4.7 Article

A Survey of Fast-Recovery Mechanisms in Packet-Switched Networks

期刊

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS
卷 23, 期 2, 页码 1253-1301

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/COMST.2021.3063980

关键词

Routing; IP networks; Multiprotocol label switching; Resilience; Internet; Tutorials; Delays; Fast reroute; network resilience; data plane

资金

  1. COST Action by COST (European Cooperation in Science and Technology) [CA15127]
  2. Vienna Science and Technology Fund (WWTF) Project, Fast and Quantitative What-If Analysis for Dependable Communication Networks (WHATIF) [20202024, ICT19-045]
  3. KTH Digital Futures Center
  4. Ericsson Research, Hungary
  5. Austrian-Hungarian Joint Research Project [FWF-30668/OTKA-135606]

向作者/读者索取更多资源

This survey provides a systematic overview of packet-based fast-recovery mechanisms in the data plane in modern packet-switched communication networks, focusing on concepts and structured around different networking technologies. It examines the evolution of fast-recovery standards and mechanisms over time, identifies fundamental principles and algorithms, and proposes future research directions.
In order to meet their stringent dependability requirements, most modern packet-switched communication networks support fast-recovery mechanisms in the data plane. While reactions to failures in the data plane can be significantly faster compared to control plane mechanisms, implementing fast recovery in the data plane is challenging, and has recently received much attention in the literature. This survey presents a systematic, tutorial-like overview of packet-based fast-recovery mechanisms in the data plane, focusing on concepts but structured around different networking technologies, from traditional link-layer and IP-based mechanisms, over BGP and MPLS to emerging software-defined networks and programmable data planes. We examine the evolution of fast-recovery standards and mechanisms over time, and identify and discuss the fundamental principles and algorithms underlying different mechanisms. We then present a taxonomy of the state of the art, summarize the main lessons learned, and propose a few concrete future directions.

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