Self-adjusting linear networks

Chen Avin, Ingo van Duijn, Stefan Schmid

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Emerging networked systems become increasingly flexible and “reconfigurable”. This introduces an opportunity to adjust networked systems in a demand-aware manner, leveraging spatial and temporal locality in the workload for online optimizations. However, it also introduces a tradeoff: while more frequent adjustments can improve performance, they also entail higher reconfiguration costs. This paper initiates the formal study of linear networks which self-adjust to the demand in an online manner, striking a balance between the benefits and costs of reconfigurations. We show that the underlying algorithmic problem can be seen as a distributed generalization of the classic dynamic list update problem known from self-adjusting datastructures: in a network, requests can occur between node pairs. This distributed version turns out to be significantly harder than the classical problem in generalizes. Our main results are a Ω(log n) lower bound on the competitive ratio, and a (distributed) online algorithm that is O(log n)- -competitive if the communication requests are issued according to a linear order.

Original languageEnglish
Title of host publicationStructural Information and Communication Complexity - 26th International Colloquium, SIROCCO 2019, Proceedings
EditorsKeren Censor-Hillel, Michele Flammini
PublisherSpringer Verlag
Number of pages4
ISBN (Print)9783030249212
StatePublished - 1 Jan 2019
Event26th International Colloquium on Structural Information and Communication Complexity, SIROCCO 2019 - L'Aquila, Italy
Duration: 1 Jul 20194 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11639 LNCS


Conference26th International Colloquium on Structural Information and Communication Complexity, SIROCCO 2019


  • Communication networks
  • Competitive analysis
  • Distributed algorithms
  • Self-adjusting datastructures

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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