to more optimally allocate road capacity. We develop a kinematic wave theory in which the number of lanes changes in space and time, and approximately solve it with a cell transmission model. We study two methods of determining lane direction. First, we present a mixed integer linear program for system optimal dynamic traffic assignment. Since this program is computationally difficult to solve, we also study dynamic lane reversal on a single link with deterministic and stochastic demands. The resulting policy is shown to significantly reduce travel times on a city network. AVs also admit reservation-based intersection control, which can make greater use of intersection capacity than traffic signals. AVs communicate with the intersection manager to reserve space-time paths through the intersection. We create a mesoscopic node model by starting with the conflict point variant of reservations and aggregating conflict points into capacity-constrained conflict regions. This model yields an integer program that can be adapted to arbitrary objective functions. To motivate optimization, we present several examples on theoretical and realistic networks demonstrating that naïve reservation policies can perform worse than traffic signals. These occur due to asymmetric intersections affecting optimal capacity allocation and/or user equilibrium route choice behavior. To improve reservations, we adapt the decentralized backpressure wireless packet routing and P0 traffic signal policies for reservations. Results show significant reductions in travel times on a city network. Having developed link and node models, we explore how AVs might affect travel demand and congestion. First, we study how capacity increases and reservations might affect freeway, arterial, and city networks. Capacity increases consistently reduced congestion on all networks, but reservations were not always beneficial. Then, we use dynamic traffic assignment within a four-step planning model, adding the mode choice of empty repositioning trips to avoid parking costs. Results show that allowing empty repositioning to encourage adoption of AVs could reduce congestion. Also, once all vehicles are AVs, congestion will still be significantly reduced. Finally, we present a framework to use the dynamic network loading model to study shared AVs. Results show that shared AVs could reduce congestion if used in certain ways, such as with dynamic ride-sharing. However, shared AVs also cause significant congestion. To summarize, this dissertation presents a complete mesoscopic simulation model of AVs that could be used for a variety of studies of AVs by planners and practitioners. This mesoscopic model includes new node and link technologies that significantly improve travel times over existing infrastructure. In addition, we motivate and present more optimal policies for these AV technologies. Finally, we study several travel behavior scenarios to provide insights about how AV technologies might affect future traffic congestion. The models in this dissertation will provide a basis for future network analyses of AV technologies.Civil, Architectural, and Environmental Engineerin

OPTIMAL RESPONSE ARCHITECTURE FOR NETWORK OVERLAY PROTOCOL

Genetic networks are often designed to work with a single routing path, such as the shortest path, which is known as sub-optimal performance. On the other hand, the optimal performance policies suggested above (i.e. back pressure) require that each device in the network take dynamic routing decisions. In this paper, we examine the overlay structure of dynamic routing, so you only need a subset of devices (overlay nodes) to make dynamic routing decisions. We define the basic set of nodes that must form traffic to maximize network performance in many products. We apply the optimal node approach algorithm to many graphs and the results show that a small fraction of the overlapping nodes is sufficient for maximum performance. Finally, we propose a policy based on guiding thresholds and policy, which dynamically controls the traffic jams in overlapping nodes. The BP-T policy has been clarified to increase the performance of the situation when the underlying tracks do not overlap. In all simulated simulation scenarios, OBP not only achieves total productivity but also reduces delay compared to optimal performance guidance

Backpressure or no backpressure? Two simple examples

Open access via Elsevier agreementPeer reviewe

Desenvolupament, proves de camp i anàlisi de resultats en una xarxa de sensors

The objective of this master thesis is to describe the problems of the underwater acoustic sensor network and make some experiments. The experiments carried out try to characterize the communication in underwater environments in order to be able to develop underwater sensor networks. In the first chapter we describe the motivations, features of aquatic environment, the difficulties of underwater acoustic channels, and the open questions in mobile underwater sensor network design. In the second chapter we try to describe the experiments, show the results and try to explain these results. And finally in the third chapter we explain the conclusions and the further works of this master thesis

Center for Aeronautics and Space Information Sciences

This report summarizes the research done during 1991/92 under the Center for Aeronautics and Space Information Science (CASIS) program. The topics covered are computer architecture, networking, and neural nets

Predictive and distributed routing balancing (PR-DRB) : high speed interconnection networks

Current parallel applications running on clusters require the use of an interconnection network to perform communications among all computing nodes available. Imbalance of communications can produce network congestion, reducing throughput and increasing latency, degrading the overall system performance. On the other hand, parallel applications running on these networks posses representative stages which allow their characterization, as well as repetitive behavior that can be identified on the basis of this characterization. This work presents the Predictive and Distributed Routing Balancing (PR-DRB), a new method developed to gradually control network congestion, based on paths expansion, traffic distribution and effective traffic load, in order to maintain low latency values. PR-DRB monitors messages latencies on intermediate routers, makes decisions about alternative paths and record communication pattern information encountered during congestion situation. Based on the concept of applications repetitiveness, best solution recorded are reapplied when saved communication pattern re-appears. Traffic congestion experiments were conducted in order to evaluate the performance of the method, and improvements were observed.Les aplicacions paral·leles actuals en els Clústers requereixen l'ús d'una xarxa d'interconnexió per comunicar a tots els nodes de còmput disponibles. El desequilibri en la càrrega de comunicacions pot congestionar la xarxa, incrementant la latència i disminuint el throughput, degradant el rendiment total del sistema. D'altra banda, les aplicacions paral·leles que s'executen sobre aquestes xarxes contenen etapes representatives durant la seva execució les quals permeten caracteritzar-les, a més d'extraure un comportament repetitiu que pot ser identificat en base a aquesta caracterització. Aquest treball presenta el Balanceig Predictiu de Encaminament Distribuït (PR-DRB), un nou mètode desenvolupat per controlar la congestió a la xarxa en forma gradual, basat en l'expansió de camins, la distribució de trànsit i càrrega efectiva actual per tal de mantenir una latència baixa. PR-DRB monitoritza la latència dels missatges en els encaminadors, pren decisions sobre els camins alternatius a utilitzar i registra la informació de la congestió sobre la base del patró de comunicacions detectat, utilitzant com a concepte base la repetitivitat de les aplicacions per després tornar a aplicar la millor solució quan aquest patró es repeteixi. Experiments de trànsit amb congestió van ser portats a terme per avaluar el rendiment del mètode, els quals van mostrar la bondat del mateix.Las aplicaciones paralelas actuales en los Clústeres requieren el uso de una red de interconexión para comunicar a todos los nodos de cómputo disponibles. El desbalance en la carga de comunicaciones puede congestionar la red, incrementando la latencia y disminuyendo el throughput, degradando el rendimiento total del sistema. Por otro lado, las aplicaciones paralelas que corren sobre estas redes contienen etapas representativas durante su ejecución las cuales permiten caracterizarlas, además de un comportamiento repetitivo que puede ser identificado en base a dicha caracterización. Este trabajo presenta el Balanceo Predictivo de Encaminamiento Distribuido (PR-DRB), un nuevo método desarrollado para controlar la congestión en la red en forma gradual; basado en la expansión de caminos, la distribución de tráfico y carga efectiva actual, a fin de mantener una latencia baja. PR-DRB monitorea la latencia de los mensajes en los encaminadores, toma decisiones sobre los caminos alternativos a utilizar y registra la información de la congestión en base al patrón de comunicaciones detectado, usando como concepto base la repetitividad de las aplicaciones para luego volver a aplicar la mejor solución cuando dicho patrón se repita. Experimentos de tráfico con congestión fueron llevados a cabo para evaluar el rendimiento del método, los cuales mostraron la bondad del mismo