Time-Optimal k-Server

The time-optimal $k$-server problem minimizes the time spent serving all requests instead of the distances traveled. We give a lower bound of $2k-1$ on the competitive ratio of any deterministic online algorithm for this problem, which coincides with the best known upper bound on the competitive ratio achieved by the work-function algorithm for the classical $k$-server problem. We provide further lower bounds of $k+1$ for all Euclidean spaces and $k$ for uniform metric spaces. For the latter, we give a matching $k$-competitive deterministic algorithm. Our most technical result, proven by applying Yao's principle to a suitable instance distribution on a specifically constructed metric space, is a lower bound of $k+\mathcal{O}(\log k)$ that holds even for randomized algorithms, which contrasts with the best known lower bound for the classical problem that remains polylogarithmic. With this paper, we hope to initiate a further study of this natural yet neglected problem.

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Publication Details

Journal
Repository for Publications and Research Data (ETH Zurich)
Published
2025-03-07
DOI
https://doi.org/10.3929/ethz-c-000792000
Primary Topic
Optimization and Search Problems
Type
article
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article

Time-Optimal k-Server

Moritz Stocker, Fabian Frei, Dennis Komm, Philip Whittington
Repository for Publications and Research Data (ETH Zurich)
Optimization and Search Problems
article

Time-Optimal k-Server

Moritz Stocker, Fabian Frei, Dennis Komm, Philip Whittington
article en

Abstract

The time-optimal $k$-server problem minimizes the time spent serving all requests instead of the distances traveled. We give a lower bound of $2k-1$ on the competitive ratio of any deterministic online algorithm for this problem, which coincides with the best known upper bound on the competitive ratio achieved by the work-function algorithm for the classical $k$-server problem. We provide further lower bounds of $k+1$ for all Euclidean spaces and $k$ for uniform metric spaces. For the latter, we give a matching $k$-competitive deterministic algorithm. Our most technical result, proven by applying Yao's principle to a suitable instance distribution on a specifically constructed metric space, is a lower bound of $k+\mathcal{O}(\log k)$ that holds even for randomized algorithms, which contrasts with the best known lower bound for the classical problem that remains polylogarithmic. With this paper, we hope to initiate a further study of this natural yet neglected problem.

Repository for Publications and Research Data (ETH Zurich)
Openalex Percentile: Top 31%
Optimization and Search Problems
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Time-Optimal k-Server — Moritz Stocker, Fabian Frei, et al. · Repository for Publications and Research Data (ETH Zurich) (2025) | TGRS Research Map | TGRS