A linear-time probabilistic counting algorithm for database applications
ACM Transactions on Database Systems (TODS)
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Maintaining stream statistics over sliding windows: (extended abstract)
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Properties and prediction of flow statistics from sampled packet streams
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Issues in data stream management
ACM SIGMOD Record
Gigascope: a stream database for network applications
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Bitmap algorithms for counting active flows on high speed links
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
Identifying frequent items in sliding windows over on-line packet streams
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
Declarative Network Monitoring with an Underprovisioned Query Processor
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Why go logarithmic if we can go linear?: Towards effective distinct counting of search traffic
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Load shedding in network monitoring applications
ATC'07 2007 USENIX Annual Technical Conference on Proceedings of the USENIX Annual Technical Conference
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Counting the number of flows present in network traffic is not trivial, given that the naive approach of using a hash table to track the active flows is too slow for the current backbone network speeds. Several algorithms have been proposed in the recent literature that can calculate an approximate count using small amount of memory and few memory accesses per packet. Fewer works have addressed the more complex problem of counting flows over sliding windows, where the main challenge is to continuously expire old information. One of the existing proposals is a straightforward adaptation of the direct bitmaps technique to the sliding window model. We present an algorithm called Countdown Vector that also builds upon the direct bitmaps technique. Our algorithm, however, obtains significant cost reductions both in terms of memory and CPU, by introducing an extra approximation in the mechanism in charge of the expiration of old information.