Yunqi Li
ABSTRACT
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.
- Babcock, B., Olston, C., Distributed Top-K Monitoring. In Proc. of ACM SIGMOD International Conference on Management of Data, San Diego, CA, United States, 2003. 28--39. Google Scholar
Digital Library
- Mori, T., Uchida, M., Kawahara, R., et al. Identifying elephant flows through periodically sampled packets. In Proc. of ACM SIGCOMM Internet Measurement Conference (IMC '04), Taormina, Italy, 2004, 115--120. Google ScholarDigital Library
- Manjhi, A., Shkapenyuk, V., Dhamdhere, K., et al. Finding (recently) frequent items in distributed data streams. In Proc. of International Conference on Data Engineering, Tokyo, Japan, 2005, 767--778. Google ScholarDigital Library
- Estan, C., Savage, S., and Varghese, G. Automatically Inferring Patterns of Resource Consumption in Network Traffic. In Proc. of ACM SIGCOMM, Karlsruhe, Germany, 2003, 137--148. Google ScholarDigital Library
- Metwally, A., Agrawal, D. and Abbadi, E. Efficient computation of frequent and top-k elements in data streams. In Proc. of 10th International Conference on Database Theory (ICDT '05), Edinburgh, UK, 2005, 398--412. Google ScholarDigital Library
- Cormode, G. and Muthukrishnan, S. What's new: Finding significant differences in network data streams. IEEE/ACM Trans. on Networking, 13, 6(2005), 1219--1232. Google ScholarDigital Library
- Cormode, G., Muthukrishnant, S., Korn, F., et al. Diamond in the rough: Finding hierarchical heavy hitters in multidimensional data. In Proc. of ACM SIGMOD International Conference on Management of Data, Paris, France, 2004, 155--166. Google ScholarDigital Library
- Hershberger, J., Shrivastava, N., Suri, S., et al. Space complexity of Hierarchical Heavy Hitters in Multi-Dimensional Data Streams. In Proc. of International Conference on Very Large Data Bases (VLDB '05), 2005, 338--347. Google ScholarDigital Library
- Zhang, Y. and Ge, Z. Finding Critical Traffic Matrices. In Proc. of International Conference on Dependable Systems and Networks (DSN'05), Yokohama, Japan, 2005, 188--197. Google ScholarDigital Library
- Zhang, Y., Singh, S., Sen, S., et al. Online identification of hierarchical heavy hitters: Algorithms, evaluation, and applications. In Proc. of ACM SIGCOMM Internet Measurement Conference (IMC '04), Taormina, Italy, 2004, 101--114. Google ScholarDigital Library
- Gupta, P. and McKeown, N. Algorithms for packet classification. IEEE Network, 15, 2 (2001), 24--32. Google ScholarDigital Library
- Srinivasan, V., Varghese, G., Suri, S., et al. Fast and scalable layer four switching. Computer Communication Review, 28, 4 (1998), 191--202. Google ScholarDigital Library
- Zhang, J., Yang, J., C. An, et al. Traffic Measurement and Analysis of TUNET. In Proc. of International Conference on Cyberworlds 2005, Singapore, 2005, 330--334. Google ScholarDigital Library
- Box, G., Jenkins, G, and Reinsel, G. Time Series Analysis, Forecasting and Control. Prentice-Hass, Englewood Cliffs, 1994. Google ScholarDigital Library
- Li, Y., Yang, J., An, C., Zhang, H., Finding Hierarchical Heavy Hitters in Network Measurement System, Technical Report, http://nmgroup.tsinghua.edu.cn/paper/liyq-sac07-full.pdf, Tsinghua University, P.R China, 2006.Google Scholar
Index Terms
- Finding hierarchical heavy hitters in network measurement system
Recommendations
Finding hierarchical heavy hitters in streaming data
Data items that arrive online as streams typically have attributes which take values from one or more hierarchies (time and geographic location, source and destination IP addresses, etc.). Providing an aggregate view of such data is important for ...
Using 2D Hierarchical Heavy Hitters to Investigate Binary Relationships
Visual Data MiningThis chapter presents VHHH: a visual data mining tool to compute and investigate hierarchical heavy hitters (HHHs) for two-dimensional data. VHHH computes the HHHs for a two-dimensional categorical dataset and a given threshold, and visualizes the HHHs ...
Online identification of hierarchical heavy hitters: algorithms, evaluation, and applications
IMC '04: Proceedings of the 4th ACM SIGCOMM conference on Internet measurementIn traffic monitoring, accounting, and network anomaly detection, it is often important to be able to detect high-volume traffic clusters in near real-time. Such heavy-hitter traffic clusters are often hierarchical (<i>ie</i>, they may occur at ...
Comments