WIDE Decentralized and Wireless Control of Large-Scale Systems
Diagnosing: Home Wireless & Wide-area Networks
Transcript of Diagnosing: Home Wireless & Wide-area Networks
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Diagnosing:Home Wireless &
Wide-area NetworksPartha Kanuparthy, Constantine Dovrolis
Georgia Institute of Technology
1Monday, February 13, 2012
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Two PartsDiagnosing home wireless networks [CCR’12]
Joint work between GT, Telefonica, CMU
Diagnosing wide-area networks [in-progress]
Joint work with Constantine Dovrolis
and a quick update on ShaperProbe
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Diagnosing Home Wireless
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Home 802.11 NetworksUbiquitous: most residential e2e paths start/end with 802.11 hopUse a shared channel across devices
infrastructure, half-duplex
Co-exist with neighborhood wireless and non-802.11 devices (2.4GHz cordless, Microwave ovens, ...)
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802.11 Performance Problems
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802.11 Performance Problems
Wireless clients see problems:
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802.11 Performance Problems
Wireless clients see problems:Low signal strength (due to distance, fading and multipath)
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802.11 Performance Problems
Wireless clients see problems:Low signal strength (due to distance, fading and multipath)Congestion (due to shared channel)
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802.11 Performance Problems
Wireless clients see problems:Low signal strength (due to distance, fading and multipath)Congestion (due to shared channel)Hidden terminals (no carrier sense)
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802.11 Performance Problems
Wireless clients see problems:Low signal strength (due to distance, fading and multipath)Congestion (due to shared channel)Hidden terminals (no carrier sense)Non-802.11 interference (microwave, cordless, ...)
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WLAN-ProbeWe diagnose 3 performance pathologies:
congestion, low signal strength, hidden terminals
Tool: WLAN-Probesingle 802.11 prober
user-level: works with commodity NICs
no special hardware or administrator requirements
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WLAN-ProbeWe diagnose 3 performance pathologies:
congestion, low signal strength, hidden terminals
Tool: WLAN-Probesingle 802.11 prober
user-level: works with commodity NICs
no special hardware or administrator requirements
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WLAN-ProbeWe diagnose 3 performance pathologies:
congestion, low signal strength, hidden terminals
Tool: WLAN-Probesingle 802.11 prober
user-level: works with commodity NICs
no special hardware or administrator requirements
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WLAN-ProbeWe diagnose 3 performance pathologies:
congestion, low signal strength, hidden terminals
Tool: WLAN-Probesingle 802.11 prober
user-level: works with commodity NICs
no special hardware or administrator requirements
6Monday, February 13, 2012
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Life of 802.11 PacketDelays in a busy channel:
channel busy-wait delay
Delays in presence of bit errors:L2 retransmissions
random backoffs
Unavoidable variable delays:TX-delay(s) (based on L2 TX-rate)
802.11 ACK receipt delay
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Life of 802.11 PacketDelays in a busy channel:
channel busy-wait delay
Delays in presence of bit errors:L2 retransmissions
random backoffs
Unavoidable variable delays:TX-delay(s) (based on L2 TX-rate)
802.11 ACK receipt delay
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Life of 802.11 PacketDelays in a busy channel:
channel busy-wait delay
Delays in presence of bit errors:L2 retransmissions
random backoffs
Unavoidable variable delays:TX-delay(s) (based on L2 TX-rate)
802.11 ACK receipt delay
Usually implemented in NIC firmware
Can we measure these delays?
Yes!
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Access Delaybusy-wait re-TXs backoffs TX-delay ACKs
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Access Delaybusy-wait re-TXs backoffs TX-delay ACKs
rate adaptation!
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Access Delay
Captures channel “busy-ness” and channel bit errors
excludes 802.11 rate modulation effects
d = OWD - (TX delay)
busy-wait re-TXs backoffs TX-delay ACKs
rate adaptation!
first L2 transmission
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Access Delay
Captures channel “busy-ness” and channel bit errors
excludes 802.11 rate modulation effects
d = OWD - (TX delay)
busy-wait re-TXs backoffs TX-delay ACKs
rate adaptation!
first L2 transmission
??
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Access Delay: TX delay
d = OWD - (TX delay)TX-rate?
send 50-packet train with few tiny packets
use packet pair dispersion to get TX-rate:
current busy-wait delays
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Access Delay: noise?d = OWD - (TX delay)
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Access Delay: noise?Dispersion underestimates:
due to re-TXs, busy-waits, etc.
d = OWD - (TX delay)
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Access Delay: noise?Dispersion underestimates:
due to re-TXs, busy-waits, etc.
Insight: TX-rate typically remains same at timescales of a single train
d = OWD - (TX delay)
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Access Delay: noise?Dispersion underestimates:
due to re-TXs, busy-waits, etc.
Insight: TX-rate typically remains same at timescales of a single trainFind a single rate for the train!
d = OWD - (TX delay)
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Diagnosis
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Size-dependent Pathologies
Low signal strengthHidden terminals Congestion
Bit errors increase with packet size:Higher percentile access delays show trends.
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Hidden TerminalsHidden terminals respond to frame corruption
by random backoffs
Look at immediate neighbors of large delay or lost (L3) packets
hidden terminal: neighbor delays are small
low SNR: neighbors are similar
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Hidden TerminalsDefine two measures:
pu = P [ high delay or L3 loss ]
pc = P [ neighbor is high delay or L3 loss | high delay or L3 loss ]
Hidden terminal:pc ≈ pu
time
Acce
ss de
lay
t imeAc
cess
dela
y
Hiddenterminal(s)
Low SNR
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Hidden Terminals
Hidden terminal: pc ≈ pu Low SNR:
pc ≫ pu
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SummaryWLAN-Probe: tool for user-level diagnosis of 802.11 pathologies
Single 802.11 probing point
Commodity NICs
No kernel/admin-level changes
Extensions:wide-area probing for 802.11 diagnosis? (“M-Lab”)
passive (TCP) inference?
