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Work-Competitive Scheduling on Task Dependency Graphs

机译:任务依赖图上的工作竞争性调度

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A fundamental problem in distributed computing is the task of cooperatively executing a given set of t tasks by p asynchronous processors where the communication medium is dynamic and subject to failures. Also known as do-all, this problem been studied extensively in various distributed settings. In [2], the authors consider a partitionable network scenario and analyze the competitive performance of a randomized scheduling algorithm for the case where the tasks to be completed are independent of each other. In this paper, we study a natural extension of this problem where the tasks have dependencies among them. We present a simple randomized algorithm for p processors cooperating to perform t known tasks where the dependencies between them are defined by a k-partite task dependency graph and additionally these processors are subject to a dynamic communication medium. By virtue of the problem setting, we pursue competitive analysis where the performance of our algorithm is measured against that of the omniscient offline algorithm which has complete knowledge of the dynamics of the communication medium. We show that the competitive ratio of our algorithm is tight and depends on the dynamics of the communication medium viz. the computational width defined in [2] and also on the number of partitions of the task dependency graph.
机译:分布式计算中的一个基本问题是任务,其中p个异步处理器协同执行一组给定的t个任务,其中通信介质是动态的,并且容易出错。也称为“全部解决”,在各种分布式环境中对此问题进行了广泛的研究。在[2]中,作者考虑了可分割的网络场景,并针对要完成的任务彼此独立的情况分析了随机调度算法的竞争性能。在本文中,我们研究了任务之间具有依赖性的问题的自然扩展。我们为p个处理器合作执行t个已知任务提供了一种简单的随机算法,其中之间的依赖关系由k部分任务依赖关系图定义,此外,这些处理器还受动态通信介质的约束。通过问题设置,我们进行竞争性分析,在该分析中,我们的算法的性能与无所不知的脱机算法的性能进行了比较,后者对通信介质的动态性有全面的了解。我们证明了我们算法的竞争率是紧密的,并且取决于通信介质的动力学。 [2]中定义的计算宽度,以及任务依赖图的分区数。

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