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
收藏
页码：141 / 143
页数：3
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