Automated Cellular Root Cause Analysis
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Transcript of Automated Cellular Root Cause Analysis
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Automated Cellular Root Cause Analysis
Sayandeep Sen Bell Labs India
Joint work with Sourjya Bhaumik & Rijin John
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Cellular Base Station Monitoring
Monitoring Centre
Cell site
Cell sites
Every 15 minutes
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Performance countersExample: connected users, average signal strength, cell radius etc.
Cell site
Cell sites
Performance counters
Cellular Base Station Monitoring
Monitoring Centre
Every 15 minutes
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Cellular Base Station Monitoring
KPI: Key Performance IndicatorExample: Call drop rate, Successful connection setup rate, Throughput
Cell site
Cell sites
KPI
Every 15 minutes
Monitoring Centre
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Root cause analysis
Monitoring Centre
Cell site
Cell sites
KPIKPI
Perf
orm
ance
coun
ters
Why KPI went below threshold ?
Manually
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Root Cause Analysis – Issues
Time
Time
Time
KPI
Para
met
er 1
Para
met
er N
Too many variables• ~300 parameters• 1 engineer per O(100) cell
sites
Manual debugging is inefficient
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Time
Time
Time
KPI
Para
met
er 1
Para
met
er N
??? Sporadic parameter dips
Root Cause Analysis – Issues
Manual debugging is inefficient
Too many variables• ~300 parameters• 1 engineer per O(100) cell
sites
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Time
Time
Time
KPI
Para
met
er 1
Para
met
er N
Multiple parameter interaction
Root Cause Analysis – Issues
Sporadic parameter dips
Manual debugging is inefficient
Too many variables• ~300 parameters• 1 engineer per O(100) cell
sites
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Carry out automated (fast) root cause analysis which accounts for sporadic dips and multiple parameter interactions while ensuring human readable output.
Problem Statement
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• Motivation
• Problem statement
• Approach
• Insight, Mechanism, Customizations
• Results
• Ongoing work
• Other work
Outline
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KPI-parameter relationship is dependent on other parameter values
Key Intuition
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Conn. Req.
Call SuccessHan
doff rate
Key Intuition
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Conn. Req.
Threshold
Handoff ra
te
Call Success
y
Conn. Req. > X & H/o =y
X
Key Intuition
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Conn. Req.
Handoff ra
te
Call Success
Conn. Req. > X’ & H/o =y’
y’
Key Intuition
KPI-parameter relationship is dependent on other parameter values
X’Determine the rules for various parameter combination values using Regression trees
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• Motivation
• Problem statement
• Approach
• Insight, Mechanism, Customizations
• Results
• Ongoing work
• Other work
Outline
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Form clusters of points
To minimize the sum of distance metric for sub-clusters
Δ
Δ’Δ”
Regression treesCall Success
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Distance metric: sum of Euclidean distance of points in a sub-cluster
Δ
Δ’Δ”
Regression treesCall Success
Form clusters of points
To minimize the sum of distance metric for sub-clusters
Provide human readable rule for each cluster
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Conn. Req.
2) Calculate Δ
Regression trees
1) Pick an axis
Call Success
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1) Pick an axis 2) Calculate Δ
Conn. Req.
X
Regression treesCall Success
3)Pick pivot to divide points in two clusters,
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Conn. Req.4) Calculate Δ’+Δ”
3)Pick pivot to divide points in two clusters,
1) Pick an axis 2) Calculate ΔX
Regression treesCall Success
Δ”
Δ’
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Conn. Req.4) Calculate Δ’+Δ”
3)Pick pivot to divide points in two clusters,
1) Pick an axis 2) Calculate ΔX X X X Repeat for
all pivots
Regression treesCall Success
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Conn. Req.4) Calculate Δ’+Δ”
3)Pick pivot to divide points in two clusters,
1) Pick an axis 2) Calculate Δ
Repeat for
all pivots
Regression treesRepeat for all axis
Call Success
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Conn. Req.
4) Calculate Δ’+Δ”
3)Pick pivot to divide points in two clusters,
1) Pick an axis 2) Calculate Δ
Repeat for
all pivots
5) Pick pivot with minimum Δ’+Δ”
X
Conn.Req<X Conn.Req>=X
Regression treesRepeat for all axis
Call Success
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Conn. Req.
4) Calculate Δ’+Δ”
3)Pick pivot to divide points in two clusters,
1) Pick an axis 2) Calculate Δ
Repeat for all axis
Repeat for
all pivots
5) Pick pivot with minimum Δ’+Δ”
X
Repeat for sub-clusters
Conn.Req<X Conn.Req>=X
Regression treesCall Success
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Conn. Req.
