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DevOps Evolution
the next generation
Marc Hornbeek
Principal Consultant DevOps
www.Trace3.com
Marc Hornbeek
// Principal Consultant - DevOps, ETS
Marc is a consultant with over 37 years of experience architecting, de...
• DevOps Evolution – the next generation?
• DevOps trends – state-of-practice and state-of-art
• What is the future of Dev...
“Extinction is the rule. Survival is the exception.” Carl Sagan
Business Problems
Addressed by DevOps
Agility
Lack of innovation
Security
Unauthorized uses
Satisfaction
Employee frustrat...
Technology Evolution
Innovation
Mechanization
Variation
Standardization
Optimization
Ad-Hoc Repeatable Defined Managed Opt...
Agility
Security
Satisfaction Quality
Stability
50%
Efficiency
Less time on unplanned work
and rework
Shorter
lead times
E...
Software Engineering Evolution
Innovation
Mechanization
Variation
Standardization
Optimization
Ad-Hoc Repeatable Defined M...
Ad-Hoc / Innovation: Programming
1943
“Colossus”
Digital
Computer
Code Breaker
1939
A. Turing’s
“Bombe”
Enigma
Code
Breake...
Waterfall Process - 1956
• Serial inefficient handoffs
between stages
Organization Silos
• Disjointed responsibilities
• D...
Repeatable / Mechanization: Large Scale Software
1961 - Computer Programming Fundamentals, Leeds, Weinberg
1963 - Flowchar...
Defined / Variation: Agile Method 1995
• Collaborative teams test each iteration of a product development in
“Sprints”. Ag...
DevOps pipeline: 2009
Cauldron of DevOps Tools Stew
REPOs
ANALYTICS TEST TOOLS
TEST AUTOMATIONCODE TEST
DEPLOY
INFRASTRUCTUREBUILD
15
“Sailors on a
becalmed sea, we
sense the stirring
of a breeze.”
Carl Sagan
Managed/Standardization : DevOps pipeline
P2675 - DevOps - Standard for
Building Reliable and Secure
Systems Including App...
Software Engineering Evolution
Innovation
Mechanization
Variation
Standardization
Optimization
Ad-Hoc Repeatable Defined M...
Optimizing : DevOps Evolution
DevOps 2.0
Orchestrated
deployment
infrastructure, micro-
services, containers
DevOps Evolut...
Optimizing : DevOps Predictive Analytics
Input
The analyzed result
is used to drive the
input of one or more
DevOps stages...
How to evolve and keep
the DevOps 7 pillars
in balance?
• Collaborative culture
• Design for DevOps
• Continuous Integrati...
Leadership Evolution
http://searchcio.techtarget.com/feature/DevOps-model-a-profile-in-CIO-leadership-change-management
• ...
Developer Evolution
• Developers thoroughly understand customer use cases
• Culture supports designers
• Design coding pra...
QA Role Evolution
QA team roles become more strategic
• QA oversight
• Robust testing infrastructure
• Satisfying user exp...
DevOps Role Evolution
DevOps profession will mature
• Everything as code
• Roles specialties
• End-to-end tools and infras...
Culture Evolution - Ops
Ops Admin transformation to
Reliability Engineer
• Become cloud connoisseurs
• Craft new automated...
DevOps Pipeline Predictive Analytics
Work Live !
DevOps Evolution
Intelligent advanced data analytics drive end-to-end aut...
Use Case: Self-optimizing Dev QA checks
Work
Analysis of production failure trends drive
application development process t...
Example – Automated Gate Promotions
Use Case: Self-optimizing Test Selection
Analysis of test failure trends can drive
continuous test selections
Work Live !
Example – Automated Continuous Testing
Automated test
selection and
analysis
Use Case: Self-optimizing Fixer Assignments
Analysis of test result trends drive fixer
assignments
Work Live !
Use Case: Self-optimizing Infrastructure Scaling
Work Live !
Analysis of DevOps pipeline
process time trends drive
infrast...
Dev
DevOps Pipeline Model
33
Work Df Cf Pf Rf
System simulations and experiences have shown that optimum agility, efficien...
Use Case: Self-optimizing Infrastructure Cost
Work Live !
Analysis of DevOps
infrastructure cost trends can
drive infrastr...
35
Prepare for role shifts
Choose tools with end-to-end automation
capabilities and plugins
Learn analytics
Preparing for ...
36
DevOps Evolution
the next generation
Marc Hornbeek
Principal Consultant DevOps
www.Trace3.com
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DevOps Evolution - The Next Generation ?

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Where is DevOps in its maturity? Is DevOps life near its beginning, middle, mature, near end-of-life or near extinction? What does the next generation look like? This presentation posits the next generation will be a new level of process optimization driven by coupling analytics with DevOps pipeline tools and associated role shifts.

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DevOps Evolution - The Next Generation ?

