457,051 research outputs found
Clonal and Common and Lilac Protocol
The purpose of this resource is to have students observe their lilac plants and identify the five phenophases (first leaf, full or 95% leafed, first bloom, full bloom and end of bloom) for each lilac plant. During the growing season, students will observe their lilac plants and identify the five phenophases (first leaf, full or 95% leafed, first bloom, full bloom and end of bloom) for each lilac plant. Educational levels: Primary elementary, Intermediate elementary, Middle school, High school
Don't Thrash: How to Cache Your Hash on Flash
This paper presents new alternatives to the well-known Bloom filter data
structure. The Bloom filter, a compact data structure supporting set insertion
and membership queries, has found wide application in databases, storage
systems, and networks. Because the Bloom filter performs frequent random reads
and writes, it is used almost exclusively in RAM, limiting the size of the sets
it can represent. This paper first describes the quotient filter, which
supports the basic operations of the Bloom filter, achieving roughly comparable
performance in terms of space and time, but with better data locality.
Operations on the quotient filter require only a small number of contiguous
accesses. The quotient filter has other advantages over the Bloom filter: it
supports deletions, it can be dynamically resized, and two quotient filters can
be efficiently merged. The paper then gives two data structures, the buffered
quotient filter and the cascade filter, which exploit the quotient filter
advantages and thus serve as SSD-optimized alternatives to the Bloom filter.
The cascade filter has better asymptotic I/O performance than the buffered
quotient filter, but the buffered quotient filter outperforms the cascade
filter on small to medium data sets. Both data structures significantly
outperform recently-proposed SSD-optimized Bloom filter variants, such as the
elevator Bloom filter, buffered Bloom filter, and forest-structured Bloom
filter. In experiments, the cascade filter and buffered quotient filter
performed insertions 8.6-11 times faster than the fastest Bloom filter variant
and performed lookups 0.94-2.56 times faster.Comment: VLDB201
Low-power bloom filter architecture for deep packet inspection
Bloom filters are frequently used to identify malicious content like viruses in high speed networks. However, architectures proposed to implement Bloom filters are not power efficient. In this letter, we propose a new Bloom filter architecture that exploits the well-known pipelining technique. Through power analysis we show that pipelining can reduce the power consumption of Bloom filters up to 90%, which leads to the energy-efficient implementation of intrusion detection systems. Ā© 2006 IEEE
Preventing DDoS using Bloom Filter: A Survey
Distributed Denial-of-Service (DDoS) is a menace for service provider and
prominent issue in network security. Defeating or defending the DDoS is a prime
challenge. DDoS make a service unavailable for a certain time. This phenomenon
harms the service providers, and hence, loss of business revenue. Therefore,
DDoS is a grand challenge to defeat. There are numerous mechanism to defend
DDoS, however, this paper surveys the deployment of Bloom Filter in defending a
DDoS attack. The Bloom Filter is a probabilistic data structure for membership
query that returns either true or false. Bloom Filter uses tiny memory to store
information of large data. Therefore, packet information is stored in Bloom
Filter to defend and defeat DDoS. This paper presents a survey on DDoS
defending technique using Bloom Filter.Comment: 9 pages, 1 figure. This article is accepted for publication in EAI
Endorsed Transactions on Scalable Information System
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