A linear-time probabilistic counting algorithm for database applications
ACM Transactions on Database Systems (TODS)
New sampling-based summary statistics for improving approximate query answers
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Practical automated detection of stealthy portscans
Journal of Computer Security
New directions in traffic measurement and accounting
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
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
Efficient implementation of a statistics counter architecture
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS 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
FlowScan: A Network Traffic Flow Reporting and Visualization Tool
LISA '00 Proceedings of the 14th USENIX conference on System administration
Space efficient mining of multigraph streams
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Design of a novel statistics counter architecture with optimal space and time efficiency
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
Bitmap algorithms for counting active flows on high-speed links
IEEE/ACM Transactions on Networking (TON)
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
Fast Algorithms for Heavy Distinct Hitters using Associative Memories
ICDCS '07 Proceedings of the 27th International Conference on Distributed Computing Systems
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Probabilistic lossy counting: an efficient algorithm for finding heavy hitters
ACM SIGCOMM Computer Communication Review
Counter braids: a novel counter architecture for per-flow measurement
SIGMETRICS '08 Proceedings of the 2008 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Towards 100G packet processing: Challenges and technologies
Bell Labs Technical Journal - Core and Wireless Networks
IEEE Journal on Selected Areas in Communications
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Spreader classification is an online traffic measurement function that has many important applications. In order to keep up with ever-higher line speed, the recent research trend is to implement such functions in fast but small on-die SRAM. However, the mismatch between the huge amount of Internet traffic to be monitored and limited on-die memory space presents a significant technical challenge. In this paper, we propose an Efficient Spreader Classification (ESC) scheme based on dynamic bit sharing, a compact information storage method. We design a maximum likelihood estimation method to extract per-source information from the compact storage and determine the heavy spreaders. Our new scheme ensures that false positive/negative ratios are bounded. Moreover, given an arbitrary set of bounds, we develop a systematic approach to determine the optimal system parameters that minimize the amount of memory needed to meet the bounds. Experiments based on a real Internet traffic trace demonstrate that the proposed spreader classification scheme reduces memory consumption by 3-20 times when compared to the best existing work. We also investigate a new multi-objective spreader classification problem and extend our classification scheme to solve it.