Fast and scalable layer four switching
Proceedings of the ACM SIGCOMM '98 conference on Applications, technologies, architectures, and protocols for computer communication
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Automatically inferring patterns of resource consumption in network traffic
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Diamond in the rough: finding Hierarchical Heavy Hitters in multi-dimensional data
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Online identification of hierarchical heavy hitters: algorithms, evaluation, and applications
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Identifying elephant flows through periodically sampled packets
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Finding (Recently) Frequent Items in Distributed Data Streams
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Space complexity of hierarchical heavy hitters in multi-dimensional data streams
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Finding Critical Traffic Matrices
DSN '05 Proceedings of the 2005 International Conference on Dependable Systems and Networks
Traffic Measurement and Analysis of TUNET
CW '05 Proceedings of the 2005 International Conference on Cyberworlds
What's new: finding significant differences in network data streams
IEEE/ACM Transactions on Networking (TON)
Efficient computation of frequent and top-k elements in data streams
ICDT'05 Proceedings of the 10th international conference on Database Theory
Algorithms for packet classification
IEEE Network: The Magazine of Global Internetworking
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Focused on identifying hierarchical heavy hitters (HHH) in multiple dimensions from network management perspective, this paper presents a framework of finding HHHs in network measurement systems and proposes a heuristic algorithm on finding static and dynamic HHH in two dimensions. Our algorithm dramatically reduces the space and time complexity comparing with other previous algorithms. We implement and test it in a typical local network and the experimental results verify the effectiveness and efficiency of the algorithm.