Adaptive Sampling for Network Management

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.