Measurement-based network monitoring and surveillance is an important area for providing reliability and security to IP network infrastructure. A challenge is how to develop scalable monitoring approaches.
This Project will investigate the scalability and performance of measurement-based network monitoring. At the high level scalability relates to the growth rate of monitoring resource with respect to the size of a network. The performance characterizes the accuracy of network monitoring. The scalability and performance together impact the feasibility of such approaches to large networks.
The PI will use a statistical learning framework to investigate the issues of scalability and performance. She will formally define and analyze scalability, and provide understanding on when and why scalability may be achieved. She will then investigate the trade-off between scalability and performance, and provide guidelines for developing good measurement-based approaches for large networks.
Although there have been many efforts to develop measurement-based methods for network monitoring, the scalability and performance issues have been investigated little. Meanwhile, though statistical learning theory has been applied to many areas, it has not been widely used in measurement-based network monitoring. The proposed investigation hopes to benefit measurement-based network monitoring, and to enrich the statistical learning theory also. This research will be done in Collaboration with Anwar Elwalid at Bell-Labs Lucent Technologies.
