Overview
The Internet has evolved from a small tightly controlled network
serving only a few users in the late 1970's to the immense decentralized
multi-layered collection of heterogeneous terminals, routers
and other platforms that we encounter today when surfing the
web. The lack of centralized control has allowed Internet service
providers (ISP)'s to develop a rich variety of user-services
at different quality-of-service (QoS) levels. However, in such
a decentralized environment quantitative assessment of network
performance is difficult. One cannot depend on the cooperation
of individual servers and routers to freely transmit vital network
statistics such as traffic rates, link delays, and dropped packet
rates. Indeed, an ISP may regard such information as highly
confidential. On the other hand, sophisticated methods of active
probing and/or passive monitoring can be used to extract useful
statistical quantities that can reveal hidden network structure
and detect and isolate congestion, routing faults, and anomalous
traffic. The problem of extracting such hidden information from
active or passive traffic measurements falls in the realm of
statistical inverse problems; an area which has long been of
interest to signal.
A fundamental ingredient in the successful design, control
and management of coming networks will be the accurate measurement
and characterization of its dynamics. For the task of network
management and monitoring, the computation cost is high, and
currently Yu's group has developped a pseudo likelihood approach
to cut down the computational cost. Also we view the data network
project for a constrained independent component analysis (CICA)
point of view. Our proposed research focuses on the following
goals:
- Development of rigorous statistical estimation methodology
for multicast internal link distribution through end-to-end
measurements.
- Develop and evaluate parametric and non-parametric estimators
for link-level performance metrics such as loss rate, delay
statistics.
- Multicast network topology identification.
- Network origin-destination (OD) matrix inference.
Talks and Papers
- R. Castro, M. Coates, G. Liang, R. Nowak, and B. Yu. (2003)
Internet Tomography
(Recently Development), Statistical Science (invited).
- G. Liang and B. Yu. (2003) Maximum Pseudo
Likelihood Estimation in Network Tomography, IEEE
Transaction on Signal Processing (special issue on data network).
- M. Coates, A. Hero, R. Nowak and B. Yu. (2002)
Large scale inference and tomography for network monitoring
and diagnosis, Signal Processing Magazine.
more...
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