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A Distributed Event-Based System based on Compressed Fragmented-Iterated Bloom Filters

机译:基于压缩碎片迭代布隆过滤器的基于事件的分布式系统

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In this research, we propose the construction of a new architecture of Fragmented-Iterated Bloom Filters to redirect events of a distributed event-based system. We introduce two novel structures of Bloom Filters: Fragmented Bloom Filters and Iterated Bloom Filters. The aim of Iterated Bloom Filters is to discard single events that do not match any subscription. Then, Fragmented Bloom Filters deal with conjunctive and disjunctive set of events. Whether a match is found at the Fragmented Bloom Filters the publication is forwarded. Our strategy is compared to the alternative one relying on Standard Bloom Filters. The results show that Fragmented Bloom Filters lead to save memory and computational resources at the membership test. Moreover, we show that there is no memory cost for dividing a Bloom Filter in smaller Bloom Filters using the same: (1) number of elements to insen and (2) probability of false positives. Then, we prove that fast hash functions required for Fragmented Bloom Filters present a lower complexity that those required for Standard Bloom Filters. Additionally, we determine that the double hashing technique does not result in a lower complexity. Afterwards, we show that the construction of a structure of Iterated Bloom Filters using an Iterated Hash Function reduces the complexity because smaller filters and less hash functions are required. Furthermore, if information is structured in a tree Iterated Bloom Filters decrease the probability of false positives. We also focus on the improvement of data exchange for updating Fragmented-Iterated Bloom Filters between nodes. The goal is to reduce data transmitted. For this purpose, we study the effect of compressing Fragmented-Iterated Bloom Filters. The main benefit of Compressed Bloom Filters is that they transmit less bits. Therefore, less bandwidth is required and the latency of the network is reduced. The choice of Compressed Fragmented Bloom Filters preserves all these positive effects by limiting the number of transmitted bits due to its flexible structure. Besides, Compressed Iterated Bloom Filters also decrease computation.
机译:在这项研究中,我们提出了分段迭代布隆过滤器的新架构的构造,以重定向基于分布式事件的系统的事件。我们介绍了布隆过滤器的两种新颖结构:分段布隆过滤器和迭代布隆过滤器。迭代布隆过滤器的目的是丢弃与任何预订都不匹配的单个事件。然后,碎片化布隆过滤器处理连接的和分离的事件集。是否在“碎片布隆过滤器”中找到匹配项,将转发出版物。我们的策略与依赖于标准布隆过滤器的另一种策略进行了比较。结果表明,在成员资格测试中,碎片化布隆过滤器可节省内存和计算资源。此外,我们表明使用相同的方法将较小的Bloom过滤器划分为较小的Bloom过滤器不会产生存储成本:(1)要感应的元素数量和(2)假阳性的概率。然后,我们证明了分段布隆过滤器所需的快速哈希函数所呈现的复杂度低于标准布隆过滤器所需的复杂度。此外,我们确定双重哈希技术不会导致较低的复杂度。此后,我们表明使用迭代哈希函数构造迭代Bloom过滤器的结构可降低复杂性,因为需要较小的过滤器和较少的哈希函数。此外,如果信息是树状结构,则迭代布隆过滤器可降低误报的可能性。我们还专注于数据交换的改进,以更新节点之间的分段迭代布隆过滤器。目的是减少传输的数据。为此,我们研究了压缩片段迭代布隆过滤器的效果。压缩布隆过滤器的主要优点是它们传输的位更少。因此,需要较少的带宽并且减少了网络的等待时间。压缩碎片布鲁姆滤波器的选择由于其灵活的结构而限制了传输位的数量,从而保留了所有这些积极效果。此外,压缩迭代布隆过滤器也会减少计算量。

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