<?xml version="1.0" standalone="no"?>
                    <!DOCTYPE div SYSTEM "/www/backend/www-xml-443/dtd/caidaML.dtd">
                    <!-- do NOT ERASE the DOCTYPE declaration! --><div>


<tr bgcolor="#f4f4f4">
  <td>
<font face="helvetica,arial" size="2">
<b>URL:</b>
</font>
</td>
  <td>
<font face="helvetica,arial" size="2">
<a href="http://www.caida.org/publications/papers/2000/DSOM-00/">http://www.caida.org/publications/papers/2000/DSOM-00/</a>
</font>
  </td>
</tr>


<tr bgcolor="#e9e9e9">
  <td>
<font face="helvetica,arial" size="2">
<b>Entry Date:</b>
</font>
</td>
  <td>
<font face="helvetica,arial" size="2">
2004-02-02


</font>
  </td>
</tr>


<tr bgcolor="#f4f4f4">
  <td>
<font face="helvetica,arial" size="2">
<b>Abstract:</b>
</font>
</td>
  <td>
<font face="helvetica,arial" size="2">
<p>

An understanding of certain network functions is critical for suc-
cessful network management. Managers must have insight into net-
work topology, protocol performance and fault detection/isolation.
The ability to obtain such insight iseven more critical when try-
ing to support evolving technologies like multicast. With these
technologies, the pace of change is high, modifications to routing
mechanisms are frequent, and faults are common. In this paper we
introduce Mantra, a tool we have developed to monitor multicast.
Mantra collects, analyzes, and visualizes network-layer (routing and
topology) data about the global multicast infrastructure. The two
most important functions of Mantra are: (1) monitoring multicast
networks on a global scale; and (2) presenting results in the form
of intuitive visualizations. To achieve accurate monitoring, Mantra
collects data from several topologically and geographically diverse
networks. For the purpose of presentation, Mantra uses several in-
teractive visualization mechanisms to present statistics, topology
maps and geographic properties. Another noteworthy feature of
Mantra is its exible and scalable architecture. This architecture
helps keep our monitoring erts current with the fast pace of mul-
ticast developments. It also enables us to expand Mantra's moni-
toring scope to more networks and larger data sets.

</p>


</font>
  </td>
</tr>


<tr bgcolor="#e9e9e9">
  <td>
<font face="helvetica,arial" size="2">
<b>Datasets:</b>
</font>
</td>
  <td>
<font face="helvetica,arial" size="2">


</font>
  </td>
</tr>


<tr bgcolor="#f4f4f4">
  <td>
<font face="helvetica,arial" size="2">
<b>Experiments:</b>
</font>
</td>
  <td>
<font face="helvetica,arial" size="2">


</font>
  </td>
</tr>


<tr bgcolor="#e9e9e9">
  <td>
<font face="helvetica,arial" size="2">
<b>Results:</b>
</font>
</td>
  <td>
<font face="helvetica,arial" size="2">


</font>
  </td>
</tr>


<tr bgcolor="#f4f4f4">
  <td>
<font face="helvetica,arial" size="2">
<b>References:</b>
</font>
</td>
  <td>
<font face="helvetica,arial" size="2">




</font>
  </td>
</tr>
</div>

