SeedTree: A Dynamically Optimal and Local Self-Adjusting Tree

Arash Pourdamghani, Chen Avin, Robert Sama, Stefan Schmid

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

1 Scopus citations


We consider the fundamental problem of designing a self-adjusting tree, which efficiently and locally adapts itself towards the demand it serves (namely accesses to the items stored by the tree nodes), striking a balance between the benefits of such adjustments (enabling faster access) and their costs (reconfigurations). This problem finds applications, among others, in the context of emerging demand-aware and reconfigurable datacenter networks and features connections to self-adjusting data structures. Our main contribution is SeedTree, a dynamically optimal self-adjusting tree which supports local (i.e., greedy) routing, which is particularly attractive under highly dynamic demands. SeedTree relies on an innovative approach which defines a set of unique paths based on randomized item addresses, and uses a small constant number of items per node. We complement our analytical results by showing the benefits of SeedTree empirically, evaluating it on various synthetic and real-world communication traces.

Original languageEnglish
Title of host publicationINFOCOM 2023 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9798350334142
StatePublished - 1 Jan 2023
Event42nd IEEE International Conference on Computer Communications, INFOCOM 2023 - Hybrid, New York City, United States
Duration: 17 May 202320 May 2023

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


Conference42nd IEEE International Conference on Computer Communications, INFOCOM 2023
Country/TerritoryUnited States
CityHybrid, New York City


  • Online algorithms
  • Reconfigurable datacenters
  • Self-adjusting data structure

ASJC Scopus subject areas

  • General Computer Science
  • Electrical and Electronic Engineering


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