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Anomalies and Similarities Among Consensus Numbers of Variously-Relaxed Queues

机译:各种松弛队列的共识数量之间的异常和相似性

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Shared data structures are a basic building block in distributed computing, but can be expensive to implement. One way to circumvent the high implementation cost of linearizability is to relax the sequential specification of the data type. This gives up some guarantees, for instance on the ordering of data elements, as a tradeoff against performance. We want to explore the effects of this tradeoff on the computational power of the shared data structures. In this paper, we characterize the effects of three different types of relaxation, chosen from the literature, on the computational power of FIFO queues. By parametrically relaxing each of the three operations on a queue (Enqueue, Dequeue, Peek), we obtain an infinite 3-dimensional space for each type of relaxation. We find the consensus number, a standard measure of the computational power of shared data types, of each point in these spaces, completely describing the effect of these three types of relaxation on the computational power of queues.
机译:共享数据结构是分布式计算的基本构建块,但是实现起来可能会很昂贵。规避线性化的高昂实现成本的一种方法是放宽数据类型的顺序规范。这就放弃了一些保证,例如在数据元素的排序上,作为对性能的折衷。我们想探索这种折衷对共享数据结构的计算能力的影响。在本文中,我们描述了从文献中选择的三种不同类型的松弛对FIFO队列的计算能力的影响。通过对队列中的三个操作(“入队”,“出队”,“窥视”)进行参数放宽,我们为每种放宽类型获得了无限的3维空间。我们找到这些空间中每个点的共识数,这是共享数据类型的计算能力的标准度量,完全描述了这三种松弛类型对队列计算能力的影响。

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