5,493 research outputs found
Self-generated Self-similar Traffic
Self-similarity in the network traffic has been studied from several aspects:
both at the user side and at the network side there are many sources of the
long range dependence. Recently some dynamical origins are also identified: the
TCP adaptive congestion avoidance algorithm itself can produce chaotic and long
range dependent throughput behavior, if the loss rate is very high. In this
paper we show that there is a close connection between the static and dynamic
origins of self-similarity: parallel TCPs can generate the self-similarity
themselves, they can introduce heavily fluctuations into the background traffic
and produce high effective loss rate causing a long range dependent TCP flow,
however, the dropped packet ratio is low.Comment: 8 pages, 12 Postscript figures, accepted in Nonlinear Phenomena in
Complex System
Modeling Scalability of Distributed Machine Learning
Present day machine learning is computationally intensive and processes large
amounts of data. It is implemented in a distributed fashion in order to address
these scalability issues. The work is parallelized across a number of computing
nodes. It is usually hard to estimate in advance how many nodes to use for a
particular workload. We propose a simple framework for estimating the
scalability of distributed machine learning algorithms. We measure the
scalability by means of the speedup an algorithm achieves with more nodes. We
propose time complexity models for gradient descent and graphical model
inference. We validate our models with experiments on deep learning training
and belief propagation. This framework was used to study the scalability of
machine learning algorithms in Apache Spark.Comment: 6 pages, 4 figures, appears at ICDE 201
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