On Approximate Sequencing Policies for Linear Storage Devices

TL;DR

This paper analyzes and develops new algorithms for sequencing file requests in linear storage devices like tapes, aiming to improve data retrieval efficiency through theoretical and numerical performance evaluations.

Contribution

It introduces novel approximation algorithms for deterministic, stochastic, and online settings, with proven performance guarantees, for sequencing policies in linear storage devices.

Findings

Traditional FIFO policies perform poorly in deterministic settings.

New algorithms achieve constant-factor approximation guarantees.

Proposed methods outperform existing policies in average reading times.

Abstract

This paper investigates sequencing policies for file reading requests in linear storage devices, such as magnetic tapes. Tapes are the technology of choice for long-term storage in data centers due to their low cost and reliability. However, their physical structure imposes challenges to data retrieval operations reflected in classic optimization and operations research problems. In this work, we provide a theoretical and numerical performance analysis of low-complexity algorithms under deterministic, stochastic, and online settings, which are key in practice due to their interpretability and the large scale of existing data services. In the deterministic setting, we show that traditional policies, such as first-in first-out (FIFO), have arbitrarily poor performance, and we develop and investigate new constant-factor approximations. For the stochastic setting, we present a fully polynomial-time approximation scheme that weighs files based on their access frequencies. Finally, we investigate an online extension and propose a new algorithm with constant competitive-factor guarantees. Our numerical analysis on synthetic and real-world data suggest that the proposed algorithms may significantly outperform policies currently adopted in practice with respect to average reading times.