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ABSTRACT

A distributed monitoring framework can serve as an important building block for constructing large-scale data aggregation and continuous event monitoring applications, such as IP traffic monitoring (DDoS attacks), network anomaly detection (Internet worms), accounting and bandwidth provisioning (hot spots, flash crowds), sensor monitoring and control, and grid resource monitoring. At the core of these applications is a distributed query engine that aggregates information and performs continuous tracking of queries over collections of physically-distributed and rapidly-updating data streams. The underlying aim is to provide a global view of information in the system at a reasonable cost and within a specified precision bound. To achieve this objective, a distributed monitoring system should (a) scale to a large number of streams and query attributes, (b) incur minimal communication overhead for aggregating query results, (c) be time responsive for quickly identifying anomalies, and (d) be able to bound the inaccuracy of the computed value for the aggregate function.

REFERENCES

• 1: Amit Manjhi , Vladislav Shkapenyuk , Kedar Dhamdhere , Christopher Olston, Finding (Recently) Frequent Items in Distributed Data Streams, Proceedings of the 21st International Conference on Data Engineering (ICDE'05), p.767-778, April 05-08, 2005 [doi>10.1109/ICDE.2005.68]
• 2: Praveen Yalagandula , Mike Dahlin, A scalable distributed information management system, Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications, August 30-September 03, 2004, Portland, Oregon, USA [doi>10.1145/1015467.1015509]