1. Analysis of Network Distance Prediction with Global Network Positioning Mathieu Rodrigue Department of Computer Science University of Hartford
17/09/2018

2. Background
“Predicting Internet Network Distance With Coordinates-Based Approaches”
Triangulated Heuristic
IDMaps
Global Network Positioning (GNP)

3. IDMaps
Topological map of the internet
Query HOPS Servers to get distance
Consistent, but, many over predictions

4. Global Network Positioning
Reference points are disseminated known as “Landmarks”
Landmarks and hosts compute coordinates relative to each other by utilizing an objective function

5. Minimization
Objective function for Landmarks:

6. Minimization
Objective function for ordinary hosts:

7. Global Network Positioning
Coordinates are applied to nodes within the network
A standard vector distance function is utilized to predict network distance

8. Factors that affect results:
Methodology
Factors that affect results:
Number of dimensions
Number of Landmarks
Network speed when sending ICMP ping packets
Directional relative error:

9. Using the Planet Lab service:
Methodology
Using the Planet Lab service:
Choose five initial international reference points to serve as landmarks/tracers
Binghamton, New York
Hamburg, Germany
Christchurch, New Zealand
Taipei, Taiwan
Bologna, Italy
Choose two international hosts
Santa Cruz, California
Osaka, Japan

10. Results
Measured Distance Matrix in milliseconds

11. Results
Vector/Edge distances for Landmarks in two dimensions

12. Results
Vector/Edge distances for Landmarks in six dimensions

13. Results
Vector/Edge distances for Hosts in two dimensions

14. Results
Vector/Edge Distances for Hosts in six dimensions

15. Effective in higher dimensions
Conclusions
Effective in higher dimensions
Specifically six dimensions
Host predictions become inconsistent
Number of Landmarks?
Worth investigating for future work
Algorithm needs to be decentralized