The Babel Routing Protocol

Chroboczek, Juliusz

doi:10.17487/rfc8966

Cited by 44 publications

(8 citation statements)

References 2 publications

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“…(17). Algorithm 1 does not mandate nodes to know the entire topology and it is fully distributed, therefore its implementation on a DV routing protocol is straightforward and we effectively integrated it with the Babel protocol [15], a well-known DV routing protocol 3 .…”

Section: Tuning Babel With Centralitymentioning

confidence: 99%

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Exact Distributed Load Centrality Computation: Algorithms, Convergence, and Applications to Distance Vector Routing

Maccari

Ghiro

Guerrieri

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

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Many optimization techniques for networking protocols take advantage of topological information to improve performance. Often, the topological information at the core of these techniques is a centrality metric such as the Betweenness Centrality (BC) index. BC is, in fact, a centrality metric with many well-known successful applications documented in the literature, from resource allocation to routing. To compute BC, however, each node must run a centralized algorithm and needs to have the global topological knowledge; such requirements limit the feasibility of optimization procedures based on BC. To overcome restrictions of this kind, we present a novel distributed algorithm that requires only local information to compute an alternative similar metric, called Load Centrality (LC). We present the new algorithm together with a proof of its convergence and the analysis of its time complexity. The proposed algorithm is general enough to be integrated with any distance vector (DV) routing protocol. In support of this claim, we provide an implementation on top of Babel, a real-world DV protocol. We use this implementation in an emulation framework to show how LC can be exploited to reduce Babel's convergence time upon node failure, without increasing control overhead. As a key step towards the adoption of centrality-based optimization for routing, we study how the algorithm can be incrementally introduced in a network running a DV routing protocol. We show that even when only a small fraction of nodes participate in the protocol, the algorithm accurately ranks nodes according to their centrality.

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Section: Tuning Babel With Centralitymentioning

confidence: 99%

“…To further show the practical applicability of our contribution, we extend babeld, the open-source implementation of the widely used DV routing protocol for mesh networks specified in RFC 6126 [15]. We show that, exploiting the notion of LC, the protocol convergence time can be improved up to 13% without increasing signaling overhead.…”

Section: Introductionmentioning

confidence: 99%

Exact Distributed Load Centrality Computation: Algorithms, Convergence, and Applications to Distance Vector Routing

Maccari

Ghiro

Guerrieri

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

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“…if it is closer to the source). This condition is used in other routing protocols to prevent the existence of routing loops; it is called feasibility condition [16,17,18,19]. In our case, it is used to ensure that routers do not maintain trees indefinitely in the presence of routing loops.…”

Section: Parent Routermentioning

confidence: 99%

HPIM-DM: a fast and reliable dense-mode multicast routing protocol (extended version)

Oliveira¹,

Silva²,

Valadas³

2020

Preprint

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This paper proposes the HPIM-DM (Hard-state Protocol Independent Multicast -Dense Mode) multicast routing protocol. HPIM-DM is a hard-state version of PIM-DM that keeps its main characteristics but has faster convergence and better resilience to replay attacks. Like PIM-DM, HPIM-DM is meant for dense networks and supports its operation on a unicast routing protocol and reverse path forwarding checks. However, routers maintain sense of the multicast trees at all times, allowing fast reconfiguration in the presence of network failures or unicast route changes. This is achieved by (i) keeping information on all upstream neighbors from which multicast data can be received, (ii) ensuring the reliable transmission and sequencing of control messages, and (iii) synchronizing the routing information immediately when a new router joins the network. The protocol was fully implemented in Python, and the implementation is publicly available. Finally, the correctness of the protocol was extensively validated using model checking, logical reasoning and tests performed over the protocol implementation.

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“…This makes it ideal for real-time applications such as surveillance or search and rescue. Beyond the explained protocols, there are an extensive variety of alternative protocols employed in FANETs, such as Predictive-OLSR (P-OLSR) [196], Mobility and Load-Aware OLSR (ML-OLSR) [197], Contention-Based OLSR (COLSR) [198], Modified-OLSR (M-OLSR) [199], Cartography-Enhanced OLSR (CE-OLSR) [200], Topology Broadcast Based on Reverse-Path Forwarding (TBRPF) [201], Fisheye State Routing (FSR) [202], and Babel [203].…”

Section: Contribution To Enhancing the Cognitive Capability Of Its Us...mentioning

confidence: 99%

“…Static LCAD [180], MLHR [181], DCR [182] Proactive OLSR [184], DSDV [185], BATMAN [186], BATMAN-ADV [195], DOLSR [187], P-OLSR [196], ML-OLSR [197], COLSR [198], M-OLSR [199], , TBRPF [201], FSR [202], Babel [203] Reactive DSR [130], AODV [204], TS-AODV [205], MAODV [215], AODVSEC [216] Hybrid ZRP [217], TORA [218], RTORA [219], HWMP [220], SHARP [221], HRP [222] Position-Based…”

Section: Topology-basedmentioning

confidence: 99%

Contribución al aumento de la capacidad cognitiva de los sistemas inteligentes de transporte mediante inteligencia artificial

Pérez¹

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The Babel Routing Protocol

Cited by 44 publications

References 2 publications

Exact Distributed Load Centrality Computation: Algorithms, Convergence, and Applications to Distance Vector Routing

Exact Distributed Load Centrality Computation: Algorithms, Convergence, and Applications to Distance Vector Routing

HPIM-DM: a fast and reliable dense-mode multicast routing protocol (extended version)

Contribución al aumento de la capacidad cognitiva de los sistemas inteligentes de transporte mediante inteligencia artificial

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