Load Shedding in Network Monitoring Applications

USENIX 2007 - 2007 USENIX Annual Technical Conference

Volumn , Issue , 2007, Pages 59-72

  Barlet Ros, Pere ^a   Iannaccone, Gianluca ^b   Sanjuàs Cuxart, Josep ^a   Amores López, Diego ^a   Solé Pareta, Josep ^a  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

^b INTEL RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING;

DATA STREAM; IN NETWORKS; LOAD-SHEDDING; MONITORING APPLICATIONS; MONITORING SYSTEM; NETWORK MONITORING; NETWORK TRAFFIC; OPERATING DATA; REAL TIME NETWORK DATA; TRAFFIC QUERIES;

INTRUSION DETECTION;

EID: 84888470618     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. BAR-YOSSEF, Z., JAYRAM, T. S., KUMAR, R., SIVAKUMAR, D., AND TREVISAN, L. Counting distinct elements in a data stream. In Proc. of Intl. Workshop on Randomization and Approximation Techniques (2002), pp. 1-10.
2. BARAKAT, C., IANNACCONE, G., AND DIOT, C. Ranking flows from sampled traffic. In Proc. of CoNEXT (2005), pp. 188-199.
3. BARLET-ROS, P., IANNACCONE, G., SANJUÀS-CUXART, J., AMORES-LÓPEZ, D., AND SOLÉ-PARETA, J. Predicting resource usage of arbitrary network traffic queries. Tech. rep., Technical University of Catalonia, 2006. http://loadshedding.ccaba.upc.edu/prediction.pdf.
4. BLOOM, B. H. Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13, 7 (1970), 422-426.
5. CARNEY, D., ET AL. Monitoring streams - a new class of data management applications. In Proc. of Intl. Conf. on Very Large Data Bases (2002), pp. 215-226.
6. CARTER, J. L., AND WEGMAN, M. N. Universal classes of hash functions. Journal of Computer and System Sciences 18, 2 (1979), 143-154.
7. CHANDRASEKARAN, S., ET AL. TelegraphCQ: Continuous dataflow processing of an uncertain world. In Proc. of Conf. on Innovative Data Systems Research (2003).
8. CHI, Y., YU, P. S., WANG, H., AND MUNTZ, R. R. Loadstar: A load shedding scheme for classifying data streams. In Proc. of SIAM Intl. Conf. on Data Mining (2005).
9. CISCO SYSTEMS. NetFlow services and applications. White Paper, 2000.
10. DILLON, W. R., AND GOLDSTEIN, M. Multivariate Analysis: Methods and Applications. John Wiley and Sons, 1984.
11. DUFFIELD, N. Sampling for passive internet measurement: A review. Statistical Science 19, 3 (2004), 472-498.
12. DUFFIELD, N., LUND, C., AND THORUP, M. Flow sampling under hard resource constraints. In Proc. of ACM Sigmetrics (2004), pp. 85-96.
13. ENDACE. http://www.endace.com.
14. ESTAN, C., KEYS, K., MOORE, D., AND VARGHESE, G. Building a better NetFlow. In Proc. of ACM Sigcomm (2004), pp. 245-256.
15. ESTAN, C., VARGHESE, G., AND FISK, M. Bitmap algorithms for counting active flows on high speed links. In Proc. of ACM Sigcomm Conf. on Internet Measurement (2003), pp. 153-166.
16. IANNACCONE, G. Fast prototyping of network data mining applications. In Proc. of Passive and Active Measurement (2006).
17. INTEL CORPORATION. The IA-32 Intel Architecture Software Developer-s Manual, Volume 3B: System Programming Guide, Part 2. 2006.
18. KEYS, K., MOORE, D., AND ESTAN, C. A robust system for accurate real-time summaries of internet traffic. In Proc. of ACM Sigmetrics (2005), pp. 85-96.
19. L-ANELLA CI ENTÍFICA (THE SCI ENTI FIC RI NG). http://www.cesca.es/en/comunicacions/anella.html.
20. NLANR: NATIONAL LABORATORY FOR APPLIED NETWORK RESEARCH. http://www.nlanr.net.
21. PRESS, W. H., TEUKOLSKY, S. A., VETTERLING, W. T., AND FLANNERY, B. P. Numerical Recipes in C: The Art of Scientific Computing, 2nd ed. 1992.
22. REISS, F., AND HELLERSTEIN, J. M. Declarative network monitoring with an underprovisioned query processor. In Proc. ofIntl. Conf. on Data Engineering (2006), pp. 56-67.
23. TATBUL, N., ET AL. Load shedding in a data stream manager. In Proc. of Intl. Conf. on Very Large Data Bases (2003), pp. 309-320.
24. WELSH, M., CULLER, D. E., AND BREWER, E. A. SEDA: An architecture for well-conditioned, scalable internet services. In Proc. of ACM Symposium on Operating System Principles (2001), pp. 230-243.
25. WHANG, K.-Y., VANDER-ZANDEN, B. T., AND TAYLOR, H. M. A linear-time probabilistic counting algorithm for database applications. ACM Trans. Database Syst. 15, 2 (1990), 208-229.
26. YU, L., AND LIU, H. Feature selection for high-dimensional data: A fast correlation-based filter solution. In Proc. of Intl. Conf. on Machine Learning (2003), pp. 856-863.