Push-Down Trees: Optimal Self-Adjusting Complete Trees

Chen Avin; Kaushik Mondal; Stefan Schmid

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arXiv · 1807.04613

Push-Down Trees: Optimal Self-Adjusting Complete Trees

Chen Avin·Kaushik Mondal·Stefan Schmid

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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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BibTeX RIS

Chen Avin, Kaushik Mondal, Stefan Schmid. 2018-07-12. Push-Down Trees: Optimal Self-Adjusting Complete Trees. https://arxiv.org/abs/1807.04613

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