10-year history of internet delay
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10-Year History of Internet Delay. DK Lee , Kenjiro Cho*, Gianluca Iannaccone **, Sue Moon CAIDA-WIDE-CASFI Joint Workshop April 24, 2010 Division of Computer Science, KAIST *IIJ Research Laboratory **Intel Research, Berkeley. For the Last Few Decades. - PowerPoint PPT PresentationTRANSCRIPT
10-Year History of Internet Delay
1April 24, 2010, [email protected]
DK Lee, Kenjiro Cho*, Gianluca Iannaccone**, Sue Moon
CAIDA-WIDE-CASFI Joint WorkshopApril 24, 2010
Division of Computer Science, KAIST*IIJ Research Laboratory
**Intel Research, Berkeley
For the Last Few Decades
• Many large-scale Internet measurements: – NLANR AMP, CAIDA’s Ark, DIMES, iPlane– UCSD network teloscope, RouteViews, RIPE RIS
• About the Internet evolution, we know that – Internet topology has been shrunken in terms of the av-
erage AS hop count (by network densification)– Dominant Internet traffic types have changed from web
to peer-to-peer traffic
April 24, 2010, [email protected] 2
What We Know About Internet Delay
• Transmission delay– Improved with faster link speed
• Propagation delay– Improved with new undersea cables
• Queuing and processing delay– Improved with faster devices
• Routing Issues – Loops or detours from VPNs, overlays– Delays can be Improved with new AS peering practices
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Talk Outline
• Has the Internet delay gotten better or worse? • Two main methodologies: – Path stitching– Random sampling of the Internet host pairs
• Data sets• Preliminary results– Delay distributions from 2004 to 2009
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Reasons for No Authoritative Statement About the Internet-
wide Delay• “Random sampling” has not been feasible– No measurement system with access to every AS and
subnet of the Internet– No rigorous method to address bias in Internet sampling
• Only a selective set of statistics has been possible– Stability, variation, and abrupt changes of delay as a path
statistic have been well studied
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Path Stitching for Random Sampling
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• Internet-wide path and round-trip delay estimation between any pair of Internet hostsby recycling existing data
– Keep database of end-to-end measurement data seg-mented by the AS
– Identifies relevant segments efficiently– Produces path and round-trip delay estimates, by stitch-
ing segments together
Path Segment Repository• Indexing the path segments by the AS number
:A: Intra-domain segments of A ::B: Intra-domain segments of B :A::B Inter-domain segments between A and B :
:A: + A::B + :B: = Router-level paths from A to B :
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A B C
traceroute outputs:
AS path:
a1 a2 a3 a4 b1 b2 b3 c1 c2 c3
a1 a2 a3 a4 b1 b2 b3
Overview of Path Stitching• Question:
• Answers:
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a ? c
A C
A CB
Step 1. IP-to-AS mapping
Step 2. AS path inference
:A: rttA
:C: rttC
:B:rttB
A::B rttAB
Step 3. Path stitching
:A::B::C:B::C rttBC
Router-level paths and RTT from a to c ?
Path =
RTT = rttA + rttAB + rttB + rttBC + rttC
Results of Path Stitching• We evaluate the Internet-wide coverage and accu-
racy of the estimated results– More than 70% of pairs are covered by the algorithm– 80% of pairs have absolute errors less than 20msec– Median absolute error is less than 5msec
• Reference: DK Lee, Keon Jang, Changhyun Lee, Gianluca Iannaccone, Sue Moon, “Internet-wide Path and Delay Estimation from Existing Measurements”, IEEE INFOCOM 2010 Mini-conference
April 24, 2010, [email protected] 12
Survey Design: Select a Set of Host Pairs to
Examine• Random sampling design with size n– Internet consists of N unique pairs of /24 IP prefixes– Extract all routable /24 prefixes from BGP table– Randomly select n=10,000 pairs
• Fraction of responded pairs with path stitching– 67% in 2004 and 65% in 2009
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Sampling Errors for the Population Median – (1)
• Confidence Interval (CI) for the population median estimator:
= q0.5 ±
• In 2009/06, n = 10,000= 211.6 ± 4.9 msec
• In 2009/06, n = 100,000= 213.0 ± 1.3 msec
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n1
)(qf0.5)-0.5(1z
0.52
Sampling Errors for the Population Median – (2)
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Sample size n=100,000 is very accurate
Sampling Errors for the Population Median – (3)
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Results for the sample size n>=10,000 are almost identical
Data Sets:E2E Measurements + Routing
Data• End-to-end path and delay measurement– Traceroute measurements• CAIDA Ark project (from 1998~)• NLANR’s AMP project (from 1999~)
