Network Management Strategies for the CIO
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There are a variety of third-party tools that are available on the market from companies such as Network Physics, WildPackets, NetQos, and others. They specialize in gathering statistical information about traffic flow across an enterprise network and then present a unified view of that data to a network manager. With this information, the network manager can gain valuable insight into the overall health and utilization of the network. For example, the network manager could pinpoint network problems such as excess retransmissions due to a WAN link with unusually high packet loss. On the application side, the network manager could determine information such as which applications are running on the network (for example, to identify rogue applications such as worms), which applications are most popular, how much bandwidth each application is consuming, and which users are accessing the applications. With this information, a network manager has more visibility into what bits are running across the network and where.
The data to provide this information is gathered by deploying specialized vendor appliances or probes in strategic locations in your network (at a routing boundary). These probes then collect the data by examining traffic flow and provide that data to a centralized appliance(s) or workstation that crunches all the data and formats the information in a tabular or graphical form.
While these tools offer valuable insight, for many networks the cost and complexity associated with deploying specialized network probes is too high. Instead, they rely on the data provided by other devices in the network such as switches, routers, or WAN optimization appliances. For example, the F5 WANJet appliance is primarily deployed in enterprise networks to provide WAN optimization; however, it also has robust network diagnostic and reporting capabilities that can provide a network manager with insight into their application performance. To be sure, the specialized network probes offer greater depth of data than a WANJet alone, but they also require a separate appliance, maintenance, training, and support that can be cost prohibitive for many organizations.
This was first published in December 2005
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