Fast Estimation of Aggregates in Unstructured Networks

Carlos Baquero, Paulo Sérgio Almeida, R. Menezes
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引用次数: 25

Abstract

Aggregation of data values plays an important role on distributed computations, in particular over peer-to-peer and sensor networks, as it can provide a summary of some global system property and direct the actions of self-adaptive distributed algorithms. Examples include using estimates of the network size to dimension distributed hash tables or estimates of the average system load to direct load-balancing. Distributed aggregation using non-idempotent functions, like sums, is not trivial as it is not easy to prevent a given value from being accounted for multiple times; this is especially the case if no centralized algorithms or global identifiers can be used. This paper introduces Extrema Propagation, a probabilistic technique for distributed estimation of the sum of positive real numbers. The technique relies on the exchange of duplicate insensitive messages and can be applied in flood and/or epidemic settings, where multi-path routing occurs; it is tolerant of message loss; it is fast, as the number of message exchange steps equals the diameter; and it is fully distributed, with no single point of failure and the result produced at every node.
非结构化网络中聚合的快速估计
数据值的聚合在分布式计算中起着重要的作用,特别是在点对点和传感器网络中,因为它可以提供一些全局系统属性的摘要,并指导自适应分布式算法的操作。示例包括使用网络大小的估计来划分分布式哈希表的维度,或使用平均系统负载的估计来指导负载平衡。使用非幂等函数的分布式聚合,如和,不是微不足道的,因为它不容易防止一个给定的值被多次计算;如果不能使用集中式算法或全局标识符,情况尤其如此。本文介绍了极值传播——一种用于正实数和分布估计的概率技术。该技术依赖于重复的不敏感信息的交换,可以应用于洪水和/或流行病环境中,其中存在多路径路由;它可以容忍消息丢失;它是快速的,因为消息交换步骤的数量等于直径;它是完全分布式的,没有单点故障,每个节点都产生结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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