Brief Announcement: Scheduling Jobs for Minimum Span: Improved Bounds and Learning-Augmented Algorithms

被引：0

作者：

Liu, Mozhengfu ^{[1
]}

Tang, Xueyan ^{[2
]}

机构：

[1] Northwestern Univ, Evanston, IL 60201 USA

[2] Nanyang Technol Univ, Singapore, Singapore

来源：

PROCEEDINGS OF THE 36TH ACM SYMPOSIUM ON PARALLELISM IN ALGORITHMS AND ARCHITECTURES, SPAA 2024 | 2024年

关键词：

scheduling; span; competitive ratio; algorithms with predictions;

D O I：

10.1145/3626183.3660263

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We study a flexible job scheduling problem. A set of jobs is released over time, each with a starting deadline and a processing length. The jobs are to be started by an online scheduler no later than their starting deadlines and will run nonpreemptively. The objective is to minimize the span - the time duration for which at least one job is running. We present a new lower bound of 4 on the competitiveness of any online algorithm. We also establish tight competitiveness bounds in the learning-augmented setting of the problem.

引用

页码：141 / 143

页数：3

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