The document discusses the evolution of DevOps, emphasizing its importance for enhancing enterprise agility, security, and quality through automation and collaboration. It outlines the historical development of software engineering and DevOps practices, highlighting the transition from traditional methods to modern, automated pipelines and roles. The author, Marc Hornbeek, suggests that embracing DevOps principles is crucial for organizations to innovate and maintain competitiveness in the changing technological landscape.

1. 1
DevOps Evolution
the next generation
Marc Hornbeek
Principal Consultant DevOps
www.Trace3.com

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. • 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. “Extinction is the rule. Survival is the exception.” Carl Sagan

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. 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. 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. Software Engineering Evolution
Innovation
Mechanization
Variation
Standardization
Optimization
Ad-Hoc Repeatable Defined Managed Optimizing
Capability Maturity Model
?????

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. Waterfall Process - 1956
• Serial inefficient handoffs
between stages
Organization Silos
• Disjointed responsibilities
• Disjointed tool chains
• Manual workflows

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. 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. DevOps pipeline: 2009

14. Cauldron of DevOps Tools Stew
REPOs
ANALYTICS TEST TOOLS
TEST AUTOMATIONCODE TEST
DEPLOY
INFRASTRUCTUREBUILD

15. 15
“Sailors on a
becalmed sea, we
sense the stirring
of a breeze.”
Carl Sagan

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. Software Engineering Evolution
Innovation
Mechanization
Variation
Standardization
Optimization
Ad-Hoc Repeatable Defined Managed Optimizing
Capability Maturity Model
?
DevOpsWaterfall Engineering Agile
CI CD

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. 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. 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. 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. 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. 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. 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. 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. DevOps Pipeline Predictive Analytics
Work Live !
DevOps Evolution
Intelligent advanced data analytics drive end-to-end automated
self-optimizations

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. Example – Automated Gate Promotions

29. Use Case: Self-optimizing Test Selection
Analysis of test failure trends can drive
continuous test selections
Work Live !

30. Example – Automated Continuous Testing
Automated test
selection and
analysis

31. Use Case: Self-optimizing Fixer Assignments
Analysis of test result trends drive fixer
assignments
Work Live !

32. Use Case: Self-optimizing Infrastructure Scaling
Work Live !
Analysis of DevOps pipeline
process time trends drive
infrastructure scaling to meet
business goals

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. Use Case: Self-optimizing Infrastructure Cost
Work Live !
Analysis of DevOps
infrastructure cost trends can
drive infrastructure cost
optimization

35. 35
Prepare for role shifts
Choose tools with end-to-end automation
capabilities and plugins
Learn analytics
Preparing for the Future

36. 36
DevOps Evolution
the next generation
Marc Hornbeek
Principal Consultant DevOps
www.Trace3.com