Dynamically Optimal Self-Adjusting Single-Source Tree Networks

Dynamically Optimal Self-Adjusting Single-Source Tree Networks

Abstract

This paper studies a fundamental algorithmic problem related to the design of demand-aware networks: networks whose topologies adjust toward the traffic patterns they serve, in an online manner. The goal is to strike a tradeoff between the benefits of such adjustments (shorter routes) and their costs (reconfigurations). In particular, we consider the problem of designing a self-adjusting tree network which serves single-source, multi-destination communication. The problem has interesting connections to self-adjusting datastructures. We present two constant-competitive online algorithms for this problem, one randomized and one deterministic. Our approach is based on a natural notion of Most Recently Used (MRU) tree, maintaining a working set. We prove that the working set is a cost lower bound for any online algorithm, and then present a randomized algorithm Random-Push which approximates such an MRU tree at low cost, by pushing less recently used communication partners down the tree, along a random walk. Our deterministic algorithm Move-Half does not directly maintain an MRU tree, but its cost is still proportional to the cost of an MRU tree, and also matches the working set lower bound.

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Authors
  • Avin, Chen
  • Mondal, Kaushik
  • Schmid, Stefan
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Supplemental Material
Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
14th Latin American Theoretical Informatics Symposium (LATIN)
Divisions
Communication Technologies
Subjects
Informatik Allgemeines
Event Location
University of Sao Paulo, Sao Paulo, Brazil
Event Type
Conference
Event Dates
May 2020
Date
2020
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