分布式算法的组合竞争性
Compositional competitiveness for distributed algorithms
摘要：
我们定义了一个衡量分布式算法的竞争力的性能的基础上的吞吐量，任务的数量，一个算法可以在一个固定的工作量。这种新的测量补充了Ajtai等人的延迟测量，它测量算法可以多快地完成在指定时间开始的任务。吞吐量测量的新特征，将其与延迟测量区分开来，是它是合成的：它支持相对于一类子程序具有竞争性的算法的概念，具有相对于一类子程序具有k-竞争性的算法与该类的l-竞争性成员相结合的属性，给出了具有kl-竞争性的组合算法。特别是，我们证明了一类收集操作的算法的吞吐量竞争力，其中一组n个进程中的每一个获得存储在n个寄存器的数组中的所有值。收集是一个基本的建筑块的各种共享内存的分布式算法，我们表明，这样的算法是有竞争力的相对收集。在这些算法中插入竞争收集给出了通过使用一般构造的组合获得的竞争分布式算法的第一个例子。
英文摘要：
We define a measure of competitive performance for distributed algorithms based on throughput, the number of tasks that an algorithm can carry out in a fixed amount of work. This new measure complements the latency measure of Ajtai et al., which measures how quickly an algorithm can finish tasks that start at specified times. The novel feature of the throughput measure, which distinguishes it from the latency measure, is that it is compositional: it supports a notion of algorithms that are competitive relative to a class of subroutines, with the property that an algorithm that is k-competitive relative to a class of subroutines, combined with an l-competitive member of that class, gives a combined algorithm that is kl-competitive. In particular, we prove the throughput-competitiveness of a class of algorithms for collect operations, in which each of a group of n processes obtains all values stored in an array of n registers. Collects are a fundamental building block of a wide variety of shared-memory distributed algorithms, and we show that several such algorithms are competitive relative to collects. Inserting a competitive collect in these algorithms gives the first examples of competitive distributed algorithms obtained by composition using a general construction.
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