  1. 1. 1 DevOps Evolution the next generation Marc Hornbeek Principal Consultant DevOps www.Trace3.com
  2. 2. Marc Hornbeek // Principal Consultant - DevOps, ETS Marc is a consultant with over 37 years of experience architecting, designing, developing and managing high-performance solutions for IT and engineering infrastructures deployed in commercial and government applications globally. Marc has served in senior roles including CEO, Board Member, founder, corporate executive, CTO, VP, General Manager, Principal Consultant, Senior Solutions Architect and Professional Engineer. Bell-Northern Research, Tekelec, ECI Telecom, GSI Lumonics, Vpacket, EdenTree Technologies, Spirent Communications and Trace3. Marc is an innovator who has lead many successful automation, Lab-as-a-Service and DevOps projects for systems manufacturers and operators. Marc is a regular speaker, blogger, author and educator on topics including DevOps, Lab-as-a-Service and continuous test automation. Skills: Consulting – DevOps, LaaS, QA, Test Automation, Engineering Leadership https://www.linkedin.com/in/marchornbeek Skype: mhexcalibur http://devops.com/author/marc-hornbeek/ Twitter: mhexcalibur “DevOps-the-gray” http://meetu.ps/306Lc3
  3. 3. • DevOps Evolution – the next generation? • DevOps trends – state-of-practice and state-of-art • What is the future of DevOps and why? • How can an enterprise position itself now to take advantage of future DevOps benefits?
  4. 4. “Extinction is the rule. Survival is the exception.” Carl Sagan
  5. 5. Business Problems Addressed by DevOps Agility Lack of innovation Security Unauthorized uses Satisfaction Employee frustration Quality Failure frequency Stability Long problem fix time Efficiency Wasted resources © 123RF Stock Photo
  6. 6. Technology Evolution Innovation Mechanization Variation Standardization Optimization Ad-Hoc Repeatable Defined Managed Optimizing Capability Maturity Model “You have to know the past to understand the present.” Carl Sagan
  7. 7. Agility Security Satisfaction Quality Stability 50% Efficiency Less time on unplanned work and rework Shorter lead times Employees more likely to recommend their organizations as a great place to work. Faster recovery time for failures Less time remediating security issues 3x lower change failure rate 22% More frequent deployments DevOps is an Enterprise Success Differentiator
  8. 8. Software Engineering Evolution Innovation Mechanization Variation Standardization Optimization Ad-Hoc Repeatable Defined Managed Optimizing Capability Maturity Model ?????
  9. 9. Ad-Hoc / Innovation: Programming 1943 “Colossus” Digital Computer Code Breaker 1939 A. Turing’s “Bombe” Enigma Code Breaker 1946 “ENIAC” Ballistics Tables, H-Bomb 1954 IBM 650 First Mass- produced computer Programs dedicated to specific machines
  10. 10. Waterfall Process - 1956 • Serial inefficient handoffs between stages Organization Silos • Disjointed responsibilities • Disjointed tool chains • Manual workflows
  11. 11. Repeatable / Mechanization: Large Scale Software 1961 - Computer Programming Fundamentals, Leeds, Weinberg 1963 - Flowchart symbols standard, Rossheim 1964 - First Basic program, Dartmouth College 1965 - IBM 360 – 1 MLOC 1967 - Function Test Control Programs, IBM 1967 - “Software Engineering”, NATO 1968 - “Software Quality Assurance”, NATO 1971 - IEEE Computer Society founded 1972 - C, Dennis Ritchie, Brian Kernighan 1974 - MIL-S-52779 SW Quality Requirements 1975 - Microsoft founded 1976 - Apple founded 1976 - SW reliability: principles, Glenford Myers 1976 - Design and Code Inspections, Michael Fagan 1976 - Cyclomatic complexity metric, Tom McCabe 1979 - The Art of Software Testing, Glenford Myers 1979 1983 - IEEE 829 Standard for Software Test
  12. 12. Defined / Variation: Agile Method 1995 • Collaborative teams test each iteration of a product development in “Sprints”. Agile emphasizes test automation. Does not prescribe infrastructures for integrating test activities across the development- to-delivery infrastructure. Extreme programming & Test Driven Development (TDD)
  13. 13. DevOps pipeline: 2009
  14. 14. Cauldron of DevOps Tools Stew REPOs ANALYTICS TEST TOOLS TEST AUTOMATIONCODE TEST DEPLOY INFRASTRUCTUREBUILD
  15. 15. 15 “Sailors on a becalmed sea, we sense the stirring of a breeze.” Carl Sagan
  16. 16. Managed/Standardization : DevOps pipeline P2675 - DevOps - Standard for Building Reliable and Secure Systems Including Application Build, Package and Deployment TST006_CICD_and_Devops_report DevOps tools integrations DevOps Training DevOps Standards Plugins Plugins
  17. 17. Software Engineering Evolution Innovation Mechanization Variation Standardization Optimization Ad-Hoc Repeatable Defined Managed Optimizing Capability Maturity Model ? DevOpsWaterfall Engineering Agile CI CD
