Showing papers on "Equal-cost multi-path routing published in 2021"

The vehicle routing problem with cross-docking and resource constraints

Abstract: In this paper, we propose an extension of the vehicle routing problem with cross-docking that takes into account resource constraints at the cross-dock. These constraints limit the number of docks that can be used simultaneously. To solve this new problem, we adapt a recently proposed matheuristic based on large neighborhood search. In particular, we focus on the feasibility tests for insertions and compare heuristics and constraint programming strategies. Finally, computational experiments on instances adapted from the vehicle routing problem with cross-docking are reported. They give insights on the impact of a limited cross-dock capacity on the routing cost.

Segment Routing Centralized BGP Egress Peer Engineering

Abstract: Segment Routing (SR) leverages source routing. A node steers a packet through a controlled set of instructions, called segments, by prepending the packet with an SR header. A segment can represent any instruction topological or service-based. SR allows to enforce a flow through any topological path while maintaining per-flow state only at the ingress node of the SR domain. The Segment Routing architecture can be directly applied to the MPLS dataplane with no change on the forwarding plane. It requires a minor extension to the existing link- state routing protocols. This document illustrates the application of Segment Routing to solve the BGP Egress Peer Engineering (BGP-EPE) requirement. The SR-based BGP-EPE solution allows a centralized (Software Defined Network, SDN) controller to program any egress peer policy at ingress border routers or at hosts within the domain.

Deep Learning Aided Packet Routing in Aeronautical Ad-Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multi-Objective Optimization

Abstract: Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging due to their high-dynamic topology. In this paper, we invoke deep learning (DL) to assist routing in AANETs. We set out from the single objective of minimizing the end-to-end (E2E) delay. Specifically, a deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. The DNN is trained by exploiting the regular mobility pattern of commercial passenger airplanes from historical flight data. After training, the DNN is stored by each airplane for assisting their routing decisions during flight relying solely on local geographic information. Furthermore, we extend the DL-aided routing algorithm to a multi-objective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity and maximizing the path lifetime. Our simulation results based on real flight data show that the proposed DL-aided routing outperforms existing position-based routing protocols in terms of its E2E delay, path capacity as well as path lifetime, and it is capable of approaching the Pareto front that is obtained using global link information.

YANG Data Model for Segment Routing

Abstract: This document defines a YANG data model ([RFC6020], [RFC7950]) for segment routing ([RFC8402]) configuration and operation. This YANG model is intended to be used on network elements to configure or operate segment routing. This document defines also generic containers that SHOULD be reused by IGP protocol modules to support segment routing.