XHan
doff ra
teY
Conn.Req<X Conn.Req>=X
Handoff Rate >= Y
Handoff Rate < Y
Regression treesCall Success
4) Calculate Δ’+Δ”
3)Pick pivot to divide points in two clusters,
1) Pick an axis 2) Calculate Δ
Repeat for all axis
Repeat for
all pivots
5) Pick pivot with minimum Δ’+Δ”
Repeat for sub-clusters
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4) Calculate Δ’+Δ”
3)Pick pivot to divide points in two clusters,
1) Pick an axis 2) Calculate Δ
Repeat for all axis
Repeat for
all pivots
5) Pick pivot with minimum Δ’+Δ”
Repeat for sub-clusters
Conn. Req.
XHan
doff ra
teY
Conn.Req<X Conn.Req>=X
Handoff Rate >= Y
Handoff Rate < Y
Select rules corresponding to low KPI values
Regression treesCall Success
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Conn. Req.
XHan
doff ra
teY
Conn.Req<X Conn.Req>=X
Handoff Rate >= Y
Handoff Rate < Y
Regression treesCall Success
Human readable
Capture multiple variable interaction
Capture sporadic events due to time agnostic clustering
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• Motivation
• Problem statement
• Approach
• Insight, Mechanism, Customizations
• Results
• Ongoing work
• Other work
Outline
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• Distance metric oblivious of significance of KPI values• Curse of dimensionality
Regression trees – Issues
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Conn. Req.
Handoff rate
Metric oblivious KPI value significance
Call Success
Need big separation between good and bad values
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Conn. Req.
Handoff rate
Call Success
98.5%
Bad
Call Success
Metric oblivious KPI value significance
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Conn. Req.
Handoff rate
98.5%
98.6%
98.7 %
98.5%
Bad
Call Success
Metric oblivious KPI value significance
Call Success
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Conn. Req.
Handoff rate
98.5%
98.6%
98.7 %
98.5%
Bad
Call Success
Metric oblivious KPI value significance
Distinction between good and bad is small
Stratify KPI values
Call Success
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Conn. Req.
Handoff rate
98.5%
98.6%
98.7 %
98.5%
Bad
Call Success
Metric oblivious KPI value significance
Distinction between good and bad is small
Call Success
Multiply KPI value with custom step function
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Conn. Req.
Handoff rate
98.5%
98.6%
98.7 %
98.5%
Bad
Stratification of dataCall Success
Multiply KPI value with custom step function
Call Success
Distinction between good and bad is small
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Conn. Req.
Handoff rate
98.5%
98.6%
98.7 %
Bad
Stratification of dataCall Success
Call Success
Distinction between good and bad is small
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Conn. Req.
Handoff rate
Stratification of data
98.5%
98.6%
98.7 %
98.5%
Bad
Call Success
Call Success
Distinction between good and bad is small
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• Distance metric oblivious of significance of KPI values• Stratify KPI values
• Curse of dimensionality reduction
Regression trees – Issues
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Interference
Traffic Load
Curse of DimensionalityCall Success
Traffic Load > X & Interference > Y
Handoff rate < X & Conn. Req. < Y
Cell Radius > X & Allotted Power < Y
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Interference
Traffic Load
Traffic Load > X & Interference > Y
Handoff rate < X & Conn. Req. < Y
Cell Radius > X & Allotted Power < Y
Call SuccessCurse of Dimensionality
~300 variables lead to 2^300 combinationsregression tree can be misled
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• Preprocessing – Remove correlated, barely changing parameters etc.
• Domain knowledge based filtering– Remove unrelated parameters, apply weights
● Heuristics– Spike, Correlation, 3 more …
Dimensionality reduction
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Spike heuristic
Time
Time
Call SuccessValues spike around same time
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Correlation heuristic
Conn. Req. Conn. Req.
