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Equal-cost multi-path routing

About: Equal-cost multi-path routing is a research topic. Over the lifetime, 10472 publications have been published within this topic receiving 249362 citations.


Papers
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01 Jan 2008
TL;DR: In this paper, the authors presented several meta-heuristics based on separating the first and second level routing problems and solving iteratively the two resulting routing subproblems, while adjusting the satellite workloads linking them.
Abstract: The Two-Echelon Vehicle Routing Problem (2E-VRP) is an extension of the classical VRP where the delivery from a single depot to customers is managed by routing and consolidating the freight through intermediate depots called satellites. We presented several meta-heuristics based on separating the first and second level routing problems and solving iteratively the two resulting routing subproblems, while adjusting the satellite workloads linking them. The two main meta-heuristics use a clustering and a multi-depot approach, respectively. We present experimental results comparing the meta-heuristics among them and with an exact method, as well as examining the impact of different customer and satellites spatial distributions on the performance of the methods and the cost of the distribution system. The experiments show that the clustering-based metaheuristics perform very well and that a two-echelon system may significantly reduce the cost of distribution.

66 citations

Journal ArticleDOI
TL;DR: The proposed routing algorithm, MaxEW, adopts the social welfare function from social sciences to compute energy welfare as a goodness measure for energy populations and supports preparedness and hence robustness to diverse event generation patterns.

