7,130 research outputs found
Using cascading Bloom filters to improve the memory usage for de Brujin graphs
De Brujin graphs are widely used in bioinformatics for processing
next-generation sequencing data. Due to a very large size of NGS datasets, it
is essential to represent de Bruijn graphs compactly, and several approaches to
this problem have been proposed recently. In this work, we show how to reduce
the memory required by the algorithm of [3] that represents de Brujin graphs
using Bloom filters. Our method requires 30% to 40% less memory with respect to
the method of [3], with insignificant impact to construction time. At the same
time, our experiments showed a better query time compared to [3]. This is, to
our knowledge, the best practical representation for de Bruijn graphs.Comment: 12 pages, submitte
In-packet Bloom filters: Design and networking applications
The Bloom filter (BF) is a well-known space-efficient data structure that
answers set membership queries with some probability of false positives. In an
attempt to solve many of the limitations of current inter-networking
architectures, some recent proposals rely on including small BFs in packet
headers for routing, security, accountability or other purposes that move
application states into the packets themselves. In this paper, we consider the
design of such in-packet Bloom filters (iBF). Our main contributions are
exploring the design space and the evaluation of a series of extensions (1) to
increase the practicality and performance of iBFs, (2) to enable
false-negative-free element deletion, and (3) to provide security enhancements.
In addition to the theoretical estimates, extensive simulations of the multiple
design parameters and implementation alternatives validate the usefulness of
the extensions, providing for enhanced and novel iBF networking applications.Comment: 15 pages, 11 figures, preprint submitted to Elsevier COMNET Journa
Distributed Collaborative Monitoring in Software Defined Networks
We propose a Distributed and Collaborative Monitoring system, DCM, with the
following properties. First, DCM allow switches to collaboratively achieve flow
monitoring tasks and balance measurement load. Second, DCM is able to perform
per-flow monitoring, by which different groups of flows are monitored using
different actions. Third, DCM is a memory-efficient solution for switch data
plane and guarantees system scalability. DCM uses a novel two-stage Bloom
filters to represent monitoring rules using small memory space. It utilizes the
centralized SDN control to install, update, and reconstruct the two-stage Bloom
filters in the switch data plane. We study how DCM performs two representative
monitoring tasks, namely flow size counting and packet sampling, and evaluate
its performance. Experiments using real data center and ISP traffic data on
real network topologies show that DCM achieves highest measurement accuracy
among existing solutions given the same memory budget of switches
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