RFC 2123 (rfc2123) - Page 3 of 34

Traffic Flow Measurement: Experiences with NeTraMet

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RFC 2123                Traffic Flow Measurement              March 1997


1.1 NeTraMet structure and development

   The Traffic Flow Architecture document [1] describes four components:

     - METERS, which are attached to the network at the points where
       it is desired to measure the traffic,

     - METER READERS, which read data from meters and store it for later
       use,

     - MANAGERS, which configure meters and control meter readers, and

     - ANALYSIS APPLICATIONS, which process the data from meter readers
       so as to produce whatever reports are required.

   NeTraMet is a computer program which implements the Traffic Meter,
   stores the measured flow data in memory, and provides an SNMP agent
   so as to make it available to Meter Readers.  The NeTraMet
   distribution files include NeMaC, which is a combined Manager and
   Meter Reader capable of managing an arbitrary number of meters, each
   of which may be using its own rule set, and having its flow data
   collected at its own specified intervals.  The NeTraMet distribution
   also includes several rudimentary Analysis Applications, allowing
   users to produce simple plots from NeMaC's flow data files (fd_filter
   and fd_extract) and to monitor - in real time - the flows at a remote
   meter (nm_rc and nifty).

   Since the first release the Traffic Meter MIB [2] has been both
   improved and simplified.  Significant changes have included better
   ways to specify traffic flows (i.e. more actions and better control
   structures for the Packet Matching Engine), and computed attributes
   (class and kind).  These changes have been prompted by operational
   requirements at sites using NeTraMet, and have been tested
   extensively in successive versions of NeTraMet.

   NeTraMet is widely used to collect usage data for Internet Service
   Providers.  This is especially so in Australia and New Zealand, but
   there are also active users at sites around the world, for example in
   Canada, France, Germany and Poland.

   NeTraMet is very useful as a tool for understanding exactly where
   traffic is flowing in large networks.  Since the Traffic Meters
   perform considerable data reduction (as specified by their rule sets)
   they significantly reduce the volume of data to be read by Meter
   Readers.  This characteristic makes NeTraMet particularly effective
   for networks with many remote sites.  An example of this (the
   Kawaihiko network) is briefly described below.




Brownlee                     Informational
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