An Exact Algorithm for the Linear Tape Scheduling Problem

TL;DR

This paper introduces an exact polynomial-time algorithm for the linear tape scheduling problem, improving request ordering to enhance data access performance in tape storage systems, and compares it with existing solutions using real-world data.

Contribution

It presents a novel exact algorithm for an NP-hard problem, refined with U-turn penalties, and offers low-cost variants for practical use, validated on real storage system logs.

Findings

The exact algorithm outperforms previous heuristics.

Low-cost variants provide near-optimal solutions efficiently.

Reproducible implementation and dataset are publicly available.

Abstract

Magnetic tapes are often considered as an outdated storage technology, yet they are still used to store huge amounts of data. Their main interests are a large capacity and a low price per gigabyte, which come at the cost of a much larger file access time than on disks. With tapes, finding the right ordering of multiple file accesses is thus key to performance. Moving the reading head back and forth along a kilometer long tape has a non-negligible cost and unnecessary movements thus have to be avoided. However, the optimization of tape request ordering has then rarely been studied in the scheduling literature, much less than I/O scheduling on disks. For instance, minimizing the average service time for several read requests on a linear tape remains an open question. Therefore, in this paper, we aim at improving the quality of service experienced by users of tape storage systems, and not only the peak performance of such systems. To this end, we propose a reasonable polynomial-time exact algorithm while this problem and simpler variants have been conjectured NP-hard. We also refine the proposed model by considering U-turn penalty costs accounting for inherent mechanical accelerations. Then, we propose low-cost variants of our optimal algorithm by restricting the solution space, yet still yielding an accurate suboptimal solution. Finally, we compare our algorithms to existing solutions from the literature on logs of the mass storage management system of a major datacenter. This allows us to assess the quality of previous solutions and the improvement achieved by our low-cost algorithms. Aiming for reproducibility, we make available the complete implementation of the algorithms used in our evaluation, alongside the dataset of tape requests that is, to the best of our knowledge, the first of its kind to be publicly released.