Evaluation of Hash Functions for Multipoint Sampling in IP Networks
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Sprache:Englisch
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ePUB
Kopierschutz
Nein
Family Sharing
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Text-to-Speech
Ja
Erscheinungsdatum
02.03.2012
Verlag
GRINSeitenzahl
104 (Printausgabe)
Dateigröße
4329 KB
Auflage
1. Auflage
Sprache
Englisch
EAN
9783869436067
Diploma Thesis from the year 2008 in the subject Computer Science - Applied, grade: 1, Technical University of Berlin, language: English, abstract: Network Measurements play an essential role in operating and developing today's
Internet. A variety of measurement applications demand for multipoint
network measurements, e.g. service providers need to validate their delay guarantees
from Service Level Agreements and network engineers have incentives to
track where packets are changed, reordered, lost or delayed. Multipoint measurements
create an immense amount of measurement data which demands for high
resource measurement infrastructure. Data selection techniques, like sampling
and filtering, provide efficient solutions for reducing resource consumption while
still maintaining sufficient information about the metrics of interest. But not all
selection techniques are suitable for multipoint measurements; only deterministic filtering allows a synchronized selection of packets at multiple observation points.
Nevertheless a fillter bases its selection decision on the packet content and hence
is suspect to bias, i.e the selected subset is not representative for the whole population.
Hash-based selection is a filtering method that tries to emulate random
selection in order to obtain a representative sample for accurate estimations of
traffic characteristics.
The subject of the thesis is to assess which hash function and which packet content
should be used for hash-based selection to obtain a seemingly random and
unbiased selection of packets. This thesis empirically analyzes 25 hash functions
and different packet content combinations on their suitability for hash-based
selection. Experiments are based on a collection of 7 real traffic groups from
different networks.
Internet. A variety of measurement applications demand for multipoint
network measurements, e.g. service providers need to validate their delay guarantees
from Service Level Agreements and network engineers have incentives to
track where packets are changed, reordered, lost or delayed. Multipoint measurements
create an immense amount of measurement data which demands for high
resource measurement infrastructure. Data selection techniques, like sampling
and filtering, provide efficient solutions for reducing resource consumption while
still maintaining sufficient information about the metrics of interest. But not all
selection techniques are suitable for multipoint measurements; only deterministic filtering allows a synchronized selection of packets at multiple observation points.
Nevertheless a fillter bases its selection decision on the packet content and hence
is suspect to bias, i.e the selected subset is not representative for the whole population.
Hash-based selection is a filtering method that tries to emulate random
selection in order to obtain a representative sample for accurate estimations of
traffic characteristics.
The subject of the thesis is to assess which hash function and which packet content
should be used for hash-based selection to obtain a seemingly random and
unbiased selection of packets. This thesis empirically analyzes 25 hash functions
and different packet content combinations on their suitability for hash-based
selection. Experiments are based on a collection of 7 real traffic groups from
different networks.
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