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PaNDAA Big Data Approach to OSS Analytics
John Evans
Distinguished Consulting Engineer
Trevor Smith
Technical Leader
• Virtualisation / automation / orchestration has made real-time service provisioning possible
• Open source big data / analytics technologies now being widely applied outside of big web companies
• OSS architectures simply not keeping pace with the rest of industry
• No coherent industry direction on how OSS needs to change in the presence of these new technologies
Context
Simplified OSS / BSS Stack
OSS
BSS
Network and
Services
CustomerOrder
Order Mgmt
Provisioning & Activation
Service Data
Accounting and Monitoring
Billing and Reporting
Bills and Reports
Orchestration ?OSS Analytics
Relationship Between Orchestration and OSS Analytics
Orchestration OSS Analytics
Data CenterCoreAccess &
Aggregation
User
Sta
te Data
Related as loosely coupled
but tightly integrated systems
OSS analytics is responsible for
collecting data from the
infrastructure, monitoring and
analysis
The “F_APS” in FCAPS
Orchestration is responsible
for service provisioning and
pushes state to the
infrastructure
The “C” in FCAPS
• OSS analytics applications can be addressed by performing a query function against the entire OSS data set
• Fault management = (event data)
• Performance management = (metric data)
• Billing mediation = (event data, metric data)
• Capacity management = (metric data)
• Security analytics = (metric data, route data)
OSS Analytics is a big data problem!
Fix the data first
“Analytics is only as good as the
availability and quality of the data that
can be processed — so you need to
fix the data first”
In "How to Compete on Analytics",
Davenport lays out 4 quadrants of
analytics maturity…
…but how to get to quadrant 4?
Problem space is addressed
by a platform, not a product
Relationship Between Orchestration and Analytics
Orchestration OSS Analytics
Data CenterCoreAccess &
Aggregation
User
Sta
te Data
Related as loosely coupled
but tightly integrated systems
OSS analytics is responsible for
collecting data from the
infrastructure, monitoring and
analysis
The “F_APS” in FCAPS
Orchestration is responsible
for service provisioning and
pushes state to the
infrastructure
The “C” in FCAPS
We have platforms for
orchestration in Cisco NSO,
OpenDaylight, Openstack
We need a companion
platform for OSS Analytics
PaNDAPlatform for Network Data Analytics
Platform for Network Data Analytics
• Vision:
• Simple, scalable, open big data / analytics platform
• Forms a generic big data analytics platform supporting different types of analytics applications for cloud based networks and services• Operational Intelligence, e.g.
OSS
• Business intelligence, e.g. BSS
• Leverage rapid innovation in Big Data analytics space
Orchestration
Controllers
Customer
Devices
Applications
QoE Monitoring
Infr
astr
uct
ure
an
d
serv
ice-
leve
l dat
aC
ust
om
er-l
evel
dat
a
Data
Distribution
Data Store
& Processing
Open Data Platform
Producers:
Data aggregation
Event Data
Log Data
Metric Data
Network Telemetry
Data Sources
App
App
App
App
App
App
App
Consumers:
Data analysis
Applications
Inventory Topology Geography Geography
Context:
Horizontally
Scalable Data
Platform
Applications
App
App
App
Live stream
Platform for Network Data Analytics
Orchestration
Controllers
Customer
Devices
Applications
QoE Monitoring
Infr
astr
uct
ure
an
d
serv
ice-
leve
l dat
aC
ust
om
er-l
evel
dat
a
Data
Distribution
Data Store
& Processing
Master Data
Store
Open Data Platform
Batch
Processing
Stream
processing
Real Time
Data Store
Deep H
isto
rical Q
uery
Real T
ime Q
uery
Producers:
Data aggregation
Event Data
Log Data
Metric Data
Network Telemetry
Data Sources
Inventory Topology Geography Geography
Context:
• Principles
• Decouple data aggregation (publishers) from data analysis (consumers) – allow any app tto access any data source
• Simple, scalable, open data distribution platform
• Immutable dataset with minimal filtering/processing on ingress
• Analytics based approach to analysis functions
• Support for streaming apps, real-time queries and batch processing
Live stream
App
App
App
App
App
App
App
Consumers:
Data analysis
Applications
App
Platform for Network Data Analytics
Data
Distribution
Data Store
& Processing
Master Data
Store
Open Data Platform
Batch
Processing
Stream
processing
Real Time
Data Store
Deep H
isto
rical Q
uery
Real T
ime Q
uery
Producers:
Data aggregation
Event Data
Log Data
Metric Data
Network Telemetry
Data Sources
Capacity Analytics
Billing (Mediation)
Business Intelligence
Fault Analysis
PerfAnalysis
Log Analytics
Security and Threat Analysis
Inventory
Consumers:
Data analysis
Applications
Inventory Topology Geography Geography
Context:
Live stream
SNMP
Logs
SNMP
Monit,
Collectd,
Logstash,
Ceilometer
Netflow
Benefits
• An open system architecture
• Collect data once – allow any analysis application to mine any data source, leveraging the full value of the OSS dataset
• Extensible – add new OSS analysis functions quickly and seamlessly with minimum of development cost
• Leverage rapid innovation in Big Data analytics space
Spark
Streaming
Logstash
Logstash
pmacct
Kafka
Spark
Hbase
Impala
ELK
OpenSoc
Demo
See us in the DevNet zone
Online at panda.cisco.com
Hands-on with our Learning Labs at developer.cisco.com/site/panda/
Want to know more?
Thank you
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