• Routing information– BGP routing tables• University of Oregon, RouteViews (from 1997~)• RIPE RIS (from 1999~)
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Data Processing Oveview• For each YYYY/MM, we process:
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Traceroute outputs
(Skitter .arts.gz)(Ark .warts.gz)
BGP Routing Table Snapshots
(RIPE RIS MRTv1, v2)(RouteViews `sh ip bgp`, MRTv1, v2)
Path Stitching Sampled pairs of
/24 prefixes
Queries
Path and delay estimations for queries
Median Delays Increase Con-stantly
• Delay distribution has gotten worse from 2004 to 2009, both at first/last mile and in the core
• IP/AS hop counts decreased end-to-end– IP hop counts: 14.8 (2004) 14.1 (2009) – AS hop counts: 3.77 (2004) 3.65 (2009)
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Delay distributions from 2004 to 2009
(For the Same Pairs)• Only 2432 pairs are constantly responded from 2004 to 2009
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2004 vs. 2009 (For the Same Pairs)
• Median delay: 163.5 msec 156.931 msec
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Median Delays improved(For the Same Pairs)
• Delay distributions for the same set of sample host pairs remain almost identical or slightly improved from 2004 to 2009
• IP/AS hop counts decreased
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Finding the corroborating Evi-dence for the observations
• IP address usage have expanded from 2004 to 2009– /24 prefixes of those hosts in 2009 existed in 2004?– ASes of those hosts in 2009 existed in 2004? • In sampled pairs in 2009, compared to 2004,
1729 ASes are disappeared, 2091 Ases are newly appeared.
• Network densification helps AS hop count to de-crease. Does it also help IP hop count or delay to decrease?
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Other Challenges
• Analyzing the delay distribution in 1999– Skitter’s old-format does not have hop-by-hop delays– NLANR AMP dataset is too small– RouteViews have very restricted number of peers
• Effect of non-response– Where does the missing 35% come from?
• Effect of measurement errorsApril 24, 2010, [email protected] 30
Conclusion
• We present the methodology for the Internet delay history reconstruction and analysis: – Path-stitching with existing measurements– Random sampling of the Internet host pairs
• Our approach is very feasible in showing insight about the overall Internet delay distribution
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Thank You!• Any Questions?
• We are looking for other traceroute outputs and BGP table snapshots archived before 2000
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What If There Are• Too few segments:
• Too many segments:
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A::B ? B::C
:A: :C::B: ?
... ...
We Employ Approximations(i) Missing AS
» No solutions (other than collecting more measurements. )
(ii) Missing inter-domain segment » Search for reverse path segments.
(i.e., if we cannot find A::B, use B::A instead)
(iii) Path segments do not rendezvous at the same address(i.e., the segment cannot be stitched)
» Identify nearby segments (on the same router, PoP, Prefix)
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:A: :B:
B::A
X
Z
Y
W
A
X::A::W = ?
We Apply Preference Rules• Rank the list of candidate path segments– Eliminate candidates as many as possible while keeping
the most accurate one. – Reflect the actual routing mechanism
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Source AS Destination ASIntermediate ASes ...
...
Rule #1, 2, 3 Rule #1, 2, 3Rule # 2, 3
Rule #1: Proximity• Preference to the path segments that closest to the queried
source and destination address
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Source AS
a.b.c.1 a.b.c.2
a.b.1.1 d.b.1.2
x.y.z.1 x.y.z.2
Query: a.b.c.d --> x.y.z.w
Rule #2: Destianation-bound
• Preference to the segments from traceroutes with the same destination prefix
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Query: a.b.c.d --> x.y.z.w
Source AS
Original traceroutes
traceroutes to x.y.z.1
traceroutes to u.v.w.1
Rule #3: Most Recent• Preference to the most recent path segment
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Query: a.b.c.d --> x.y.z.w
Source AS
Original traceroutestraceroutes to x.y.z.1
YYYYMMDD-12:30:00
YYYYMMDD-10:30:00
Comparisons with iPlane – (1)
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• CDF of absolute errors for pl-easy pairs
Errors <= 20ms for 90% of pl-easy pairs
Comparisons with iPlane – (1)• CDF of absolute errors for pl-hard pairs
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Very promising results:With accurate AS paths inference,
errors <= 20ms for 80% of pl-hard pairs