19,527 research outputs found
Performance evaluation of an open distributed platform for realistic traffic generation
Network researchers have dedicated a notable part of their efforts
to the area of modeling traffic and to the implementation of efficient traffic
generators. We feel that there is a strong demand for traffic generators
capable to reproduce realistic traffic patterns according to theoretical
models and at the same time with high performance. This work presents an open
distributed platform for traffic generation that we called distributed
internet traffic generator (D-ITG), capable of producing traffic (network,
transport and application layer) at packet level and of accurately replicating
appropriate stochastic processes for both inter departure time (IDT) and
packet size (PS) random variables. We implemented two different versions of
our distributed generator. In the first one, a log server is in charge of
recording the information transmitted by senders and receivers and these
communications are based either on TCP or UDP. In the other one, senders and
receivers make use of the MPI library. In this work a complete performance
comparison among the centralized version and the two distributed versions of
D-ITG is presented
Efficient multi-label classification for evolving data streams
Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory.
This paper proposes a new experimental framework for studying multi-label evolving stream classification, and new efficient methods that combine the best practices in streaming scenarios with the best practices in multi-label classification. We present a Multi-label Hoeffding Tree with multilabel classifiers at the leaves as a base classifier. We obtain fast and accurate methods, that are well suited for this challenging multi-label classification streaming task. Using the new experimental framework, we test our methodology by performing an evaluation study on synthetic and real-world datasets. In comparison to well-known batch multi-label methods, we obtain encouraging results
Markovian Monte Carlo program EvolFMC v.2 for solving QCD evolution equations
We present the program EvolFMC v.2 that solves the evolution equations in QCD
for the parton momentum distributions by means of the Monte Carlo technique
based on the Markovian process. The program solves the DGLAP-type evolution as
well as modified-DGLAP ones. In both cases the evolution can be performed in
the LO or NLO approximation. The quarks are treated as massless. The overall
technical precision of the code has been established at 0.05% precision level.
This way, for the first time ever, we demonstrate that with the Monte Carlo
method one can solve the evolution equations with precision comparable to the
other numerical methods.Comment: 38 pages, 9 Postscript figure
DRAGON: Monte Carlo generator of particle production from a fragmented fireball in ultrarelativistic nuclear collisions
A Monte Carlo generator of the final state of hadrons emitted from an
ultrarelativistic nuclear collision is introduced. An important feature of the
generator is a possible fragmentation of the fireball and emission of the
hadrons from fragments. Phase space distribution of the fragments is based on
the blast wave model extended to azimuthally non-symmetric fireballs.
Parameters of the model can be tuned and this allows to generate final states
from various kinds of fireballs. A facultative output in the OSCAR1999A format
allows for a comprehensive analysis of phase-space distributions and/or use as
an input for an afterburner.Comment: name of the model changed from QuaG to DRAGON in the new version,
otherwise only cosmetic changes, uses elsart.cls, the software package
described here can be downloaded from
http://www.fpv.umb.sk/~tomasik/soft.htm
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