© 2014 Guavus, Inc. All rights reserved.
Nicolas Hohn Director of Analytics Guavus
LARGE SCALE PREDICTIVE ANALYTICS FOR ANOMALY DETECTION
2015
© 2015 Guavus, Inc. All rights reserved. 2
Guavus Applications Focus
Planning Engineering
• Network Analytics • Capacity Management • Trending & Forecasting • Value-based
Network Planning
• Quality of Experience • Complaints Mitigation • Proactive Care • Revenue Assurance • Self Care Usage Portal
Network Operations Marketing Care
• Service Management • QoS Management • Performance Monitoring • Proactive Service
Assurance
• Subscriber Profiling • Personalization &
Targeting • CSP Data
Monetization
Service Assurance Customer Experience
© 2015 Guavus, Inc. All rights reserved. 3
Anomaly Detection • Anomaly: something that is unusual or unexpected
• Detection: extraction of particular information from a larger stream of information
without specific cooperation from or synchronization with the sender
Implementation • Rule based: manual thresholds • Automated: thresholds set by machine learning
Operational Intelligence
Service Degrading Problem!
Service Anomalies
Identification!
Root-Cause Analysis!
Problem Resolution!
Quantify how ‘unusual’ a signal value is
Unsupervised learning to send trigger when signal is ‘unexpected enough’
DOES NOT SCALE
spike step slope
time time time
KP
I
© 2015 Guavus, Inc. All rights reserved. 4
Anomaly detection
Event Arrival Times ���2014-09-16 00:00:06
2014-09-16 00:00:09
2014-09-16 00:00:40
2014-09-16 00:00:42
2014-09-16 00:00:45
2014-09-16 00:01:00
2014-09-16 00:01:09
2014-09-16 00:01:11
2014-09-16 00:01:20
2014-09-16 00:02:09
……
5
4
Define KPI and time scale Predict conditional baseline (black line) and probability density given historical data KPI value (green line)
Trigger alert (red dot) if data point significantly above baseline, i.e. outside confidence interval (gray bands)
1 2 3
4
• 4 step process
# ev
ents
Time
© 2015 Guavus, Inc. All rights reserved. 5
KP
I
time
EASIER
Challenges • Predict distribution of current value based on past values
– Uni/Multi variate time series analysis
• Unify uncertainty metric across all types of input signals to build a global ranking of alarms • Scale on limited hardware footprint. Real time monitoring of potentially millions of time series • Keep customer happy (no alarm flooding, limit false positives, rank alarms by severity)
KP
I
time
HARDER
HARD HARD HARD
© 2015 Guavus, Inc. All rights reserved. 6
Solutions
time
Ano
mal
y in
dica
tor
#eve
nts
• Data Science: – Robust to past anomalies
• No guarantee that ‘training’ data is anomaly free – Adapt to changes
• Retrain model – Cannot rely on labeled data:
• Understand customer ‘utility function’, business impact of anomalies • Set thresholds automatically • Quantify cost of false positives and false negatives
• Engineering: – Intelligent caching – Compression – Scalable system
© 2015 Guavus, Inc. All rights reserved. 7
• Monitor KPIs, such as dropped call rate on each base station in a 4G network • Detect anomalies • Infer root cause by analyzing 1000s of other KPIs available on each cell of the network
Use case: Networks analytics
© 2015 Guavus, Inc. All rights reserved. 8
Architecture of the solution
Data fusion & aggrega/on
Compute Cluster Analytics Cluster
Intelligent Cache Collector
Adapter 1
Custom
Adapter 2
Columnar Storage
Anomaly Detec/on
User Interface
Time series analy/cs
Rules/ alerts frame-‐work
M2M
Interface w
ith customer
system
Data streams
© 2015 Guavus, Inc. All rights reserved. 9
Conclusion Lessons learned
• No silver bullet, but multiple methods each with their own pros/cons • Simple and scalable solution • Adapt to:
– data changes – customer needs
• API design: – black-box approach: hide complexity from developers
© 2015 Guavus, Inc. All rights reserved.
Q&A
Nicolas Hohn, Director of Analytics [email protected]
2015
Top Related