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Finding a Needle in a Haystack: Pinpointing Significant BGP Routing
Changes in an IP Network
Jian Wu (University of Michigan) Z. Morley Mao (University of Michigan) Jennifer Rexford (Princeton University)
Jia Wang (AT&T Labs Research)
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Motivation
CBR
CBRCBR
CBRAS1
AS2 AS3
destination
A BC
D
Failure
Disruption
Congestion
Mitigation
AS4
source
A backbone network is vulnerable to routing changes that occur in other domains.
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Goal Identify important routing anomalies
Lost reachability Persistent flapping Large traffic shifts
Contributions:•Build a tool to identify a small number of important routing disruptions from a large volume of raw BGP updates in real time.•Use the tool to characterize routing disruptions in an operational network
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Interdomain Routing:Border Gateway Protocol
Prefix-based: one route per prefix Path-vector: list of ASes in the path Incremental: every update indicates a change Policy-based: local ranking of routes
CBRCBR CBRCBRCBRCBRCBRCBR
“I can reach 12.34.158.0/24”
“I can reach 12.34.158.0/24
via AS 1”
AS 1 AS 2
12.34.158.5
data traffic data trafficAS 3iBGPeBGP eBGP
12.34.158.0/24
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Capturing Routing Changes
CBRCBR
CPEBGP Monit
or
CBRCBR
CBRCBR
CBRCBR
CBRCBR
CBRCBR
iBGP
iBG
P
iBGP
eBGP
eBGP
eBGPUpdatesUpdates
Best routes Best routes
A large operational network(8/16/2004 – 10/10-2004)
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Challenges Large volume of BGP updates
Millions daily, very bursty Too much for an operator to manage
Different from root-cause analysis Identify changes and their effects Focus on actionable events rather than
diagnosis Diagnose causes in/near the AS
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System Architecture
Event Classification
“Typed”Events
EEBR
EEBR
EEBR
BGP Updates
(106)
BGP Update Grouping
Events
Persistent Flapping Prefixes(101)
(105)
EventCorrelation
Clusters
Frequent Flapping Prefixes
(103)
(101)
Traffic ImpactPrediction
EEBREEBR EEBR
LargeDisruptions
Netflow Data
(101)
From millions of updates to a few dozen reports
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Grouping BGP Update into EventsChallenge: A single routing change
leads to multiple update messages affects routing decisions at multiple routers
Approach:
•Group together all updates for a prefix with inter-arrival < 70 seconds•Flag prefixes with changes lasting > 10 minutes.
BGP Update Grouping
EEBR
EEBR
EEBR
BGP Updates
Events
Persistent Flapping Prefixes
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Grouping Thresholds Based on our understanding of BGP
and data analysis Event timeout: 70 seconds
2 * MRAI timer + 10 seconds 98% inter-arrival time < 70 seconds
Convergence timeout: 10 minutes BGP usually converges within a few
minutes 99.9% events < 10 minutes
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Persistent Flapping Prefixes
Types of persistent flapping Conservative damping parameters (78.6%) Protocol oscillations due to MED (18.3%) Unstable interfaces or BGP sessions (3.0%)
A surprising finding: 15.2% of updates were caused by persistent-flapping prefixes even though flap damping is enabled.
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Example: Unstable eBGP Session
ISP Peer
CustomerEC
EB
EA ED
p
Flap damping parameters is session-based Damping not implemented for iBGP sessions
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Event ClassificationChallenge: Major concerns in network management
Changes in reachability Heavy load of routing messages on the routers Change of flow of the traffic through the network
Event ClassificationEvents “Typed” Events,
e.g., Loss/Gain of Reachability
Solution: classify events by severity of their impact
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Event Category – “No Disruption”
ISP
EA
p
EB
EC
EE
AS2
ED
AS1
No Traffic Shift
“No Disruption”: no border routers have any traffic shift. (50.3%)
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Event Category – “Internal Disruption”
ISP
EA
p
EB
EC
EE
AS2
ED
AS1
Internal Traffic Shift
“Internal Disruption”: all traffic shifts are internal. (15.6%)
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Event Category – “Single External Disruption”
ISP
EA
p
EB
EC
EE
AS2
ED
AS1
external Traffic Shift
“Single External Disruption”: only one of the traffic shifts is external (20.7%)
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Statistics on Event ClassificationEvents Updates
No Disruption 50.3% 48.6%Internal Disruption 15.6% 3.4%Single External Disruption 20.7% 7.9%Multiple External Disruption 7.4% 18.2%Loss/Gain of Reachability 6.0% 21.9%
First 3 categories have significant day-to-day variations
Updates per event depends on the type of events and the number of affected routers
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Event Correlation
Challenge: A single routing change affects multiple destination prefixes
EventCorrelation“Typed”
EventsClusters
Solution: group the same-type, close-occurring events
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EBGP Session Reset Caused most of “single external disruption”
events Check if the number of prefixes using that
session as the best route changes dramatically
Validation with Syslog router report (95%)time
Number of prefixes
session failure
session recovery
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Hot-Potato Changes Hot-Potato Changes
Caused “internal disruption” events Validation with OSPF measurement (95%)
[Teixeira et al – SIGMETRICS’ 04]
ISP
P
EA EB
EC
10119
“Hot-potato routing” = route to closest egress point
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Traffic Impact PredictionChallenge: Routing changes have different
impacts on the network which depends on the popularity of the destinations
Traffic ImpactPrediction
EEBR
Clusters LargeDisruptions
Netflow Data
EEBR EEBR
Solution: weigh each cluster by traffic volume
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Traffic Impact Prediction Traffic weight
Per-prefix measurement from netflow 10% prefixes accounts for 90% of traffic
Traffic weight of a cluster the sum of “traffic weight” of the prefixes A small number of large clusters have
large traffic weight Mostly session resets and hot-potato
changes
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Performance Evaluation Memory
Static memory: “current routes”, 600 MB Dynamic memory: “clusters”, 300 MB
Speed 99% of intervals of 1 second of updates
can be process within 1 second Occasional execution lag Every interval of 70 seconds of updates
can be processed within 70 secondsMeasurements were based on 900MHz CPU
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Conclusion BGP troubleshooting system
Fast, online fashion Operators’ concerns (reachability, flapping, traffic) Significant information reduction
millions of update a few dozens of large disruptions
Uncovered important network behavior Hot-Potato changes Session resets Persistent-flapping prefixes