A Measurement Study of Internet
Bottlenecks
Ningning Hu (CMU)
Joint work with
Li Erran Li (Bell Lab)
Zhuoqing Morley Mao (U. Mich)
Peter Steenkiste (CMU)
Jia Wang (AT&T)
Ningning Hu
Carnegie Mellon University
1
Motivation
Recent research progress on active probing makes
it possible to locate bandwidth bottlenecks
1. How persistent are the Internet bottlenecks?
 Important for measurement frequency
2. What relationship exists between bottleneck and
packet loss and queuing delay?
 Useful for congestion identification
3. Are bottlenecks shared by end users within the
same prefix?
 Useful for path bandwidth inference
4. What causes intra-AS bottlenecks?
 Important for traffic engineering
Ningning Hu
Carnegie Mellon University
2
Pathneck
Bottleneck
 Bottleneck Link: the link with the smallest available bandwidth
on a network path
 Bottleneck Router: the downstream router of a bottleneck link
Pathneck
 An active probing tool that can detect Internet bottleneck
location effectively and efficiently
 For details, please refer to
“Locating Internet Bottlenecks: Algorithms,
Measurements, and Implications” [SIGCOMM’04]
 Source code: www.cs.cmu.edu/~hnn/pathneck
Pathneck output used in this work
 Bottleneck link location
 Route
Ningning Hu
Carnegie Mellon University
3
Data collection
cmu
D
D
D
D
D
D
D
S
960
Internet
Day-1
Destinations
Probing
 Source: a CMU host
 Destinations: 960 diverse IP addresses
 10 continuous probings for each
destination (1.5 minutes)
Repeat for 38 days (for persistence study)
Limitations
 Pathneck can not cover the last hop
 960 << # of Internet paths
Ningning Hu
Carnegie Mellon University
Day-2
…
Day-38
4
Outline
1. How persistent are the Internet bottlenecks?
Route persistence
Bottleneck persistence
2. What relationship is between bottleneck and
packet loss and queuing delay?
3. Are bottlenecks shared by end users within the
same prefix?
4. What causes intra-AS bottlenecks?
Ningning Hu
Carnegie Mellon University
5
Terminology
probing
Day 1
Day 2
Day 3
Day 4
Day 5
Day 6
Day 7
Day 8
Day 9
probing set (persistent)
not persistent
Consider both AS-level route and locationlevel route
Ningning Hu
Carnegie Mellon University
6
Route Persistence
AS level
Location level
Route change is very common and must be
considered for bottleneck persistence
analysis
 Consistent with the results from Zhang, et. al.
[IMW-01] on route persistence
Ningning Hu
Carnegie Mellon University
7
Bottleneck Persistence
Persistence of a bottleneck router
# of persistent probing sets R is bottleneck
Persist(R) =
# of persistent probing sets R appears
Bottleneck Persistence of a path
Max(Persist(R)) for all bottleneck router R
Two views:
1. End-to-end view ― per (src, dst) pair
 Includes the impact of route change
2. Route-based view ― per route
 Removes the impact of route change
Ningning Hu
Carnegie Mellon University
8
Bottleneck Persistence
3
2
1
2
1. Bottleneck persistence in route-based view is higher than
end-to-end view
2. AS-level bottleneck persistence is very similar to that from
location level
3. 20% bottlenecks have perfect persistence in end-to-end
view, and 30% for route-based view
Ningning Hu
Carnegie Mellon University
9
Outline
1. How persistent are the Internet
bottlenecks?
2. What relationship exists between
bottleneck and packet loss and queuing
delay?
3. Are bottlenecks shared by end users
within the same prefix?
4. What causes intra-AS bottlenecks?
Ningning Hu
Carnegie Mellon University
10
Motivation
Possible congestion indication
 Large queuing delay
 Packet loss
 Bottleneck
They do not always occur together
 Packet scheduling algorithm  large queuing
delay
 Traffic burstiness or RED  packet loss
 Small link capacity  bottleneck
Bottleneck ? link loss | large link delay
Ningning Hu
Carnegie Mellon University
11
Method
Collected on the same set of 960 paths, but
independent measurements
1. Detect bottleneck location using Pathneck
2. Detect loss location using Tulip
 Only use the forward path results
3. Detect link queuing delay using Tulip
 medianRTT – minRTT
[ Tulip was developed in University of Washington, SOSP’03 ]
Our analysis is based on the 382 paths for which
both bottleneck location and packet loss are
detected
Ningning Hu
Carnegie Mellon University
12
Bottleneck  Packet Loss
Perfectly
correlated 30%
Ningning Hu
60% |Dist| <= 2
Carnegie Mellon University
13
Bottleneck  Link Delay
3% non-bottlenecks have delay > 5ms
15% bottlenecks have delay > 5ms
Ningning Hu
Carnegie Mellon University
14
More Results
1. How persistent are the Internet
There is not much sharing within common cluster
bottlenecks?
observe
clear correlation
withbetween
link load, while
2. We
What
relationship
exists
observing
no clear
with and
link capacity,
bottleneck
andrelationship
packet loss
queuing
router CPU load, and memory usage.
delay?
3. Are bottlenecks shared by end users
within the same prefix?
4. What causes intra-AS bottlenecks?
Ningning Hu
Carnegie Mellon University
15
Related Work
Persistence of Internet path properties
 Zhang [IMW-01], Paxson [TR-2000], Labovitz [TON-1998,
Infocom-1999]
Congestion points sharing
 Katabi [TR-2001], Rubenstein [Sigmetrics-2000]
Correlation among Internet path properties
 Paxson [1996]
Correlation between router and link properties
 Agarwal [PAM 2004]
Ningning Hu
Carnegie Mellon University
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Conclusion
Only 20-30% Internet bottlenecks have perfect
persistence

Application should be ready for bottleneck location change
Bottleneck locations have a fairly strong (60%)
correlation with packet loss locations

Bottleneck and loss detections should be used together for
congestion detection
End users within common cluster share
bottlenecks only with a low probabilityh

End user can not assume common bottlenecks
We observe evidence of a correlation between
bottleneck and link loads

Network engineers should focus on traffic load to eliminate
bottlenecks
Ningning Hu
Carnegie Mellon University
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