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Pythia:Detection, Localization, Diagnosis
of Wide-area Performance Problems
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Pythia:one tool, three objectives
Data analysis tool (e.g , perfSONAR data)Funded by DoEDetection: “noticeable loss rate between ORNL and SLAC on 07/11/11 at 09:00:02 EDT”
Localization“it happened at DENV-SLAC link”
Diagnosis“it was due to insufficient router buffers”
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Pythia: ApproachExisting diagnosis systems mine patterns and dependencies in large-scale network data (e.g., AT&T’s G-RCA)Can we use domain knowledge?
useful in inter-domain diagnosis where data is not available
Architecture: sensors do full-mesh measurements of network
central server computes and renders results
Infrastructure: perfSONAR (ESnet & Internet2)
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DetectionFirst step: “Is there a problem?”Look for deviations from baseline
Delay: nonparametric kernel density estimates to locate baselineLoss and reordering: empirical baseline estimates
NY-CLEV ALBU-ATLbaseline
2.5s rise!
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DetectionFirst step: “Is there a problem?”Look for deviations from baseline
Delay: nonparametric kernel density estimates to locate baselineLoss and reordering: empirical baseline estimates
NY-CLEV ALBU-ATLbaseline
2.5s rise!
Estimated eventsEvents / path / day
ESnet12 days, 33 monitors 933 0.1
Internet2 22 days, 9 monitors 2268 1.4
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DiagnosisFollow-up to detection:“What is the root cause?”Diagnosis types:
congestion types
routing effects
loss nature
reordering nature
end-host effects
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Congestion Nature
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Congestion Nature“Overload”: persistent queue build-up
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Congestion Nature“Overload”: persistent queue build-up“Bursty”: intermittent queues (high jitter)
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Congestion Nature“Overload”: persistent queue build-up“Bursty”: intermittent queues (high jitter)Very small buffer
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Congestion Nature“Overload”: persistent queue build-up“Bursty”: intermittent queues (high jitter)Very small bufferExcessive buffer
Overload:ESnet
Bursty:PlanetLab
Bursty:Home link
Excessivebuffer:
Home link
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Loss NatureRandom losses: (majority) losses do not correlate with high delaysOtherwise: non-random losses
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Loss NatureRandom losses: (majority) losses do not correlate with high delaysOtherwise: non-random losses
Random losses: Home link Non-random loss: ESnet
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End-host EffectsDelays and losses induced due to:
context switchesclock synchronization (NTP)others (e.g., PlanetLab virtualization)
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End-host EffectsDelays and losses induced due to:
context switchesclock synchronization (NTP)others (e.g., PlanetLab virtualization)
Internet2:context switch
PlanetLab:end-host
noise
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The Diagnosis TreeInput:
Detected Events(delay, loss, reordering)
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The Diagnosis TreeEnd-hosteffects
NTP vs.route events
Loss events Congestion
Reorderingnature
Input:Detected Events
(delay, loss, reordering)Not shown:
Unknown type
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Pythia In-ProgressMore performance problem types...Unsupervised clustering to identify unknown eventsOpen-source system implementation:
Detection, localization, diagnosis
Interfacing with data:
ESnet, I2, PL-testbed, broadband networks
Front-end for operators
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ShaperProbe: update
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The FCC 2011 reportFCC broadband study (2011) found that: “many cable service tiers exceed 100% of the advertised upstream rate”
We revisit this statement
FCC/SamKnows measured the sustained rate using a 30s TCP stream
If shaping kicks in after 25s, the sustained speed can’t be measured
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How long should we test?Capacity(Mbps)
Shaping rate (Mbps)
Burst duration (s)
Measured/sustained(%)
Comcastupstream
3.5 1 17 100
Comcastupstream
5 2 15, 31 100, 250Comcastupstream 9 5.5 26 163Comcastupstream
14.5 10 19 100
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How long should we test?Capacity(Mbps)
Shaping rate (Mbps)
Burst duration (s)
Measured/sustained(%)
Comcastupstream
3.5 1 17 100
Comcastupstream
5 2 15, 31 100, 250Comcastupstream 9 5.5 26 163Comcastupstream
14.5 10 19 100
Cox: FCC data
Capacity(Mbps)
Shaping rate
(Mbps)
Burst duration
(s)
Capacity/sustained(%)
Coxupstream 1.5 2 50 133
Measured
Advertised29Monday, February 13, 2012
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Thank You!Diagnosing:
Home Wireless &Wide-area Networks
Partha Kanuparthy, Constantine DovrolisGeorgia Institute of Technology
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LocalizationFollow-up to detection:“Which link is bad?”Link/path performance levels discrete:e.g., high delay, medium delay, low delayLocalization: minimum number of bad links that can explain bad paths
use greedy heuristic to solve iteratively
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LocalizationFollow-up to detection:“Which link is bad?”Link/path performance levels discrete:e.g., high delay, medium delay, low delayLocalization: minimum number of bad links that can explain bad paths
use greedy heuristic to solve iteratively
path:CHIC to LOSA
path:ATLA to KANS
path:HOUS to LOSA
+
=
Internet2 event: 28th Feb 2011, 00:10:51 GMT
ge-6-2-0.0-rtr.KANS
ge-6-1-0.0-rtr.LOSA
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