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.
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 โ DevOps, LaaS, QA, Test Automation, Engineering Leadership
https://www.linkedin.com/in/marchornbeek Skype: mhexcalibur
http://devops.com/author/marc-hornbeek/ Twitter: mhexcalibur
โDevOps-the-grayโ
http://meetu.ps/306Lc3
3. โข DevOps Evolution โ the next generation?
โข DevOps trends โ state-of-practice and state-of-art
โข What is the future of DevOps and why?
โข How can an enterprise position itself now to take
advantage of future DevOps benefits?
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
ยฉ 123RF Stock Photo
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
9. Ad-Hoc / Innovation: Programming
1943
โColossusโ
Digital
Computer
Code Breaker
1939
A. Turingโs
โBombeโ
Enigma
Code
Breaker
1946
โENIACโ
Ballistics
Tables,
H-Bomb
1954
IBM 650
First Mass-
produced
computer
Programs dedicated to specific machines
10. Waterfall Process - 1956
โข Serial inefficient handoffs
between stages
Organization Silos
โข Disjointed responsibilities
โข Disjointed tool chains
โข 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 โ 1 MLOC
1967 - Function Test Control Programs, IBM
1967 - โSoftware Engineeringโ, NATO
1968 - โSoftware Quality Assuranceโ, 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
โข Collaborative teams test each iteration of a product development in
โSprintsโ. Agile emphasizes test automation. Does not prescribe
infrastructures for integrating test activities across the development-
to-delivery infrastructure.
Extreme programming & Test Driven Development (TDD)
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
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?
โข Collaborative culture
โข Design for DevOps
โข Continuous Integration (CI)
โข Continuous Testing (CT)
โข Continuous Monitoring (CM)
โข Elastic Infrastructures
โข Continuous Delivery and
Deployment (CD)
https://devops.com/2016/08/01/7-pillars-of-devops-essential-foundations-for-enterprise-success/
22. Developer Evolution
โข Developers thoroughly understand customer use cases
โข Culture supports designers
โข Design coding practices are critical
โข DevOps design practices support QA
โข DevOps design practices support Ops
โข Integrated tool suite
Metrics
โข Burn rate
โข % of effort on Toil vs new work
โข Check-in rate
โข Check-ins requiring remediation
โข Pass rates
https://devops.com/2016/10/03/design-devops-best-practices/
23. QA Role Evolution
QA team roles become more strategic
โข QA oversight
โข Robust testing infrastructure
โข Satisfying user experience
โข Engage the requirements process
โข Automated testing focused
Metrics
โข Risk (e.g. Failure trend algorithm)
โข Reliability (MTBF)
โข Test escapes
โข Automation %
โข Coverage %
http://www.datical.com/blog/qas-strategic-role-enterprise-
devops/
Edward Deming
โQA is everyoneโs responsibilityโ
24. DevOps Role Evolution
DevOps profession will mature
โข Everything as code
โข Roles specialties
โข End-to-end tools and infrastructure
โข Process automation scientist
โข End-to-end process metrics
โข New Ops?
Metrics
โข DevOps infrastructure availability
โข Pipeline reverts
http://www.datical.com/blog/qas-strategic-role-enterprise-devops/
25. Culture Evolution - Ops
Ops Admin transformation to
Reliability Engineer
โข Become cloud connoisseurs
โข Craft new automated processes to
embed Ops needs into Dev
Metrics
โข Automated process coverage
โข 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 !
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