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Mini-Track: AI and ML in Network Operations Applications

Moderator: Scott Robohn, Network Automation Forum Speakers: Dave Siegel, Selector Igor Giangrossi, Nokia Javier Antich, Anetta.ai

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Your logo
here
Autonomous Intelligent
Agents
The next frontier in Network Automation
Javier Antich - javier@anetta.ai
VIDEO
Network infrastructures
are more complex than
ever
Qualified resources are
more scarce than ever
Network infrastructures
are more mission critical
than ever
Knowledge softwarization
Knowledge dependent on people
Scarce, limited.
user user
API call
+
Knowledge compiled in software
Broadly available at the distance of an API call
Elastic, scalable, “unlimited”
The LLMs are huge
“knowledge compilators”
The network softwarization journey
Standarization of
APIs
Network
Softwarization
Pervasive
Network Data
Decision
points
Decision
optimization
Cloud
LLM
Knowledge
softwarization
Dynamic
networks
Autonomous
agents
Autonomous
networks
LLM-Powered Autonomous Agents
From
LLM
Imperative / prescriptive
Task execution:
• Generate text
• Summarize text
• Translate text
• Q&A
• ...
To
Memory Function
Planning
Action
Intent based / descriptive
- Complex problem solver
- Autonomous execution
- Example: Anetta.ai
LLM-powered
Agent
Complex network information search

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Mini-Track: AI and ML in Network Operations Applications

  • 1. Your logo here Autonomous Intelligent Agents The next frontier in Network Automation Javier Antich - javier@anetta.ai VIDEO
  • 2. Network infrastructures are more complex than ever Qualified resources are more scarce than ever Network infrastructures are more mission critical than ever
  • 3. Knowledge softwarization Knowledge dependent on people Scarce, limited. user user API call + Knowledge compiled in software Broadly available at the distance of an API call Elastic, scalable, “unlimited” The LLMs are huge “knowledge compilators”
  • 4. The network softwarization journey Standarization of APIs Network Softwarization Pervasive Network Data Decision points Decision optimization Cloud LLM Knowledge softwarization Dynamic networks Autonomous agents Autonomous networks
  • 5. LLM-Powered Autonomous Agents From LLM Imperative / prescriptive Task execution: • Generate text • Summarize text • Translate text • Q&A • ... To Memory Function Planning Action Intent based / descriptive - Complex problem solver - Autonomous execution - Example: Anetta.ai LLM-powered Agent
  • 8. Network infrastructures are more complex than ever Qualified resources are more scarce than ever Network infrastructures are more mission critical than ever LLM- Powered Autonomous Agents
  • 9. Your logo here AI in Networking: where does it fit? Igor Giangrossi Sr. Dir, Consulting Engineering
  • 11. Typical use cases of AI/ML in Networking Reporting Thresholding Baselining Clustering Predicting Collect current and historical data for effective visualization and analysis Detect situations where customized indicators violate defined thresholds Learn the expected range of values for key resources Identify things that appear to be behaving differently than others Anticipate behavior that might happen in the future Passive Pro-active Active
  • 12. Centralized data collection and analysis AI/ML Actions Data
  • 13. Could we use AI/ML inside routers? Challenges: • What is the use case? • Processing resources (CPU, Memory, Disk)? • App development environment / SDK? • Data collection API? • Analysis only, or closed loop automation? • How to use the output? Opportunities: • All system state readily available • Real-time processing of events? • Smaller, custom-trained models?
  • 14. Demo: ChatGPT app in Nokia SR Linux Natural language as a CLI helper Hardware eXtensible Data Path (XDP) Infrastructure Standard Linux Kernel Pub/Sub via protobufs/gRPC Lightweight Impart Database (IDB) NetOps Development Kit (NDK) Applications BGP OSPF QoS … ACL ChatGPT App BFD ISIS MPLS Management YANG Models gNMI gNOI gRIBI OC gRPC CLI Question + context Answer LLM
  • 16. Your logo here The AIOps Maturity Model for networking Dave Siegel
  • 17. Agenda ● Definitions: Monitoring, Observability and AI Ops ● Maturity model overview ● Progression through the model ● Use Case: Tracfone
  • 18. Monitoring – Observability - AIOps Monitoring Observability AI Ops Aggregate MELT Gather MELT Root Cause Analysis Predictive Analytics Use Cases Benefits Prescriptive Analytics Self-healing Anomaly detection Support Audits Noise Reductio n Natural Language Interaction s
  • 19. Maturity Model Passive Ops Too much data – rely on customer- triggered incident Reactive and highly manual processes Silod Ops teams – poor collaboration Active Ops Silo’d observability tools Part Reactive, part proactive Silo’d Ops teams – some collaboration AIOps AI Augmented Operations Highly Proactive Strong Ops Team Collaboration NoOps Autonomous AI Supervised full automation Ops team re- skilled as developers
  • 20. Passive to Active Ops Graduation Criteria: ● Exec Sponsorship – budgets and business goal alignment ● Manual operations transitioned to tools-led ops landscape ● Architects, ops teas and developers focused on the discipline of observability Characteristics of Active Ops ● Proactive Alerting on serious issues, detecting hazcons, predicting unknown ‘unknowns’ ● Filtering noise in telemetry data ● Correlation of events across data types and sources – reduced alert fatigue ● Collected and analyzed data measures SLA and SLO compliance
  • 21. Active Ops to AI Ops Active Ops Graduation Criteria ● Adoption of enterprise AI discipline ● Refactored Ops processes to gear them towards an AI-based automation environment ● AIOps solutions are the central operations function in the enterprise ● AIOps solutions are linked seamlessly with existing Monitoring and Observability solutions Characterization of AI Ops ● Decision making is augmented by AI-based tools through descriptive and predictive analytics, as well as remediation recommendations ● Expert management of large data systems/modern data lakes/lakehouse ● Enhanced Root Cause Analysis, Anomaly & Outlier detection, and correlation of these machine interpreted incidents ● Prediction and forecasting inherent in the ML models ● Highly collaborative ops teams ● AI tools are trained/customized to the specific domain(s) they support
  • 22. Active to AI Ops Use Case: Tracfone Challenges ● Over a dozen monitoring tools ● Excellent Eng and Ops staff in their specific domain ● Cross-domain challenges resulted significant outage times The Implementation of Selector AIOps ● Ingested data from 18 different tools that monitored everything from the network all the way up to the application layer ● AI driven alert & event filtering, and correlation ● Graphical dashboards presenting a unified view and analytical insights Results ● Huge reduction in MTTR, especially in major incidents ● Improved overall uptime with proactive identification of potential incident causing issues