66 citations

Book
12 Nov 2007
TL;DR: This chapter discusses the evolution of Optical Network Architectures, which led to Path Routing and Protection, and the current state of optical networks, as well as experiments with Multi-Port Card Diversity and Distributed Routing.
Abstract: List of Figures. List of Tables. Foreword. Preface. 1 Optical Networking. 1.1 Evolution of Optical Network Architectures. 1.1.1 Transparent Networks. 1.1.2 Opaque Networks. 1.1.3 Translucent Networks. 1.2 Layered Network Architecture. 1.2.1 Optical Layer. 1.2.2 Logical Layer. 1.2.3 Service/Application Layer. 1.3 Multi-Tier Optical Layer. 1.3.1 One-Tier Network Architecture. 1.3.2 Two-Tier Network Architecture. 1.3.3 Network Scalability. 1.4 The Current State of Optical Networks. 1.5 Organization of the Book. 2 Recovery in Optical Networks. 2.1 Introduction. 2.2 Failure Recovery. 2.3 Fault Recovery Classifications. 2.4 Protection of Point-to-Point Systems. 2.4.1 (1 + 1) Protection. 2.4.2 (1 : 1) Protection. 2.4.3 (M :N) Protection. 2.5 Ring-Based Protection. 2.5.1 Failure Recovery in SONET Networks with Ring Topologies. 2.5.2 Ring-Based Failure Recovery in Optical Networks with Mesh Topologies. 2.6 Path-Based Protection. 2.6.1 Dedicated Backup Path Protection (DBPP) in Mesh Networks. 2.6.2 Shared Back Path Protection (SBPP) in Mesh Networks. 2.7 Link/Span-Based Protection. 2.8 Segment-Based Protection. 2.9 Island-Based Protection. 2.10 Mesh Network Restoration. 2.10.1 Centralized Restoration Techniques. 2.10.2 Distributed Restoration Techniques. 2.11 Multi-Layer Recovery. 2.12 Recovery Triggers and Signaling Mechanisms. 2.13 Conclusion. 3 Mesh Routing and Recovery Framework. 3.1 Introduction. 3.2 Mesh Protection and Recovery Techniques. 3.2.1 Link-Based Protection. 3.2.2 Path-Based Protection. 3.2.3 Segment-Based Protection. 3.3 Concept of Shared Risk Groups. 3.3.1 Shared Link Risk Groups. 3.3.2 Shared Node Risk Groups. 3.3.3 Shared Equipment Risk Groups. 3.4 Centralized vs Distributed Routing. 3.4.1 Centralized Routing. 3.4.2 Distributed Routing. 3.4.3 Centralized vs Distributed Routing Performance Results. 3.5 Conclusion. 4 Path Routing and Protection. 4.1 Introduction. 4.2 Routing in Path-Protected Mesh Networks. 4.3 Protection in Path-Protected Mesh Networks. 4.3.1 Dedicated Backup Path-Protected Lightpaths. 4.3.2 Shared Backup Path-Protected Lightpaths. 4.3.3 Preemptible Lightpaths. 4.3.4 Diverse Unprotected Lightpaths with Dual-Homing. 4.3.5 Multiple Simultaneous Backup Path-Protected Lightpaths. 4.3.6 Relaxing the Protection Guarantees. 4.3.7 Impact of Multi-Port Card Diversity Constraints. 4.4 Experiments and Capacity Performance Results. 4.4.1 Performance Results for Path-Based Protection Techniques. 4.4.2 Experiments with Multi-Port Card Diversity. 4.5 Recovery Time Analysis. 4.6 Recovery Time and Capacity Trade-Offs. 4.7 Conclusion. 5 Path Routing - Part 1: Complexity. 5.1 Introduction. 5.2 Network Topology Abstraction. 5.2.1 Service Definition. 5.2.2 Operational Models: Online vs Offline Routing. 5.3 Shortest-Path Routing. 5.3.1 Dijkstra's Algorithm. 5.3.2 Dijkstra's Algorithm Generalization to K-Shortest Paths. 5.3.3 Shortest-Path Routing with Constraints. 5.4 Diverse-Path Routing. 5.4.1 SRG Types. 5.4.2 Diverse-Path Routing with Default SRGs. 5.4.3 Diverse-Path Routing with Fork SRGs. 5.4.4 Diverse-Path Routing with General SRGs. 5.5 Shared Backup Path Protection Routing. 5.5.1 Protection Guarantees and Rules of Sharing. 5.5.2 Complexity of Shared Backup Path Protection Routing. 5.6 Routing ILP. 5.6.1 ILP Description. 5.6.2 Implementation Experience. 5.7 Conclusion. 5.8 Appendix. 5.8.1 Complexity of Diverse-Path Routing with General SRGs. 5.8.2 Complexity of SBPP Routing. 6 Path Routing - Part 2: Heuristics. 6.1 Introduction. 6.1.1 Operational Models: Centralized vs Distributed Routing. 6.1.2 Topology Modeling Example. 6.2 Motivating Problems. 6.2.1 Heuristic Techniques. 6.3 K-Shortest Path Routing. 6.3.1 Yen's K-Shortest Path Algorithm. 6.3.2 Constrained Shortest-Path Routing. 6.4 Diverse-Path Routing. 6.4.1 Best-Effort Path Diversity. 6.5 Shared Backup Path Protection Routing. 6.5.1 Sharing-Independent Routing Heuristic. 6.5.2 Sharing-Dependent Routing Heuristic. 6.6 Routing Preemptible Services. 6.7 General Constrained Routing Framework. 6.7.1 Implementation Experience. 6.8 Conclusion. 7 Enhanced Routing Model for SBPP Services. 7.1 Introduction. 7.2 Routing Metric. 7.3 Routing Algorithm. 7.4 Experiments. 7.4.1 Effect of . 7.4.2 Effect of alpha. 7.5 Conclusion. 8 Controlling Sharing for SBPP Services. 8.1 Introduction. 8.2 Express Links. 8.2.1 Routing with Express Links. 8.2.2 Analysis and Results. 8.2.3 Express Links-Conclusion. 8.3 Limiting Sharing. 8.3.1 Example. 8.3.2 Solution Alternatives. 8.3.3 Analysis of Capping. 8.3.4 Analysis of Load-Balancing. 8.3.5 Limiting Sharing-Conclusion. 8.4 Analysis of Active Reprovisioning. 8.4.1 Evaluation of Active Reprovisioning. 8.4.2 Active Reprovisioning-Conclusion. 8.5 Conclusion. 9 Path Computation with Partial Information. 9.1 Introduction. 9.2 Complexity of the Deterministic Approach. 9.2.1 Complexity of the Failure Dependent Strategy. 9.2.2 Complexity of the Failure Independent Strategy. 9.3 Probabilistic Approach. 9.3.1 A Problem of Combinations. 9.3.2 Analogy with SRG Arrangement into a Set of Backup Channels. 9.4 Probabilistic Routing Algorithm with Partial Information. 9.5 Locally Optimized Channel Selection. 9.5.1 Shared Mesh Protection Provisioning Using Vertex Coloring. 9.5.2 Implementation and Applications. 9.6 Required Extensions to Routing Protocols. 9.7 Experiments and Performance Results. 9.7.1 Accuracy and Distributions of Probability Functions. 9.7.2 Comparison of Deterministic vs ProbabilisticWeight Functions on Real Networks. 9.7.3 Benefits of Locally Optimized Lightpath Provisioning. 9.7.4 Summary. 9.8 Conclusion. 10 Path Reoptimization. 10.1 Introduction. 10.2 Routing Algorithm. 10.2.1 Cost model. 10.2.2 Online Routing Algorithm. 10.3 Reoptimization Algorithm. 10.4 The Complexity of Reoptimization. 10.4.1 No Prior Placement of Protection Channels or Primary Paths. 10.4.2 Prior Placement of Protection Channels or Primary Paths. 10.5 Experiments. 10.5.1 Calibration. 10.5.2 Real Networks. 10.5.3 Static Network Infrastructure. 10.5.4 Growing Network Infrastructure. 10.5.5 Network Dynamics. 10.6 Conclusion. 11 Dimensioning of Path-Protected Mesh Networks. 11.1 Introduction. 11.2 Network and Traffic Modeling. 11.3 Mesh Network Characteristics. 11.3.1 Path Length Analysis. 11.3.2 Protection-to-Working Capacity Ratio Analysis. 11.3.3 Sharing Analysis. 11.4 Asymptotic Behavior of the Protection-to-Working Capacity Ratio. 11.4.1 Examples. 11.4.2 General Results. 11.5 Dimensioning Mesh Optical Networks. 11.5.1 Node Model and Traffic Conservation Equations. 11.5.2 Dimensioning Examples and Results. 11.6 The Network Global Expectation Model. 11.7 Accuracy of Analytical Estimates. 11.8 Recovery Time Performance. 11.9 Conclusion. 12 Service Availability in Path-Protected Mesh Networks. 12.1 Introduction. 12.2 Network Service Availability. 12.2.1 Motivation. 12.2.2 Focus on Dual-Failure Scenarios. 12.2.3 Reliability and Availability. 12.3 Service Availability in Path-Protected Mesh Networks. 12.3.1 Dual-Failure Recoverability. 12.3.2 A Markov Model Approach to Service Availability. 12.3.3 Modeling Sharing of Backup Channels. 12.3.4 Impact of Channel Protection. 12.3.5 Impact of Reprovisioning. 12.4 Availability in Single and Multiple Domains. 12.4.1 Network Recovery Architecture-Single Domain. 12.4.2 Network Recovery Architecture-Multiple Domains. 12.4.3 Results and Discussion. 12.4.4 A Simple Model. 12.5 Availability in Ring and Path-Protected Networks. 12.5.1 Ring Availability Analysis. 12.5.2 Results and Discussion. 12.5.3 The Simple Model Again. 12.6 Conclusion. Bibliography. Index.

