具有不增加作业大小和缓冲区的半在线调度

IF 0.9 4区 数学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Leah Epstein, Hanan Zebedat-Haider
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引用次数: 0

摘要

这项工作考虑了在m台相同机器上的半在线调度版本,其目标是最小化完工时间。在这里研究的变体中,作业按照不增加的大小排序,大小为k的缓冲区最多可用于存储k个作业。每个到达的作业要么被放在缓冲区中等待分配,要么被立即分配给一台机器。我们证明了对于任意m和任意缓冲区大小,问题的竞争比有一个大于1的下界。为了补充这个消极的结果,我们设计了一个简单的算法,用于任何m,其竞争比随着缓冲区大小的增长而趋于1。使用这些结果,我们显示最佳竞争比率为\(1+\Theta (\frac{m}{k})\)。我们为m的小值提供了额外的边界。特别是,我们表明,对于\(m=2\), \(k=1\)与没有缓冲区的情况没有什么不同,而\(k=2\)允许提高竞争比。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semi-online scheduling with non-increasing job sizes and a buffer

This work considers a semi-online version of scheduling on m identical machines, where the objective is to minimize the makespan. In the variant studied here, jobs are presented sorted by non-increasing sizes, and a buffer of size k is available for storing at most k jobs. Every arriving job has to be either placed into the buffer until its assignment, or else it has to be assigned immediately to a machine. We prove a lower bound greater than 1 on the competitive ratio of the problem for any m and any buffer size. To complement this negative result, we design a simple algorithm for any m whose competitive ratio tends to 1 as the buffer size grows. Using those results, we show the best possible competitive ratio is \(1+\Theta (\frac{m}{k})\). We provide additional bounds for small values of m. In particular, we show that for \(m=2\) the case \(k=1\) is not different from the case without a buffer, while \(k=2\) admits an improved competitive ratio.

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来源期刊
Journal of Combinatorial Optimization
Journal of Combinatorial Optimization 数学-计算机:跨学科应用
CiteScore
2.00
自引率
10.00%
发文量
83
审稿时长
6 months
期刊介绍: The objective of Journal of Combinatorial Optimization is to advance and promote the theory and applications of combinatorial optimization, which is an area of research at the intersection of applied mathematics, computer science, and operations research and which overlaps with many other areas such as computation complexity, computational biology, VLSI design, communication networks, and management science. It includes complexity analysis and algorithm design for combinatorial optimization problems, numerical experiments and problem discovery with applications in science and engineering. The Journal of Combinatorial Optimization publishes refereed papers dealing with all theoretical, computational and applied aspects of combinatorial optimization. It also publishes reviews of appropriate books and special issues of journals.
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