Adaptive Sampling for Network Management
E. Hernandez
205 SW 75th Street
Apartment 6K
Gainesville, FL 32607, USA
Email: ehernand_AT_ufl.edu
M. Chidester
University of Florida
Department of Electrical and Computer Engineering
P.O. Box 116200
Gainesville, FL 32611, USA
Email: chideste_AT_hcs.ufl.edu
A. George
University of Florida
Department of Electrical and Computer Engineering
P.O. Box 116200
327 Larsen Hall
Gainesville, FL 32611, USA
Email: george_AT_hcs.ufl.edu
Abstract
High-performance networks require sophisticated management systems to
identify sources of bottlenecks and detect faults. At the same time, the
impact of network queries on the latency and bandwidth available to the
applications must be minimized. Adaptive techniques can be used to control
and reduce the rate of sampling of network information, reducing the amount
of processed data and lessening the overhead on the network. Two adaptive
sampling methods are proposed in this paper based on linear prediction and
fuzzy logic. The performance of these techniques is compared with
conventional sampling methods by conducting simulative experiments using
Internet and videoconference traffic patterns. The adaptive techniques are
significantly more flexible in their ability to dynamically adjust with
fluctuations in network behavior, and in some cases they are able to reduce
the sample count by as much as a factor of two while maintaining the same
accuracy as the best conventional sampling interval. The results illustrate
that adaptive sampling provides the potential for better monitoring,
control, and management of high-performance networks with higher accuracy,
lower overhead, or both.
Keywords: adaptive sampling, fuzzy logic, linear prediction, network management, SNMP
JNSM: Vol. 9, No. 4, 2001
Adaptive Sampling for Network Management [Vol. 9, No. 4, 2001]
NOTE: only abstract of paper available on-line; please contact your library or the authors for the full paper
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