A Power Management Proxy with a New Best-of-N Bloom Filter Design to Reduce False Positives

M. Jimeno, Kenneth J. Christensen, A. Roginsky
{"title":"A Power Management Proxy with a New Best-of-N Bloom Filter Design to Reduce False Positives","authors":"M. Jimeno, Kenneth J. Christensen, A. Roginsky","doi":"10.1109/PCCC.2007.358887","DOIUrl":null,"url":null,"abstract":"Bloom filters are a probabilistic data structure used to evaluate set membership. A group of hash functions are used to map elements into a bloom filter and to test elements for membership. In this paper, we propose using multiple groups of hash functions and selecting the group that generates the bloom filter instance with the smallest number of bits set to I. We evaluate the performance of this new Best-of-N method using order statistics and an actual implementation. Our analysis shows that significant reduction in the probability of a false positive can be achieved. We also propose and evaluate a new method that uses a random number generator (RNG) to generate multiple hashes from one initial \"seed\" hash. This RNG method (motivated by a method from Kirsch and Mitzenmacher) makes the computational expense of the Best-of-N method very modest. The target application is a power management proxy for P2P applications executing in a resource-constrained \"SmartNIC\".","PeriodicalId":356565,"journal":{"name":"2007 IEEE International Performance, Computing, and Communications Conference","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Performance, Computing, and Communications Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PCCC.2007.358887","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17

Abstract

Bloom filters are a probabilistic data structure used to evaluate set membership. A group of hash functions are used to map elements into a bloom filter and to test elements for membership. In this paper, we propose using multiple groups of hash functions and selecting the group that generates the bloom filter instance with the smallest number of bits set to I. We evaluate the performance of this new Best-of-N method using order statistics and an actual implementation. Our analysis shows that significant reduction in the probability of a false positive can be achieved. We also propose and evaluate a new method that uses a random number generator (RNG) to generate multiple hashes from one initial "seed" hash. This RNG method (motivated by a method from Kirsch and Mitzenmacher) makes the computational expense of the Best-of-N method very modest. The target application is a power management proxy for P2P applications executing in a resource-constrained "SmartNIC".
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
一种采用新型Best-of-N布隆滤波器设计以减少误报的电源管理代理
布隆过滤器是一种用于评估集合隶属度的概率数据结构。一组散列函数用于将元素映射到bloom过滤器并测试元素的成员关系。在本文中,我们建议使用多组哈希函数,并选择生成布隆过滤器实例的组,其位数设置为i最小。我们使用顺序统计和实际实现来评估这种新的Best-of-N方法的性能。我们的分析表明,可以显著降低假阳性的概率。我们还提出并评估了一种使用随机数生成器(RNG)从一个初始“种子”哈希生成多个哈希的新方法。这种RNG方法(受Kirsch和Mitzenmacher的一种方法的启发)使得n最优方法的计算费用非常适中。目标应用程序是在资源受限的“SmartNIC”中执行P2P应用程序的电源管理代理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Profiling Database Application to Detect SQL Injection Attacks Protecting First-Level Responder Resources in an IP-based Emergency Services Architecture Scalable and Decentralized Content-Aware Dispatching in Web Clusters CT-RBAC: A Temporal RBAC Model with Conditional Periodic Time Mobility Support of Multi-User Services in Next Generation Wireless Systems
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1