Gigascope: high performance network monitoring with an SQL interface

  • Authors:

  • Chuck Cranor;Yuan Gao;Theodore Johnson;Vlaidslav Shkapenyuk;Oliver Spatscheck

  • Affiliations:

  • AT&T Labs - Research;AT&T Labs - Research;AT&T Labs - Research;AT&T Labs - Research;AT&T Labs - Research

  • Venue:
  • Proceedings of the 2002 ACM SIGMOD international conference on Management of data
  • Year:
  • 2002

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Abstract

Operators of large networks and providers of network services need to monitor and analyze the network traffic flowing through their systems. Monitoring requirements range from the long term (e.g., monitoring link utilizations, computing traffic matrices) to the ad-hoc (e.g. detecting network intrusions, debugging performance problems). Many of the applications are complex (e.g., reconstruct TCP/IP sessions), query layer-7 data (find streaming media connections), operate over huge volumes of data (Gigabit and higher speed links), and have real-time reporting requirements (e.g., to raise performance or intrusion alerts).We have found that existing network monitoring technologies have severe limitations. One option is to use TCPdump to monitor a network port and a user-level application program to process the data. While this approach is very flexible, it is not fast enough to handle gigabit speeds on inexpensive equipment. Another approach is to use network monitoring devices. While these devices are capable of high speed monitoring, they are inflexible as the set of monitoring tasks is pre-defined. Adding new functionality is expensive and has long lead times. A similar approach is to use monitoring tools built into routers, such as SNMP, RMON, or NetFlow. These tools have similar characteristics --- fast but inflexible.A further problem with all of these tools is their lack of a query interface. The data from the monitors are dumped to a file or piped through a file stream without an association to the semantics of the data. The burden of managing and interpreting the data is left to the analyst. Due to the volume and complexity of the data, the burden can be severe. These problems make developing new applications needlessly slow and difficult. Also, many mistakes are made leading to incorrect analyses.