Self-adjusting Linear Networks

  • Chen Avin
  • Ingo van DuijnEmail author
  • Stefan Schmid
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11914)


Emerging networked systems become increasingly flexible, reconfigurable, and “self-\(*\)”. 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 list 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 it generalizes. Our main results are a \(\varOmega (\log {n})\) lower bound on the competitive ratio, and a (distributed) online algorithm that is \(\mathcal {O}(\log {n})\)-competitive if the communication requests are issued according to a linear order.


Self-adjusting datastructures Competitive analysis Distributed algorithms Communication networks 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Electrical and Computer EngineeringBen Gurion University of the NegevBeershebaIsrael
  2. 2.Department of Computer ScienceAalborg UniversityAalborgDenmark
  3. 3.Faculty of Computer ScienceUniversity of ViennaViennaAustria

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