The space complexity of approximating the frequency moments
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
New sampling-based summary statistics for improving approximate query answers
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Tracking join and self-join sizes in limited storage
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
Computing Iceberg Queries Efficiently
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Surfing Wavelets on Streams: One-Pass Summaries for Approximate Aggregate Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Finding Frequent Items in Data Streams
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Frequency Estimation of Internet Packet Streams with Limited Space
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
A simple algorithm for finding frequent elements in streams and bags
ACM Transactions on Database Systems (TODS)
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Sketch-based change detection: methods, evaluation, and applications
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
Flow sampling under hard resource constraints
Proceedings of the joint international conference on Measurement and modeling of computer systems
Data streaming algorithms for efficient and accurate estimation of flow size distribution
Proceedings of the joint international conference on Measurement and modeling of computer systems
Online identification of hierarchical heavy hitters: algorithms, evaluation, and applications
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Reversible sketches for efficient and accurate change detection over network data streams
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
A robust system for accurate real-time summaries of internet traffic
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
An improved data stream summary: the count-min sketch and its applications
Journal of Algorithms
Ranking flows from sampled traffic
CoNEXT '05 Proceedings of the 2005 ACM conference on Emerging network experiment and technology
Characterizing and Exploiting Reference Locality in Data Stream Applications
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Detection and identification of network anomalies using sketch subspaces
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Joint data streaming and sampling techniques for detection of super sources and destinations
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
How to summarize the universe: dynamic maintenance of quantiles
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Efficient computation of frequent and top-k elements in data streams
ICDT'05 Proceedings of the 10th international conference on Database Theory
Learn more, sample less: control of volume and variance in network measurement
IEEE Transactions on Information Theory
Adaptive shared-state sampling
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
High-speed per-flow traffic measurement with probabilistic multiplicity counting
INFOCOM'10 Proceedings of the 29th conference on Information communications
Finding top-k elements in data streams
Information Sciences: an International Journal
Estimating top-k destinations in data streams
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Sequential hashing: A flexible approach for unveiling significant patterns in high speed networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Mining approximate frequent closed flows over packet streams
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Per-flow traffic measurement through randomized counter sharing
IEEE/ACM Transactions on Networking (TON)
Scalable identification and measurement of heavy-hitters
Computer Communications
Spreader classification based on optimal dynamic bit sharing
IEEE/ACM Transactions on Networking (TON)
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Knowledge of the largest traffic ows in a network is important for many network management applications. The problem of finding these ows is known as the heavy-hitter problem and has been the subject of many studies in the past years. One of the most efficient and well-known algorithms for finding heavy hitters is lossy counting [29]. In this work we introduce probabilistic lossy counting (PLC), which enhances lossy counting in computing network traffic heavy hitters. PLC uses on a tighter error bound on the estimated sizes of traffic ows and provides probabilistic rather than deterministic guarantees on its accuracy. The probabilistic-based error bound substantially improves the memory consumption of the algorithm. In addition, PLC reduces the rate of false positives of lossy counting and achieves a low estimation error, although slightly higher than that of lossy counting We compare PLC with state-of-the-art algorithms for finding heavy hitters. Our experiments using real traffic traces find that PLC has 1) between 34.4% and 74% lower memory consumption, 2) between 37.9% and 40.5% fewer false positives than lossy counting, and 3) a small estimation error.