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Overcoming tomorrow's operational challenges with AIOps - DEM05-R1 - Santa Clara AWS Summit.pdf

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Digital transformation, which requires an unprecedented scale and complex infrastructure, has sparked an IT service availability crisis in the enterprise. Traditional bottom-up, rules-driven approaches to incident management have outlived their usefulness. Artificial intelligence for IT operations, or AIOps, provides a new direction by using operational data to drive a top-down approach to service availability. This enables greater business agility while improving customer service, lowering operation costs, and boosting the productivity of IT operations teams. In this talk, Moogsoft CEO, Phil Tee, describes how AIOps provides enterprises with a critical advantage and paves the way for the future of service assurance.

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Overcoming tomorrow's operational challenges with AIOps - DEM05-R1 - Santa Clara AWS Summit.pdf

  1. 1. Overcoming Tomorrow's Operational Challenges with AIOps Phil Tee, CEO | Phil@Moogsoft.com27 March 2019
  2. 2. © 2019 M oogsoft. A ll rights reserved. Mainframe Era Golden Distributed Computing Tarnished Software Defined Everything Monitoring Platforms Monolithic Suites ISV Platforms Patchwork, Open Source, Departmental Source Events Fixed ~10-20 eps Custom/Standard (SNMP) + Fixed ~100-1000 eps Chaotic, Unstructured ~1000-100,000 eps Configuration Static Time between change ~ days (TBC) Flexible TBC ~ hours Chaotic TBC <1 second Infrastructure Monolithic Single Vendor Multi vendor UNIX/IP/Windows Client Server Virtualized/Containers Fluid/UNIX/PRP/Mobile/Micro Services 1980s 1990s 2000s 2010s What Will Tomorrow Look Like?
  3. 3. © 2019 M oogsoft. A ll rights reserved. The Economy Drives IT Form Follows Function… ...Function Follows Need Digital Transformation demands DevOps & Elastic Birth of the Mainframe on an American Airlines flight Deregulation drives the client/server to virtual transition
  4. 4. © 2019 M oogsoft. A ll rights reserved. On-Demand Economy Mandates CONSTANT CHANGE & ZERO DOWNTIME In this Digitally Transformed World, We... Purchase Transact Interact Access
  5. 5. © 2019 M oogsoft. A ll rights reserved. Scale • 105 + Moving Parts • 106 + Notifications • 109+ Data Points • 1012 → 10120 + Possible Failure Modes + Bounded by the estimated information content of the universe! Current and Future Demands Compulsion to Change Complexity • Reduction in the Unit of Compute • Mainframe → Server → VM → Container • Multiple Orders of Magnitude • Increase in Change Cycle • Fully Fluid CI/CD Cycle
  6. 6. © 2019 M oogsoft. A ll rights reserved. Siloed teams and tools Too many alerts No context when an incident occurs No early warning DevOps lacks proactive assurance DATA INFORMATIONOverwhelmed by and a lack of Traditional IT Ops Caught Flat-Footed
  7. 7. © 2019 M oogsoft. A ll rights reserved. 75-80% of IT organizations are siloed 95% of Alerts lack informational value >45% incidents involve “all hands” war room >73% incidents raised by end usersMany siloed war rooms DATA INFORMATIONOverwhelmed by and a lack of Traditional IT Ops Caught Flat-Footed
  8. 8. © 2019 M oogsoft. A ll rights reserved. Evidence of Systematic Failure Troubling Trends: CIO Survey Results 74% Incidents detected by customers before support is aware 66% Existing monitoring solutions identify less than half of all performance issues or outages 59% Growing IT complexity is leading to more outages Greater Scale, Less Visibility
  9. 9. © 2019 M oogsoft. A ll rights reserved. INCREASE frequency of change, stability and availability of IT services1 REDUCE resource operations workload and INCREASE productivity2 CONSOLIDATE tools3 MIGRATE to the cloud4 SUPPORT software-defined services5 SUPPORT microservices-based software architecture6 IT Ops Priorities Driven by Digital Transformation
  10. 10. © 2019 M oogsoft. A ll rights reserved. AFTER State + Measurement Combined DATA EVENTS SYSTEM Data AI Feature Act • Fluid • High Scale • Determinate What to Do? SYSTEM EVENTS BEFORE “State” “Measurement” Analyze Act Measure Hypothesize RULES • Rigid • Low Scale • Indeterminate
  11. 11. © 2019 M oogsoft. A ll rights reserved. Typical AIOps Use Cases Time Topology Entropy TextABC
  12. 12. © 2019 M oogsoft. A ll rights reserved. Collaboration RESOLUTION Learning Collaboration Situation Similarity Reduce MTTR through collaboration Inference SITUATIONS CAUSALITY Probable Root-Cause Vertex Entropy Reduce MTTD by pinpointing causality Discovery CORRELATION ABC Text Topology Time CLUSTERS Reduce workloads, time to act and business disruption by proactively detecting actionable issues TOPOLOGICAL GRAPH DATA Relevance 10101001 01001001 00110001 0101 <f(x),x1> <g(x),x1> INFORMATION RICH DATA STREAM ENTROPY Industrialize change by surfacing relevant alerts from noise Knowledge Recycling Capture experience, make it widely available Automating All Dimensions of AIOps
  13. 13. © 2019 M oogsoft. A ll rights reserved. Level 0/NOC Operators • Improve efficiency by consolidating related alerts together • Reduce catch-n-dispatch activities AIOps Makes Your Teams Faster, Smarter, and More Productive Support SMEs & Developers • Pass incident resolution knowledge to lower support tiers • Collaborate across complex multi-disciplinary incidents IT Operations Managers • Delivery service-level state monitoring • Improve efficiency and job satisfaction • Identify and address repeating mundane work with run book automation Problem Managers • Investigate and problem-solve for frequently repeating P3-P5 incidents IT Senior Management • Achieve overall per-alert efforts reduction • Re-purpose the savings towards business’s bottom line
  14. 14. © 2019 M oogsoft. A ll rights reserved. An AIOps Leader Innovation • Technology pioneers • +50 patents • $100M in funding since 2012 (Goldman Sachs, Cisco, Redpoint, Wing) Experience • Top tier team, 20+ yrs in the domain • Purpose built platform • 7th generation solution • Multiple products Value Transforming the economics for +130 of the Global 2000, across verticals
  15. 15. © 2019 M oogsoft. A ll rights reserved. Entropy Time Language Topology Vertex Entropy Algorithmic Correlation Engine Feedback Probable Root Cause Alert Significance Timestamp Patterns Text value similarities Network proximity patterns Critical node identification Algorithms application with a deterministic policy Learnings from Ops behavior Root cause identification Our +50 Machine Learning Patents Make AI Valuable and Consumable ABC Correlation Noise Reduction Causality
  16. 16. Thank You! Ph Phil Tee, CEO | Phil@Moogsoft.com27 March 2019

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