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Kafka at the core of an AIOps pipeline
Presented by:
Sunanda Kommula (Distinguished Engineer, Selector.ai)
Alankar Sharma (Sr Principal Architect, Comcast)
Hybrid Cloud AIOps

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WWW.SELECTOR.AI
Agenda
• Comcast hybrid cloud
• Key role of Kafka
• Selector AI
• Challenges and Solutions
• System architecture
• Observability of Data Ingestion
• CI/CD with Kafka
• AIOps

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Use Case
• Hybrid cloud: infrastructure for
Internet, voice, video, storage
• Goal - Application connectivity
• Wide variety of applications
• Large cloud environment,
operationally complex
• Application perspective vs
cloud status
• End to end connectivity
Hybrid Cloud
Compute
AI/ML
Load Balancer

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The Hybrid Cloud
• Cloud environment is complex,
diverse and evolving
• Application connectivity is key
• Detect grey failures
• Isolate wide-spread issues
• Why AIOps?
• Examine millions of data-points
• Connect the dots
• Faster root-cause, resolution and
remediation
Internet
AWS
Region 1
AWS
Region 2
Data
Center 1
Data
Center 2
Data
Center 3
Backbone
Future
Azure

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Kafka empowers AIOps
• Critical hub of large networks
• Connects varied data sources
• Reliably delivers high velocity, volume
& variety of data
• Enables communication
between microservices
• Bridges organizations
Broker
Broker
Broker
AWS Cloud
Watch
Logs
Kafka
Clusters
Synthetics
Producer Producer Producer Producer
Broker
Broker
Broker
Network
Monitoring Telemetry
Engine Engine
Broker
Broker
Broker
ChatOps Portal
ML Storage
Kafka Cluster
Device
Metrics
Query Visualize

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Selector AI: Turn-key AIOps for Instant Actionable Insights
GET RESULTS WITHIN HOURS OF DATA ONBOARDING
Declarative ETL
Hybrid, Edge & Public Cloud Data
Zero Config Analytics
Automate Closed Loop Anomaly
Remediation
Zero touch user onboarding with NLP
Slack as a Collaborative Notebook
NETWORK
APM
SYNTHETICS DATA HYPERVISOR
UNIQUE I-RANK ENGINE
6

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Challenges – The 6 V's of Big Data
• Single topic
• ~350Mbps
• Noisy data
• Subscribe
• Filter
Volume
Variety
• Metrics
• Logs
• SNMP
• Avro
• JSON
Velocity
• ~230Kpps
• Deserialize
• Time-sensitive
• Ordered
• Batch
Veracity
• Trust
• Access model
• Changing data
• Changing model
Variability
• Statistical
• Events
• Correlate
Value

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Solutions – Velocity & Volume
• Message filter at ingest (I/O filter) - Head of line drop
Broker message rate (~230Kpps) Post I/O filter (5Kpps)
Byte pattern I/O filter 97% noise reduction

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Solutions – Velocity & Volume
Most busy consumers (1.8Kpps each) Internal gochannel sizes per decoder
• Leveraged Golang – performance, concurrency, channels
• Live dashboards for cluster KPI monitoring and
performance tuning

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Solutions – Velocity & Volume
• Scale-out models
Engine
Pod 1
Engine
Pod 2
Engine
Pod 3
Kafka
Cluster
Broker
Broker
Broker
Telemetry
Metrics
Engine Configuration
Sharded Data Ingestion
Shared consumer group - Data sharding
Engine
Pod 1
Engine
Pod 2
Engine
Pod 3
Kafka
Cluster
Broker
Broker
Broker
Telemetry
Metrics
Engine Configuration
Independent consumers – I/O filters
Full Data Ingestion

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Solutions – Variety & Variability
Dynamic schema
Blocked
patterns
Allowed
patterns
Schema
driven
filters
subscriptions:
- label: sevone-spdb # kafka topic
pathgroup:
- group: port # port group
paths:
- 'indicatorName=ifHCInOctets'
- 'indicatorName=ifHCInUcastPkts'
- 'indicatorName=ifHCInMulticastPkts'
- group: disk # disk group
paths:
- 'indicatorName=s1_sizebytes'
- 'indicatorName=s1_usedbytes'
- group: system # system group
paths:
- 'indicatorName=sysUpTime'
- 'indicatorName=hrProcessorLoad'
- group: bgp # bgp group
paths:
- 'indicatorName=bgpInTotalMessages'
- 'indicatorName=bgpOutTotalMessages'
- 'indicatorName=bgpPeerInUpdates'
- 'indicatorName=bgpPeerOutUpdates'
- 'indicatorName=bgpPeerState'
Dynamic subscriptions

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Solutions – Value & Veracity
• Declarative ETL
• Extract, enrich, normalize
• GraphQL based record selection
• Deployment specific, zero code changes
• Model events – syslogs and SNMP from devices
• Correlate metrics, events and synthetics
• Access considerations
• Distributed control plane
• Disaggregated data pipeline
• SaaS, DMZ, on-prem PODs

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WWW.SELECTOR.AI • CONFIDENTIAL AND PROPRIETARY
System Architecture
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Disaggregated data pipeline
DMZ
Engine
Engine
Engine
Broker
Broker
Broker
Producer
Producer
Producer
Customer
AWS
Cloud
Watch
Network
Monitoring
Elastic
Search
Selector SaaS
Kafka
Cluster
Broker
Broker
Broker
Kafka
Cluster
Ingest
Engine
ML
Storage
ChatOps
Query
Visualize
Portal

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WWW.SELECTOR.AI • CONFIDENTIAL AND PROPRIETARY
Engine Micro Architecture
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Ingest I/O Filter Decode
Match
Label
Format
Export
Extract
Selector
Pipeline

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Observability
• Highly visible Kafka
ingest pipeline
• Aggregated &
granular KPIs
• broker-clusters, topics,
consumers, partitions,
decoders, match rates
• Metric ingest KPIs
• cumulative, port, BGP,
system, memory
• Reflects subscription
configuration models
Match rates per partition Metric ingest rate (~0.5Kpps)
Port metrics ingest rate BGP metrics ingest rate

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CI/CD Pipeline
• Why – Replicate every deployment in a test environment
• What – On demand launch of Kafka cluster, fake data, fake signals
• How – Jenkins pipelines
• Deployment specific testbeds
• Kafka cluster & producers
• Integration, performance & scale tests
• Fake test data for Kafka producer
• Generate network metrics, syslogs, SNMP traps
• Policy driven metrics
• In & out of bounds traffic rates, cpu, memory, protocol metrics
• Errors, alarms, synthetics and signals
• Deterministic violations

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AIOps – Application Perspective
Application Hop Counts
Application Latency Application Packetloss

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AIOps – Network Fabric Status
Fabric Status
Device Logs
BGP Peering Status
Device Port Status Site Traffic In
Site Traffic Out
DC-1 DC-2 DC-3

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AIOps – Correlations
• Connecting the dots
• Detect grey failures
• Examine impacts
Port
Availability
BGP
Status

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Summary
• Operational complexity of a large, diverse, evolving cloud environment
• Correlation of multiple data sources – The full picture
• Kafka empowers AIOps by collecting and normalizing data at scale
• Selector AIOps
• Provides simple & powerful insights
• Enhances decision making
• Reduces downtime
• Improves performance

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Questions & Answers
Thank you for your time!