1. By Cheng-Lin Tsao Felipe Santos Ruhull Alam Bhuiyan
Planet Lab By
Cheng-Lin Tsao
Felipe Santos
Ruhull Alam Bhuiyan

2. Context and Problem
The purpose of this project is to analyze the path diversity of real Internet and evaluate its potential benefits.
Path Analysis
To collect data using scriptroute on each member
Diversity Analysis
Determine diversity of paths between any source and destination pairs
Overlay Routing
Analyze the potential benefits of multi-path routing in overlay network

3. ‘gt_siva’ Slice Architecture
Planet Lab
1. We use the local machine to collect data from servers from all the members
2. We analyze the diversity on the local machine
3. We analyze multi-path routing with source routing server and client pairs in each member. Explain the term member, which is the node in our slice. Differentiate between member and intermediate nodes.
Stage 2: Diversity Analysis
Stage 1: Path Analysis
Stage 3: Overlay Routing
Local Machine

4. Our Member Nodes How did choose the members?
Ansser: a. We could login to the server as the key was been able to propagate through the server.
b. We can collect data with the member.
c. To establish geological diversity in our members.
d. We have node in Asia, North America, South America, Europe, Australia.

5. Work outline

6. Work done Scriptroute : Ruby Script
Platform to deploy and combine multiple network test tools simultaneously
Deployed Scriptroute to 41 nodes (in our slice)
sr-quartet – each measurement consists of a random variable whose standard deviation and variance are taken into account in order to minimize error – in our tests average of 5 – 10%

7. Work done Need to physically locate each node
Initially planed on using the built-in scriptroute function built-in to sr-rockettrace to determine location – most nodes/hops failed to return its location
Netgeo (
Database and collection of Perl scripts used to map IP addresses and AS numbers to geographical locations
Performs multiple WHOIS lookups to ARIN/APNIC/RIPE for the specified IP address and those it believes are geographically close to the specified address and returns latitude/longitude, city, state, country …
Java API – simply call method providing an IP address and it returns the node location

8. Work done
Path Analysis

9. Work done Diversity Analysis Breadth-first-search D A B C E
Node-disjoint paths
Bottleneck-disjoint paths
Breadth-first-search
Preferring fewer intermediate members
Element = list of intermediate members
Starting with empty list
Generating sub-trees
Pruning sub-trees C D E CD CE DC EC

10. Work done Overlay routing Using data from path analysis
Setting up routes according to diversity analysis
Virtual channel
TCP sockets
Measure achievable end-to-end throughput D A B
data
data
data
data
data
data
data
data
data
data
data
ACEB
data
data
data
data E C L

11. Results Path Analysis (number of paths) Member To From GT, US 19
Duke, US
MIT, US
Stanford, US 21
Internet2-Chicago, US 18
Internet2-Huston, US
Internet2-Seattle, US
AT&T, US 17
McGill, US 20
TRLab, Canada
TU-Berlin, Germany
Member To
From
U. of Zunich, Switzerland 21
U. of London, UK
Tech. U. of Madrid, Spain
IBBT Ghent U., Belgium
Academia Sinica, Taiwan 19
Kaist, Korea
U. of Tokyo, Japan
U. of HK, Hong Kong
Tsinghua U., China 16 17
USTC, China
RNP, Brazil

12. Results Diversity Analysis No node-disjoint paths found
Single outbound router (default gateway)
Some have multiple inbound routers
Member
Out In GT 1
Duke
MIT 3
Stanford
I-Chicago 4
I-Huston
I-Seattle
AT&T 2*
Member
Out In
McGill 1 2*
TRLab
TU-Berlin
Unizh
ULondon
UPM
IBBT
Sinica 2
Member
Out In
Kaist 1
UTokyo
HKU 2
THU
USTC 3*
RNP
*: one router unknown

13. Results Diversity analysis
18 bottleneck-disjoint paths (BDP) GTStanford
GTStfd
GTMITStfd
GTI-ChicagoStfd
GTI-HustonStfd
GTI-SeattleStfd
GTAT&TStfd
GTMcGillStfd
GTTRLabStfd
GTIBBTStfd
GTSinicaStfd
GTUTokypStfd
GTHKUStfd
GTRNPStfd
GTDukeKAISTStfd
GTUPMI-HustonULondonStfd
GTI-HustonTRLabAT&TULondonStfd
GTI-HustonTHUAT&TULondonStfd
GTI-HustonUSTCAT&TULondonStfd

14. Results Overlay Routing Throughput of bottleneck-disjoint paths
New paths don’t affect old paths
GTStanford 1 2 4 8
Direct
3.682
3.681
3.532
3.129
MIT
2.643
2.571
2.308
I-Chicago
3.441
3.1
I-Huston
5.264
4.673
I-Seattle
3.291
AT&T
1.218
McGill
2.4
TRLab
2.316
time: 100 sec
unit: Mbps

15. Results Overlay Routing BDP > random alternative paths
Improve 700%+ throughput over direct

16. Overall implications Some middle-boxes affect path analysis
Node-disjoint paths are possible but need support from network layer
Multiple bottleneck-disjoint paths exist between two members
Bottleneck-disjoint paths aggregate bandwidth through independent bottlenecks
Bottleneck-disjoint paths provide higher throughput than random alternative paths

17. Summary and Future Works
Collected path data on PlanetLab
Analyzed path diversity
Evaluated overlay routing
Future works
More accurate path analysis, location information
Online diversity analysis
Node-disjoint paths by setting the routing table
Considering bandwidth/delay/jitter/loss in algorithm