  18. 18. Optimizing : DevOps Evolution DevOps 2.0 Orchestrated deployment infrastructure, micro- services, containers DevOps Evolution Intelligent advanced data analytics drive automate end-to-end self-optimizations DevOps 1.0 • Culture • Automated Dev/CI/Delivery pipeline Work Live ! 2009 2019? 2016
  19. 19. Optimizing : DevOps Predictive Analytics Input The analyzed result is used to drive the input of one or more DevOps stages to cause the DevOps pipeline to perform in accordance to desired goals.DevOps Stage X Monitor output AnalysisProcess Control Input Output Desired output DevOps Stage Y Process Control Output of a DevOps stage is monitored and analyzed for specific characteristics
  20. 20. How to evolve and keep the DevOps 7 pillars in balance? • Collaborative culture • Design for DevOps • Continuous Integration (CI) • Continuous Testing (CT) • Continuous Monitoring (CM) • Elastic Infrastructures • Continuous Delivery and Deployment (CD) https://devops.com/2016/08/01/7-pillars-of-devops-essential-foundations-for-enterprise-success/
  21. 21. Leadership Evolution http://searchcio.techtarget.com/feature/DevOps-model-a-profile-in-CIO-leadership-change-management • DevOps champion • Sell DevOps vision to colleagues and staff • Shepherd teams through changes • Juggle staff, hire new talent, retrain others and develop new skills • Budget for DevOps transformations and journeys Metrics • Deployment rate • Stability (MTR) • Staff satisfaction ratings • Quality (MTBF) • Security event rates • Efficiency - CapEx and OpEx per unit
  22. 22. Developer Evolution • Developers thoroughly understand customer use cases • Culture supports designers • Design coding practices are critical • DevOps design practices support QA • DevOps design practices support Ops • Integrated tool suite Metrics • Burn rate • % of effort on Toil vs new work • Check-in rate • Check-ins requiring remediation • Pass rates https://devops.com/2016/10/03/design-devops-best-practices/
  23. 23. QA Role Evolution QA team roles become more strategic • QA oversight • Robust testing infrastructure • Satisfying user experience • Engage the requirements process • Automated testing focused Metrics • Risk (e.g. Failure trend algorithm) • Reliability (MTBF) • Test escapes • Automation % • Coverage % http://www.datical.com/blog/qas-strategic-role-enterprise- devops/ Edward Deming “QA is everyone’s responsibility”
  24. 24. DevOps Role Evolution DevOps profession will mature • Everything as code • Roles specialties • End-to-end tools and infrastructure • Process automation scientist • End-to-end process metrics • New Ops? Metrics • DevOps infrastructure availability • Pipeline reverts http://www.datical.com/blog/qas-strategic-role-enterprise-devops/
  25. 25. Culture Evolution - Ops Ops Admin transformation to Reliability Engineer • Become cloud connoisseurs • Craft new automated processes to embed Ops needs into Dev Metrics • Automated process coverage • Production infrastructure availability
  26. 26. DevOps Pipeline Predictive Analytics Work Live ! DevOps Evolution Intelligent advanced data analytics drive end-to-end automated self-optimizations
  27. 27. Use Case: Self-optimizing Dev QA checks Work Analysis of production failure trends drive application development process to improve defect detection for failed code areas Live !
  28. 28. Example – Automated Gate Promotions
  29. 29. Use Case: Self-optimizing Test Selection Analysis of test failure trends can drive continuous test selections Work Live !
  30. 30. Example – Automated Continuous Testing Automated test selection and analysis
  31. 31. Use Case: Self-optimizing Fixer Assignments Analysis of test result trends drive fixer assignments Work Live !
  32. 32. Use Case: Self-optimizing Infrastructure Scaling Work Live ! Analysis of DevOps pipeline process time trends drive infrastructure scaling to meet business goals
  33. 33. Dev DevOps Pipeline Model 33 Work Df Cf Pf Rf System simulations and experiences have shown that optimum agility, efficiency, quality and stability are achieved when input rates are highest, stage durations are short, most bugs are found during earlier stages of the pipeline, and the time between stages is equal so there is continuous flow. Backlog rate Di/t New Failed changes to be reworked CI Deliver DeployCi/t Pi/t Ri/t L/t Dt Ct Pt Rt Minimum pipeline transit time Live Lf
  34. 34. Use Case: Self-optimizing Infrastructure Cost Work Live ! Analysis of DevOps infrastructure cost trends can drive infrastructure cost optimization
  35. 35. 35 Prepare for role shifts Choose tools with end-to-end automation capabilities and plugins Learn analytics Preparing for the Future
  36. 36. 36 DevOps Evolution the next generation Marc Hornbeek Principal Consultant DevOps www.Trace3.com

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