On Advice Complexity of the k-server Problem under Sparse Metrics

Sushmita Gupta; Shahin Kamali; Alejandro López-Ortiz

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arXiv · 1305.2108

On Advice Complexity of the k-server Problem under Sparse Metrics

Sushmita Gupta·Shahin Kamali·Alejandro López-Ortiz

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Abstract

We consider the k-server problem under the advice model of computation when the underlying metric space is sparse. On one side, we show that an advice of size {\Omega}(n) is required to obtain a 1-competitive algorithm for sequences of size n, even for the 2-server problem on a path metric of size N >= 5. Through another lower bound argument, we show that at least (n/2)(log {\alpha} - 1.22) bits of advice is required to obtain an optimal solution for metric spaces of treewidth {\alpha}, where 4 <= {\alpha} < 2k. On the other side, we introduce {\Theta}(1)-competitive algorithms for a wide range of sparse graphs, which require advice of (almost) linear size. Namely, we show that for graphs of size N and treewidth {\alpha}, there is an online algorithm which receives $O(n (log {\alpha} + log log N))$ bits of advice and optimally serves a sequence of length n. With a different argument, we show that if a graph admits a system of {\mu} collective tree (q,r)-spanners, then there is a (q+r)-competitive algorithm which receives O(n (log {\mu} + log log N)) bits of advice. Among other results, this gives a 3-competitive algorithm for planar graphs, provided with O(n log log N) bits of advice.

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Sushmita Gupta, Shahin Kamali, Alejandro López-Ortiz. 2013-05-09. On Advice Complexity of the k-server Problem under Sparse Metrics. https://arxiv.org/abs/1305.2108

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