Network monitoring serves as basis for a wide scope of network operations, engineering
and management. Precise network monitoring involves inspecting every
packet traversing in a network. However, this is infeasible in today’s and future
high-speed networks, due to significant overheads of processing, storing, and transferring
measured data. Therefore, scalable network monitoring techniques are in
urgent need. This book addresses the scalability issue of network monitoring from
both traffic and performance perspectives.
On scalable traffic monitoring, we present sampling techniques for total load and
flow measurement. In order to develop accurate and efficient measurement schemes,
we study various aspects of traffic characteristics and their impacts on packet sampling.
We find that static sampling does not adjust itself to dynamic traffic conditions,
yielding often erroneous estimations or excessive oversampling. We develop
the adaptive random sampling technique for total load estimation, that determines
the sampling probability adaptively according to traffic condition.We then enhance
the adaptive sampling technique to measure traffic in flow level. Flow measurement
is a particularly challenging problem, since flows arrive at random times, stay for
random durations, and their rates fluctuate over time. Those characteristics make it
hard to decide a sampling interval where sampling probability is adapted, and to
define a large flow pragmatically. Through a stratified approach, we estimate large
flows accurately, regardless of their arrival times, durations, or the rate variabilities
during their life times.