Summary cache: a scalable wide-area web cache sharing protocol
IEEE/ACM Transactions on Networking (TON)
Charging from sampled network usage
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
New directions in traffic measurement and accounting
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
Fast accurate computation of large-scale IP traffic matrices from link loads
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
The power of slicing in internet flow measurement
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
ProgME: towards programmable network measurement
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
Fast monitoring of traffic subpopulations
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
Efficient packet sampling for accurate traffic measurements
Computer Networks: The International Journal of Computer and Telecommunications Networking
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With an increasing requirement to classify traffic and track security threats, newer flexible and efficient ways are needed for collecting traffic statistics and monitoring network flows. However, traditional solutions based on packet sampling do not provide the flexibility required for these applications. For example, operators are often interested in observing as many unique flows as possible; however, random packet sampling is inherently biased towards large flows. Operators may also be interested in increasing the fidelity of flow measurements for a certain class of flows; such flexibility is lacking in today's packet sampling frameworks. In this paper, we propose a novel architecture called CLAMP that provides an efficient framework to implement class-based sampling. At the heart of CLAMP is a novel data structure we propose called composite Bloom filter (CBF) that consists of a set of Bloom filters working together to encapsulate various class definitions. In particular, we show the flexibility and efficacy of CLAMP by implementing a simple two-class size-based sampling. We also consider different objectives such as maximizing flow coverage and improving the accuracy of certain class of flows. In comparison to previous approaches that implement simple size-based sampling, our architecture requires substantially lower amounts of memory (up to 80x) and achieves higher flow coverage (up to 8x more flows) under specific configurations.