'Bloom filters' presentation slideshows

Memory Scaling: A Systems Architecture Perspective

Memory Scaling: A Systems Architecture Perspective. Onur Mutlu onur@cmu.edu May 27, 2013 IMW 2013. The Main Memory System. Main memory is a critical component of all computing systems : server, mobile, embedded, desktop, sensor

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Text and Web Search

Text and Web Search Text Databases and IR Text databases (document databases) Large collections of documents from various sources: news articles, research papers, books, digital libraries, e-mail messages, and Web pages, library database, etc. Information retrieval

1.2k views • 87 slides

Cooperative Web Caching

Cooperative Web Caching. Cooperative Caching. Previous work has shown that hit rate increases with population size [Duska et al. 97, Breslau et al. 98] However, single proxy caches have practical limits Load, network topology, organizational constraints

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DDoS, Spam, and Worms

DDoS, Spam, and Worms. Nick Feamster CS 7260 March 1, 2006. Administrivia. PS 2 Due today Quizzes will be graded by next week PS 3 is unlikely Will give demo of Virtual Network Infrastructure in class, in case anyone wants to use it for projects

599 views • 48 slides

Measurement Algorithms: Bloom Filters and Beyond

Measurement Algorithms: Bloom Filters and Beyond. George Varghese University of California, San Diego. Network Evolution?. Basic : stateless, transparent. Tools: protocol design (e.g., soft-state) 2. Active : customizable, re-configurable Tools : Code Safety (e.g., sandboxing)

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Extreme Performance with Very Big Data, Using Oracle Exadata X3-8

Extreme Performance with Very Big Data, Using Oracle Exadata X3-8. To understand over 400 million customers. Our scale as a group. Our vital statistics . 26,000,000 Lines of code - at the heart of our data solutions. 1,00,000,000 rows of data processed every week.

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Randomness and PSEudorandomness

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Exadata

Exadata. Agenda. Overview Smart Scan Predicate Filtering Column Projection Join Filtering Storage Indexes Hybrid Columnar Compression Flash Cache. Overview. Overview. What is the Smart Scan. Predicate Filtering Column Projection Function Offload Simple Joins Filtering

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Memory-Efficient 5D Packet Classification At 40 Gbps

Memory-Efficient 5D Packet Classification At 40 Gbps. Authors: Ioannis Papaefstathiou, and Vassilis Papaefstathiou Publisher: IEEE INFOCOM 2007 Presenter: Yu-Tso Chen Date: July, 09, 2008. Outline. Introduction BSPC Design Simulation Results. Introduction.

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Querying the Internet with PIER (PIER = Peer-to-peer Information Exchange and Retrieval)

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Web Caching and Consistent hashing

Web Caching and Consistent hashing. Aditya Akella 03/14 Supplemental Slides. Web caching. HTTP request. 1. 2. HTTP response. 1. Client1. 2. 1. 1. 2. Client2. 2. 1. Cache. 2. Server. Client3. Why web caching?. Client-server architecture is inherently unscalable

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Supercharge Your Searches

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An Empirical Evaluation of Extendible Arrays

An Empirical Evaluation of Extendible Arrays. Stelios Joannou & Rajeev Raman University of Leicester. Introduction. There is increasing use of in-memory RAM dynamic data structures (DS) to store large dynamic data sets. eg .: succinct DS, bloom filters and hash tables

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Bloom Filters

Bloom Filters. Kira Radinsky. Slides based on material from: Michael Mitzenmacher and Hanoch Levy Some modifications by Naama Kraus . Bloom Filter. Problem : membership testing Does item X belong to a set S ?

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Bloom Filters

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Rethinking Hardware Support for Network Analysis and Intrusion Prevention

Rethinking Hardware Support for Network Analysis and Intrusion Prevention. Vern Paxson ( ICSI ) Krste Asanovic ( MIT ) Sarang Dharmapurikar ( Nuova Systems ) John Lockwood ( WUSTL ) Ruoming Pang ( Princeton ) Robin Sommer ( ICSI ) Nicholas Weaver ( ICSI ) USENIX Hot Security July 31, 2006.

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Bloom Filters

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Approximate Caches for Packet Classification

Francis Chang, Kang Li, Wu-chang Feng {francis, kangli, wuchang}@cse.ogi.edu. The Motivation: Packet Classifiers are getting more complex and Flow Identifiers are getting more unwieldy (IPv4->IPv6) So Packet Classification Caches are getting bigger and slower The Story:

169 views • 1 slides