Call
Succ
ess
Call
Succ
ess
Call Success > 98.5 % Call Success <= 98.5 %
Correlation changes significantly
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Regression tree
Apply filters
Stratify KPI data
Select rules
Rule generation
Data store
Rule store
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Rule application
Rule storeMatching rules
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• Motivation
• Problem statement
• Approach
• Insight, Mechanism, Customizations
• Results
• Ongoing work
• Other work
Outline
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Training & Verification Data
• Analyzed 28 days of data from 217 cell sites • 2 countries, 2 OEMs
• 317 parameters @ 15 minute interval • 80% data to train and 20% to validate
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Find rules for all KPI dips
Country #1 (18 cell sites)
Country #2(60 cell sites)
Cell sites with at least 4 KPIs with more than 100 bad instances selected
1 2 3 40
100
200
300
400
500
600
700
800
Found Rule Bad KPI
1 2 3 40
50
100
150
200
250
300
350
400
450 Found Rule Bad KPI
KPI KPIIn
stan
ces
Inst
ance
s
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Rule Verification
• Picked rules for randomly selected 50 KPI dips• Show rules to 15 RF engineers (Ongoing)
• 80% rules were actionable• For all the KPI dips at least one actionable
rule in the rule set
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1) Total users in 5 to 10 KM from base station > 63%
2) Total users in bad RSS region > 21% AND Total uplink load > 831 MB
KPI dip: Call success rate < 98.5%
3) Download Traffic < 500 Kbytes AND Total active users < 200
Example rule set
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2) Total users in bad RSS region > 21% AND Total uplink load > 831 MB
3) Download Traffic < 500 Kbytes AND Total active users < 200
1) Total users in 5 to 10 KM from base station > 63%
Users concentrated at cell edge
Example rule set
KPI dip: Call success rate < 98.5%
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3) Download Traffic < 500 Kbytes AND Total active users < 200
2) Total users in bad RSS region > 21% AND Total uplink load > 831 MB
1) Total users in 5 to 10 KM from base station > 63%
21% users with bad RSSI and high traffic load
Example rule set
KPI dip: Call success rate < 98.5%
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1) Total users in 5 to 10 KM from base station > 63%
2) Total users in bad RSS region > 21% AND Total uplink load > 831 MB
3) Download Traffic < 500 Kbytes AND Total active users < 200
Do not point to meaningful cause ?
Example rule set
KPI dip: Call success rate < 98.5%
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Example rule set
1) Total users in 5 to 10 KM from base station > 63%
2) Total users in bad RSS region > 21% AND Total uplink load > 831 MB
3) Download Traffic < 500 Kbytes AND Total active users < 200
Coarse timescale leading to multiple other failures
Don’t have access to relevant parameters
Specific problem rare event in current sector
KPI dip: Call success rate < 98.5%
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• Motivation
• Problem statement
• Approach
• Insight, Mechanism, Customizations
• Results
• Ongoing work
• Other work
Outline
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Recommending solution for a problem
Cell site
Cell sitesMonitoring Centre
Parameter list
Parameter list: Remotely configurable parameters,Example: Antenna tilt, Min. signal strength to associate, allowable idle time etc.
Ongoing Work
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Recommending solution for a problem
Cell site
Cell sitesMonitoring Centre
Parameter list
When a KPI dips:• Generate rules• Find sectors where the rules do not lead to
KPI dip• Return the parameter list for those sectors
Ongoing Work
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Ongoing WorkRecommending solution for problem
More customizations necessary …
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• Motivation
• Problem statement
• Approach
• Insight, Mechanism, Customizations
• Results
• Ongoing work
• Other work
Outline
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All bits of a video application are not created equal
< 5 msec
< 105 msec
Nearer the deadline more valuable the packet
Value
I P B
MPEG4/ H.264 encoded video
Value aware networking
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ApplicationTransportNetwork
MACPHY
000101 011101 010101I P B
0001011010101100101010101100001001
Value aware application layer
I P B
API
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ApplicationTransportNetwork
MACPHY
000101 011101 010101I P B
0001011010101100101010101100001001
Value aware networking
• Order of sending data• Times to retransmit• MAC data rate
Can protocol decisions be taken in a value aware manner ?
I P B
Yes Almost no data overhead
API
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Questions?
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Backup
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Future work
• Online regression tree formation• Fast emulation systems for what-if analysis
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Research overview
Scout
[ Submitted]
[DySPAN 2012]
Range-Write[OSDI 2008]
Apex[Sigcomm 2010]
Medusa[NSDI 2010]
MOM[Submitted]
RDP-TS
DGP[MobiCom 2006]
MCB-Mesh[IMC 2008]
Fractel[INFOCOM 2008]
WiScape[IMC 2011]
[WWW 2008]
Topo-cons
WhiteCell
PhD Dissertation
Systems & ProtocolsCross-Layer design Measurement
& Analysis
Root-causeMultIfaceT [ HotMobile’10]
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Rx
Higher bandwidth• Home repeater• Vehicular
whitespace
Reliability• Whitespace femto
Benefits
Rx
Tx
Multi-Interface systems
Tx
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API with higher layersStriping decisionChannel selectionFeedback gathering
Multi-Interface systems
Challenges
Rx
Tx
Tx Rx