65 citations

Journal ArticleDOI
TL;DR: A novel infrastructure-based connectivity aware routing protocol called CAR-II that enables multihop vehicular applications, as well as mobile data offloading and Internet-based services, and improves the routing performance in VANETs by dynamically selecting routing paths with guaranteed connectivity and reduced delivery delay.
Abstract: With the high demand of mobile Internet services, vehicular ad hoc networks (VANETs) have become a promising technology to enable vehicular Internet access. However, the development of a reliable routing protocol to route data packets between vehicles and infrastructure gateways is still a challenging task due to the high mobility and frequent changes of the network topology. The conventional position-based routing (PBR) in VANETs can neither guarantee the existence of a routing path between the source and the destination prior to the transmission nor provide connection duration information, which makes it unsuitable to route Internet packets. In this paper, we propose a novel infrastructure-based connectivity aware routing protocol called $i$ CAR-II that enables multihop vehicular applications, as well as mobile data offloading and Internet-based services. $i$ CAR-II consists of a number of algorithms triggered and run by vehicles to predict local network connectivity and update location servers with real-time network information, in order to construct a global network topology. By providing real-time connectivity awareness, $i$ CAR-II improves the routing performance in VANETs by dynamically selecting routing paths with guaranteed connectivity and reduced delivery delay. Detailed analysis and simulation-based evaluations of $i$ CAR-II demonstrate the validity of using VANETs for mobile data offloading and the significant improvement of VANETs performance in terms of packet delivery ratio and end to end delay.

65 citations

Journal ArticleDOI
TL;DR: A cooperative routing strategy is proposed by introducing a new link metric that characterizes the detection error exponent that captures the contribution of a given link to the decay rate of error probability.
Abstract: In this paper, the detection of a correlated Gaussian field using a large multi-hop sensor network is investigated. A cooperative routing strategy is proposed by introducing a new link metric that characterizes the detection error exponent. Derived from the Chernoff information and Schweppe's likelihood recursion, this link metric captures the contribution of a given link to the decay rate of error probability and has the form of the capacity of a Gaussian channel with the sender transmitting the innovation of its measurement. For one-dimensional Gauss-Markov fields, the link metric can be represented explicitly as a function of the link length. Cooperative routing is achieved using the Kalman data aggregation and shortest path routing. Numerical simulations show that cooperative routing can be significantly more energy efficient than noncooperative routing for the same detection performance

65 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202327
202268
20214
20204
201912
201833