Transcript Available Bandwidth and TCP Throughput

Locating Internet Bottlenecks:
Algorithms, Measurement, and
Implications
Ningning Hu (CMU)
Li Erran Li (Bell Lab)
Zhuoqing Morley Mao (U. Mich)
Peter Steenkiste (CMU)
Jia Wang (AT&T)
SIGCOMM’04
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Goal
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
Locate network bottleneck along end-to-end
paths
With such information, network operators can
improve routing
2
Difficulties
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End users cannot gain information of network
internals
High measurement overhead
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Proposed algorithm – Pathneck
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Pathneck is an active probing tool
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Low overhead (i.e., in order of 10s-100s KB)
Fast (i.e., in order of seconds)
Single-end control (sender only)
High accuracy
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Outline
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Algorithm
Internet validation
Testbed validation
Internet measurement
Applications
Conclusion
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Definition
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Bottleneck link
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Available bandwidth
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Residual bandwidth
Choke link
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Link with smallest available bandwidth
R1
L1
R2
L2
R3
Link has lower available bandwidth than the
partial path from source to that link
Choke point
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Upstream router of choke link
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Definition
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Last choke link is bottleneck link
R1
R2
L1
R3
L2
R4
L3
Choke link
R5
L4
R6
L5
R7
L6
Choke link
/ Bottleneck
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Recursive Packet Train (PRT) in Pathneck
measurement
packets
1 2
measurement
packets
Load packets
30 255
255
255
60 pkts, 500 B
30 pkts, 60 B
TTL
255
30
2 1
30 pkts, 60 B
UDP packets
Load packets are used to measure available
bandwidth
Measurement packets are used to obtain location
information
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Gap value
Sender
Router
Packet train
Time axis
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Gap value
Sender
Router
Drop m. packet
Send ICMP
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Gap value
Sender
Router
Drop m. packet
Send ICMP
Recv ICMP
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Gap value
Sender
Router
Drop m. packet
Send ICMP
Drop m. packet
Send ICMP
Recv ICMP
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Gap value
Sender
Router
Drop m. packet
Send ICMP
Drop m. packet
Send ICMP
Recv ICMP
Recv ICMP
Gap value
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Train length
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Link capacity
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train_rate > a_bw  train_length increases
train_rate ≤ a_bw  train_length keeps same
Traffic load
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Heavily loaded  train_length increases
Lightly loaded  train_length keeps same
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gap values are the
raw measurement
Transmission of RPT
S
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255
255
255
255
255
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2
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R1
0
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2
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254
254 g1254
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254
3
2
1
0
0
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2
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253 g2253
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253
2
1
0
R2
R3
1
2
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253
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253
2
1
0
1
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252g3
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252
1
0
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Inference Model – Step 1
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Label gap sequence
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Remove data if cannot
get both ICMP
Remove the entire
probing data if cannot get
more than half routers on
path
Fix hill and valley point
Given a certain of steps,
minimize the total
distance between
individual values and the
average step values
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Inference Model – Step 2
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Confidence Threshold (conf)
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Percentage change of available bandwidth
To filter out the gap measurement noise
Default: conf ≥ 10% available bandwidth change
Detection Rate (d_rate)
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# positive probing / # total probing
A hop must appear as a choke point for at least M times
(d_rate ≥ M/N)
To select the most frequent choke point
Default: d_rate ≥ 5/10 = 50%
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Inference Model – Step 3
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Rank choke points
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Bottleneck is the choke point with largest gap
value
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Pathneck – configuration
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Each probing set contains 30 - 100 packets
Probe the same destination 6 - 10 times
Each probing set take one RTT (wait for 3
seconds, max RTT)
conf ≥ 10% filtering
d_rate ≥ 50% filtering
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Output from Pathneck
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Bottleneck location (last choke point)
Upper or lower bound for the link available
bandwidth
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Based on the gap values from each router (details
in the paper)
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Limitations
 Cannot measure the last hop
Limited ICMP rate
 ICMP packet generation time and reverse
path congestion can introduce measurement
error
Generation time is insignificant
Filter out measurement outliers
Send ICMP
Drop m. packet
Send ICMP
Recv ICMP Drop m. packet
Send ICMP
True Gap value
Measured Gap value
