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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.
CITED BY 24
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Lukasz Golab , David DeHaan , Erik D. Demaine , Alejandro Lopez-Ortiz , J. Ian Munro, Identifying frequent items in sliding windows over on-line packet streams, Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement, October 27-29, 2003, Miami Beach, FL, USA
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Graham Cormode , Theodore Johnson , Flip Korn , S. Muthukrishnan , Oliver Spatscheck , Divesh Srivastava, Holistic UDAFs at streaming speeds, Proceedings of the 2004 ACM SIGMOD international conference on Management of data, June 13-18, 2004, Paris, France
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Mark Daly , Yitzhak Mandelbaum , David Walker , Mary Fernández , Kathleen Fisher , Robert Gruber , Xuan Zheng, PADS: an end-to-end system for processing ad hoc data, Proceedings of the 2006 ACM SIGMOD international conference on Management of data, June 27-29, 2006, Chicago, IL, USA
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Hua-Gang Li , Songting Chen , Junichi Tatemura , Divyakant Agrawal , K. Selçuk Candan , Wang-Pin Hsiung, Safety guarantee of continuous join queries over punctuated data streams, Proceedings of the 32nd international conference on Very large data bases, September 12-15, 2006, Seoul, Korea
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Yijian Bai , Hetal Thakkar , Haixun Wang , Chang Luo , Carlo Zaniolo, A data stream language and system designed for power and extensibility, Proceedings of the 15th ACM international conference on Information and knowledge management, November 06-11, 2006, Arlington, Virginia, USA
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Alberto Lerner , Dennis Shasha, AQuery: query language for ordered data, optimization techniques, and experiments, Proceedings of the 29th international conference on Very large data bases, p.345-356, September 09-12, 2003, Berlin, Germany
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Graham Cormode , Flip Korn , S. Muthukrishnan , Divesh Srivastava, Finding hierarchical heavy hitters in data streams, Proceedings of the 29th international conference on Very large data bases, p.464-475, September 09-12, 2003, Berlin, Germany
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Graham Cormode , Mayur Datar , Piotr Indyk , S. Muthukrishnan, Comparing data streams using Hamming norms (how to zero in), Proceedings of the 28th international conference on Very Large Data Bases, p.335-345, August 20-23, 2002, Hong Kong, China
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