Sigmetrics '05 Workshop on
Large Scale Network Inference (LSNI):
Methods, Validation, and Applications
In recent years, large scale network inference has attracted
significant interest within the research community. On one front, considerable
progress has been made on traffic matrix estimation. Solutions have been
proposed to estimate the amount of traffic flowing between any pair of ingress
and egress points within an IP network simply based on the total amount of
traffic recorded over IP links. On another front, efforts are being made
to detect the state of the network from end to end measurements using inference
techniques or to infer the traffic workload by exploiting application behavior.
In essence, the full instrumentation of the state of an IP network is still
considered a cost prohibitive task and inference may be the only tool we have to
understand the behavior of such large scale systems. The potential benefits of
the proposed estimation techniques can be great. Accurate measurement of an IP
traffic matrix is essential for network design and planning. Moreover, accurate
estimation of the network state can facilitate troubleshooting and performance
evaluation.
The focus of this workshop will be on the state of the art of large-scale
network inference techniques in these areas and their potential applications.
More specifically, we will solicit papers on the evaluation of the proposed
techniques and their possible shortcomings. Of particular interest are papers that
address the impact of estimation errors on the applications making use of
traffic matrices and network tomography techniques. Lastly, we are interested in
understanding the future of this area and its impact on network design and
management.
We solicit 6-page long papers that present work in progress related to the following topics:
traffic matrix estimation techniques
passive and active network tomography techniques
validation methods
scalability, robustness, sensitivity issues in large scale inference
adaptive and continuous network inference
practical applications and operational experience
impact of sampling errors on estimation accuracy
Important dates:
Submission: March 25th, 2005
Author Notification: May 5th, 2005
Camera ready due: May 27th, 2005
Workshop: June 6th, 2005 (tentative)
Submissions instructions:
Submission will be handled through EDAS. To register and upload your paper use http://edas.info/Paper.cgi?c=4497. The paper should not exceed 6 pages when formatted in two columns with a 10 point size font. The deadline for the submission is 23:59:59 EDT on March 25th 2005.Technical Program Committee
| Mark Coates | Dept. Electrical and Computer Engineering, McGill University, Canada |
| Mark Crovella | Computer Science Department, Boston University, U.S.A. |
| Nick Duffield | AT&T Research, U.S.A. |
| Mikael Johansson | Royal Institute of Technology, Sweden |
| George Michailidis | University of Michigan, U.S.A. |
| Antonio Nucci | Sprint Advanced Technology Labs, U.S.A. |
| Konstantina Papagiannaki | Intel Research Cambridge, U.K. |
| Matt Roughan | University of Adelaide, Australia |
| Kave Salamatian | LIP6, France |
| Don Towsley | University of Massachusetts, U.S.A. |
| Darryl Veitch | University of Melbourne/National ICT Australia (NICTA), Australia |
| Bin Yu | Department of Statistics, University of California Berkeley, U.S.A. |
| Yin Zhang | University of Texas, U.S.A. |
Workshop organizing chairs:
Dina
Papagiannaki, Intel Research Cambridge,
Yin
Zhang, Computer Science Department, University of Texas