Traffic Measurement For Big Network Data (wireless Networks)
by Shigang Chen /
2016 / English / PDF
7.5 MB Download
This book presents several compact and fast methods for online
traffic measurement of big network data. It describes challenges
of online traffic measurement, discusses the state of the field,
and provides an overview of the potential solutions to major
problems.
This book presents several compact and fast methods for online
traffic measurement of big network data. It describes challenges
of online traffic measurement, discusses the state of the field,
and provides an overview of the potential solutions to major
problems.
The authors introduce the problem of per-flow size measurement
for big network data and present a fast and scalable counter
architecture, called Counter Tree, which leverages a
two-dimensional counter sharing scheme to achieve far better
memory efficiency and significantly extend estimation
range.
The authors introduce the problem of per-flow size measurement
for big network data and present a fast and scalable counter
architecture, called Counter Tree, which leverages a
two-dimensional counter sharing scheme to achieve far better
memory efficiency and significantly extend estimation
range.
Unlike traditional approaches to cardinality estimation problems
that allocate a separated data structure (called estimator) for
each flow, this book takes a different design path by viewing all
the flows together as a whole: each flow is allocated with a
virtual estimator, and these virtual estimators share a common
memory space. A framework of virtual estimators is designed to
apply the idea of sharing to an array of cardinality estimation
solutions, achieving far better memory efficiency than the best
existing work.
Unlike traditional approaches to cardinality estimation problems
that allocate a separated data structure (called estimator) for
each flow, this book takes a different design path by viewing all
the flows together as a whole: each flow is allocated with a
virtual estimator, and these virtual estimators share a common
memory space. A framework of virtual estimators is designed to
apply the idea of sharing to an array of cardinality estimation
solutions, achieving far better memory efficiency than the best
existing work.
To conclude, the authors discuss persistent spread estimation in
high-speed networks. They offer a compact data structure called
multi-virtual bitmap, which can estimate the cardinality of the
intersection of an arbitrary number of sets. Using multi-virtual
bitmaps, an implementation that can deliver high estimation
accuracy under a very tight memory space is presented.
To conclude, the authors discuss persistent spread estimation in
high-speed networks. They offer a compact data structure called
multi-virtual bitmap, which can estimate the cardinality of the
intersection of an arbitrary number of sets. Using multi-virtual
bitmaps, an implementation that can deliver high estimation
accuracy under a very tight memory space is presented.
The results of these experiments will surprise both professionals
in the field and advanced-level students interested in the topic.
By providing both an overview and the results of specific
experiments, this book is useful for those new to online traffic
measurement and experts on the topic.
The results of these experiments will surprise both professionals
in the field and advanced-level students interested in the topic.
By providing both an overview and the results of specific
experiments, this book is useful for those new to online traffic
measurement and experts on the topic.