1 Locating Internet Bottlenecks: Algorithms, Measurement, and Implications Ningning Hu (CMU) Li...
Transcript of 1 Locating Internet Bottlenecks: Algorithms, Measurement, and Implications Ningning Hu (CMU) Li...
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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
Locate network bottleneck along end-to-end paths
With such information, network operators can improve routing
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Difficulties
End users cannot gain information of network internals
High measurement overhead
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Proposed algorithm – Pathneck Pathneck is an active probing tool
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
Algorithm Internet validation Testbed validation Internet measurement Applications Conclusion
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Definition
Bottleneck link Link with smallest available bandwidth
Available bandwidth Residual bandwidth
Choke link Link has lower available bandwidth than the
partial path from source to that link Choke point
Upstream router of choke link
R1 R2 R3L1 L2
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Definition
Last choke link is bottleneck link
R1 R2 R3
L1 L2
R4 R5
L3 L4 L5 L6
R6 R7
Choke link Choke link/ Bottleneck
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Recursive Packet Train (PRT) in Pathneck
Load packets
60 pkts, 500 B
TTL
255255255255
measurement packets
measurement packets
30 pkts, 60 B 30 pkts, 60 B
2 130301 2
Load packets are used to measure available bandwidth
Measurement packets are used to obtain location information
UDP packets
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Gap value
RouterSender
Packet train
Time axis
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Gap value
RouterSender
Drop m. packetSend ICMP
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Gap value
RouterSender
Drop m. packetSend ICMP
Recv ICMP
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Gap value
RouterSender
Drop m. packetSend ICMP
Recv ICMP
Drop m. packetSend ICMP
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Gap value
RouterSender
Drop m. packetSend ICMP
Recv ICMP
Drop m. packetSend ICMP
Recv ICMP
Gap value
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Train length
Link capacity train_rate > a_bw train_length increases train_rate ≤ a_bw train_length keeps same
Traffic load Heavily loaded train_length increases Lightly loaded train_length keeps same
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Transmission of RPT2551 2 3 4 4 3 2 1255 255 255 255
2541 2 3 3 2 1254 254 254 254
2531 2 2 1253 253 253 253
R1
S
R2
R3
0 0
0 0
0 0
g1
g2
g3
2532 2253 253 253 2531 1
2521 1252 252 252 252
gap values are the raw measurement
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Inference Model – Step 1
Label gap sequence 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
Confidence Threshold (conf) Percentage change of available bandwidth To filter out the gap measurement noise Default: conf ≥ 10% available bandwidth change
Detection Rate (d_rate) # 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
Rank choke points Bottleneck is the choke point with largest gap
value
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Pathneck – configuration
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
Bottleneck location (last choke point) Upper or lower bound for the link available
bandwidth 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 errorGeneration time is insignificantFilter out measurement outliers
Drop m. packet Recv ICMP Drop m. packetSend ICMP
Recv ICMP
Measured Gap valueTrue Gap value
Send ICMP Send ICMP
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Limitations
Packet loss and route change will disable the measurementsMultiple probings can help
Cannot pass firewallsSimilar to most other tools and usually not
bottleneck
Bias towards early choke pointsIf change is insignificant, filtered out by
confidence threshold
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Validation
Internet validation Abilene network
Testbed validation Emulab, a fully controlled environment
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Internet validation (Abilene)
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 2-second interval between consecutive probing
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Internet validation (Abilene)
Detect only 5 non-first hop bottleneck Abilene paths are over-provisioned
Detected bottleneck are outside Abilene network, so it cannot be verified
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Testbed validation (Emulab)
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 Left figure
Fix X to 50Mbps Vary Y from 21 to 30
Mbps with step size 1Mbps
Right figure 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)
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)
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 58 probing sources (from PlanetLab & RON)
Probing destinations Over 3,000 destinations from each source Covers as many distinct AS paths as possible
10 probings for each destination conf 10%, d_rate 50%
Duration is within 2 days
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Popularity
<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
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 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.)
Up to 40% of bottleneck links are Intra-AS Consistent with earlier results [Akella et al IMC03]
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Location
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Stability
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
Pathneck is effective and efficient in locating bottlenecks 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
54% of inferences are successful for 12,212 paths with “enough information”
S DR RR R R
Help to reduce the measurement overhead
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Inference
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