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Broadcast disks with polynomial cost functions

机译:具有多项式成本函数的广播磁盘

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In broadcast disk systems, information is broadcast in a shared medium. When a client needs an item from the disk, it waits until that item is broadcast. The fundamental algorithmic problem for such systems is to determine the broadcast schedule based on the demand probability of items, and the cost incurred to the system by clients waiting. The goal is to minimize the mean access cost of a random client. Typically, it was assumed that the access cost is proportional to the waiting time. In this paper, we ask what are the best broadcast schedules for access costs which are arbitrary polynomials in the waiting time. These may serve as reasonable representations of reality in many cases, where the "patience" of a client is not necessarily proportional to its waiting time. We present an asymptotically optimal algorithm for a fluid model, where the bandwidth may be divided to allow for fractional concurrent broadcasting. This algorithm, besides being justified in its own right, also serves as a lower bound against which we test known discrete algorithms. We show that the Greedy algorithm has the best performance in most cases. Then we show that the performance of other algorithms deteriorate exponentially with the degree of the cost polynomial and approach the fractional solution for sub-linear cost. Finally, we study the quality of approximating the greedy schedule by a finite schedule.
机译:在广播磁盘系统中,信息是在共享介质中广播的。当客户端需要磁盘中的项目时,它会等待直到该项目被广播为止。这种系统的基本算法问题是,根据项目的需求概率以及等待客户的系统成本来确定广播时间表。目标是最大程度地减少随机客户端的平均访问成本。通常,假设访问成本与等待时间成正比。在本文中,我们问访问成本最好的广播时间表是什么,它是等待时间中的任意多项式。在许多情况下,这些可以用作对现实的合理表示,其中客户的“耐心”不一定与其等待时间成正比。我们提出了一种针对流体模型的渐近最优算法,其中带宽可以划分为允许部分并发广播。该算法除了本身具有合理性外,还可以作为测试已知离散算法的下限。我们表明,在大多数情况下,贪婪算法具有最佳性能。然后,我们证明了其他算法的性能随成本多项式的次数呈指数级下降,并且接近于次线性成本的分数解。最后,我们研究了通过有限进度表近似贪婪进度表的质量。

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