arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.13891cs.DS
收藏

优先和通信约束下最小化最大完工时间的分层算法

Hierarchy-Based Algorithms for Minimizing Makespan under Precedence and Communication Constraints

Janardhan Kulkarni, Shi Li, Jakub Tarnawski, Minwei Ye

摘要:

考虑了一类经典的带优先约束的工件排序问题,目标函数为极小化完工时间。当机器数量为常数时,理解问题的精确可近似性是调度理论中的一个众所周知的问题。事实上,Garey和Johnson的经典著作中有一个突出的开放问题,即在3台机器和单位长度工件的情况下,这个问题是否是NP难的。Levey和Rothvoss在最近的一次突破中,对工件长度为单位的情况给出了一个近似于准多项式时间的$(1+ε)$ -近似算法。然而,一个更困难的情况下,工作有任意的处理长度仍然开放。我们在这个更普遍的问题上取得了进展。我们发现,存在一个$(1+ε)$ -近似算法(与类似的运行时间的Levey和Rothvoss)的非迁移设置:当每个作业都被安排完全在一台机器上,但在一台机器上的作业不需要在连续的时间步长。此外,我们还表明,我们的算法框架推广到另一个经典的情况下,随着优先约束,工作也有通信延迟的限制。这两个基本问题都与数据中心调度的实践高度相关。

英文摘要:

We consider the classic problem of scheduling jobs with precedence constraints on a set of identical machines to minimize the makespan objective function. Understanding the exact approximability of the problem when the number of machines is a constant is a well-known question in scheduling theory. Indeed, an outstanding open problem from the classic book of Garey and Johnson asks whether this problem is NP-hard even in the case of 3 machines and unit-length jobs. In a recent breakthrough, Levey and Rothvoss gave a $(1+ε)$-approximation algorithm, which runs in nearly quasi-polynomial time, for the case when job have unit lengths. However, a substantially more difficult case where jobs have arbitrary processing lengths has remained open. We make progress on this more general problem. We show that there exists a $(1+ε)$-approximation algorithm (with similar running time as that of Levey and Rothvoss) for the non-migratory setting: when every job has to be scheduled entirely on a single machine, but within a machine the job need not be scheduled during consecutive time steps. Further, we also show that our algorithmic framework generalizes to another classic scenario where, along with the precedence constraints, the jobs also have communication delay constraints. Both of these fundamental problems are highly relevant to the practice of datacenter scheduling.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