Recv ICMP
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Limitations
 Packet loss and route change will disable the
measurements
Multiple probings can help
 Cannot pass firewalls
Similar to most other tools and usually not
bottleneck
 Bias towards early choke points
If change is insignificant, filtered out by
confidence threshold
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Validation
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Internet validation
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Abilene network
Testbed validation
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Emulab, a fully controlled environment
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Internet validation (Abilene)
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Source: CMU and University of Utah
22 probing destination for each source
Each 11 major routers on the Abilene
backbone is included in at least one probing
path
Each destination, probe 100 times with a 2second interval between consecutive probing
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Internet validation (Abilene)
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Detect only 5 non-first hop bottleneck
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Abilene paths are over-provisioned
Detected bottleneck are outside Abilene
network, so it cannot be verified
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Testbed validation (Emulab)
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100 probing sets
Use the result
received all
ICMP
Entire probing
interval is about
1 min
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Comparing impact of capacity and load
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Left figure
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Right figure
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Fix X to 50Mbps
Vary Y from 21 to 30
Mbps with step size
1Mbps
Set X and Y to 50Mbps
Very CBR loads to Y
from 29 to 20 Mbps
Bottleneck available
bandwidth change from
21 to 30Mbps
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Testbed validation (Emulab)
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Probing set can
identify Y as
bottleneck
86 individual
probing: 7  X
(correct), 65  Y
(correct), 14  X
(incorrect)
Due to small
difference
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Testbed validation (Emulab)
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67  X (correct),
2  Y (correct),
8  X (incorrect)
Due to small
difference
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Testbed validation (Emulab)
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Measurement Methodology
Probing sources
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Probing destinations
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Over 3,000 destinations from each source
Covers as many distinct AS paths as possible
10 probings for each destination
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58 probing sources (from PlanetLab & RON)
conf  10%, d_rate  50%
Duration is within 2 days
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Popularity
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<2% paths report more
than 3 choke links
Popularity = # positive
probe of link b / # probe
that traverse link b
Half of choke links are
detected in 20% or less
Cannot detect
sometimes due to
bursty traffic (filtered)
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Bottleneck Distribution
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Common Assumption: bottlenecks are most likely
to appear on the peering and access links, i.e.,
on Inter-AS links
Identifying Inter/Intra-AS links
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Only use AS# is not enough (Mao et al [SIGCOMM03])
We define Intra-AS links as links at least one hop away
from links where AS# changes
Two types of Inter-AS links: Inter0-AS & Inter1-AS links
We identify a subset of the real intra-AS links
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Bottleneck Distribution (cont.)
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Up to 40% of bottleneck links are Intra-AS
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Consistent with earlier results [Akella et al IMC03]
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Location
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Stability
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Sample 30 destination
randomly
Divide 3 hour
measurement into 9
epochs of 20 minute
each
Each epoch, run 5
probing trains
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Conclusion
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Pathneck is effective and efficient in locating
bottlenecks
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Sender modified, low overhead
Up to 40% of bottleneck links are Intra-AS
54% of the bottlenecks can be inferred
correctly
Guide Overlay and multihoming
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References
• http://www.cs.cmu.edu/~hnn/pathneck
• Ningning Hu and et. al., “Locating Internet
Bottleneck: Algorithms, Measurements,
and Implications,” SIGCOMM’04
• Related technical report
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Abilene Network Map
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MRTG for Abilene Network
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Table 4: Probing sources from
PlanetLab (PL) and RON
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Table 4: Probing sources from
PlanetLab (PL) and RON
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Impact of configuration parameters
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Inference
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S
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Help to reduce the measurement overhead

R

R
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R
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R
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R

D
54% of inferences are successful for
12,212 paths with “enough information”
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Inference
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Take lowest upper bound and highest lower
bound
Include upper bound if standard deviation is
less than 20% of average
Divide into training set and testing set
Exclude if testing set cannot identify
bottleneck
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Inference
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