Using Network Application Behavior to Predict Performance
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Today's continuously growing Internet requires usersand network applications to have knowledge of networkmetrics.This knowledge is critical for decisionmaking during the usage of network applications.Thisthesis studies application related networkmetrics.The major approach in this work is to examinethe traffic between a simulated user.We use thehistorical data collected from previous usage ofnetwork applications to make predictions for futureusage of those applications.Prediction mechanismsrequire us to make parameter choices so that certainweights can be placed on historical data versuscurrent...
Today's continuously growing Internet requires users
and network applications to have knowledge of network
metrics.This knowledge is critical for decision
making during the usage of network applications.This
thesis studies application related network
metrics.The major approach in this work is to examine
the traffic between a simulated user.We use the
historical data collected from previous usage of
network applications to make predictions for future
usage of those applications.Prediction mechanisms
require us to make parameter choices so that certain
weights can be placed on historical data versus
current data.We study these different choices and use
the values from our best experimental results.From
these studies we conclude that our data prediction is
quite accurate and remains stable over a range of
parameter choices.The use of shared routing paths
between users and network applications are explored
in the performance prediction of applications.The
network applications studied are also varied,
including web, streaming, DNS.We see whether sharing
information obtained from different applications can
be used to make predictions of application performance.
and network applications to have knowledge of network
metrics.This knowledge is critical for decision
making during the usage of network applications.This
thesis studies application related network
metrics.The major approach in this work is to examine
the traffic between a simulated user.We use the
historical data collected from previous usage of
network applications to make predictions for future
usage of those applications.Prediction mechanisms
require us to make parameter choices so that certain
weights can be placed on historical data versus
current data.We study these different choices and use
the values from our best experimental results.From
these studies we conclude that our data prediction is
quite accurate and remains stable over a range of
parameter choices.The use of shared routing paths
between users and network applications are explored
in the performance prediction of applications.The
network applications studied are also varied,
including web, streaming, DNS.We see whether sharing
information obtained from different applications can
be used to make predictions of application performance.
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