Businesses are speeding up development and automating operations to remain competitive and to get large organizations to scale. Project based monolithic application updates are replaced by product teams owning containerized microservices. This puts developers on call, responsible for pushing code to production, fixing it when it breaks, and managing the cost and security aspects of running their microservices. In this world operations skill-sets are either embedded in the microservices development teams, or  building and operating API driven platforms. The platform automates stress testing, canary based deployment, penetration testing and enforces availability and security requirements.  There are no meetings or tickets to file in the delivery process for updating a containerized microservice, which can happen many times a day, and takes seconds to complete. The role of site reliability engineering moves from firefighting and fixing outages to buiding tools for finding problems and routing those problems to the right developers. SREs manage the incident lifecycle for customer visible problems, and measure and publish availability metrics. This may sound futuristic but Werner Vogels described this as “You build it, you run it” in 2006.

1. When developers operate,
and operators develop…
Adrian Cockcroft @adrianco
Technology Fellow - Battery Ventures
June 2015

2. “You build it, you run it.”
Werner Vogels 2006

3. Key Goals of the CIO?
Align IT with the business
Develop products faster
Try not to get breached

4. Security Blanket Failure
Insecure applications
hidden behind firewalls
make you feel safe until
the breach happens…
http://peanuts.wikia.com/wiki/Linus'_security_blanket

5. What needs to
change?

6. Developer responsibilities:
Faster, cheaper, safer

7. Operator responsibilities:
API driven platform automation

8. Product
Development
Processes

9. Assumption:
Process prevents
problems

10. Organizations build up
slow complex “Scar
tissue” processes

11. Observe
Orient
Decide
Act Continuous
Delivery

12. Observe
Orient
Decide
Act
Land grab
opportunity Competitive
Move
Customer Pain
Point
Measure
Customers
Continuous
Delivery

13. Observe
Orient
Decide
Act
Land grab
opportunity Competitive
Move
Customer Pain
Point
INNOVATION
Measure
Customers
Continuous
Delivery

14. Observe
Orient
Decide
Act
Land grab
opportunity Competitive
Move
Customer Pain
Point
Analysis
Model
Hypotheses
INNOVATION
Measure
Customers
Continuous
Delivery

15. Observe
Orient
Decide
Act
Land grab
opportunity Competitive
Move
Customer Pain
Point
Analysis
Model
Hypotheses
BIG DATA
INNOVATION
Measure
Customers
Continuous
Delivery

16. Observe
Orient
Decide
Act
Land grab
opportunity Competitive
Move
Customer Pain
Point
Analysis
JFDI
Plan Response
Share Plans
Model
Hypotheses
BIG DATA
INNOVATION
Measure
Customers
Continuous
Delivery

17. Observe
Orient
Decide
Act
Land grab
opportunity Competitive
Move
Customer Pain
Point
Analysis
JFDI
Plan Response
Share Plans
Model
Hypotheses
BIG DATA
INNOVATION
CULTURE
Measure
Customers
Continuous
Delivery

18. Observe
Orient
Decide
Act
Land grab
opportunity Competitive
Move
Customer Pain
Point
Analysis
JFDI
Plan Response
Share Plans
Incremental
Features
Automatic
Deploy
Launch AB
Test
Model
Hypotheses
BIG DATA
INNOVATION
CULTURE
Measure
Customers
Continuous
Delivery

19. Observe
Orient
Decide
Act
Land grab
opportunity Competitive
Move
Customer Pain
Point
Analysis
JFDI
Plan Response
Share Plans
Incremental
Features
Automatic
Deploy
Launch AB
Test
Model
Hypotheses
BIG DATA
INNOVATION
CULTURE
CLOUD
Measure
Customers
Continuous
Delivery

20. Observe
Orient
Decide
Act
Land grab
opportunity Competitive
Move
Customer Pain
Point
Analysis
JFDI
Plan Response
Share Plans
Incremental
Features
Automatic
Deploy
Launch AB
Test
Model
Hypotheses
BIG DATA
INNOVATION
CULTURE
CLOUD
Measure
Customers
Continuous
Delivery

21. Observe
Orient
Decide
Act
Land grab
opportunity Competitive
Move
Customer Pain
Point
Analysis
JFDI
Plan Response
Share Plans
Incremental
Features
Automatic
Deploy
Launch AB
Test
Model
Hypotheses
BIG DATA
INNOVATION
CULTURE
CLOUD
Measure
Customers
Continuous
Delivery

22. Breaking Down the SILOs

23. Breaking Down the SILOs
QA DBA
Sys
Adm
Net
Adm
SAN
Adm
DevUX
Prod
Mgr

24. Breaking Down the SILOs
QA DBA
Sys
Adm
Net
Adm
SAN
Adm
DevUX
Prod
Mgr
Product Team Using Monolithic Delivery
Product Team Using Monolithic Delivery

25. Breaking Down the SILOs
QA DBA
Sys
Adm
Net
Adm
SAN
Adm
DevUX
Prod
Mgr
Product Team Using Microservices
Product Team Using Monolithic Delivery
Product Team Using Microservices
Product Team Using Microservices
Product Team Using Monolithic Delivery

26. Breaking Down the SILOs
QA DBA
Sys
Adm
Net
Adm
SAN
Adm
DevUX
Prod
Mgr
Product Team Using Microservices
Product Team Using Monolithic Delivery
Platform TeamProduct Team Using Microservices
Product Team Using Microservices
Product Team Using Monolithic Delivery

27. Breaking Down the SILOs
QA DBA
Sys
Adm
Net
Adm
SAN
Adm
DevUX
Prod
Mgr
Product Team Using Microservices
Product Team Using Monolithic Delivery
Platform Team
A
P
I
Product Team Using Microservices
Product Team Using Microservices
Product Team Using Monolithic Delivery

28. Breaking Down the SILOs
QA DBA
Sys
Adm
Net
Adm
SAN
Adm
DevUX
Prod
Mgr
Product Team Using Microservices
Product Team Using Monolithic Delivery
Platform Team
DevOps is a Re-Org!
A
P
I
Product Team Using Microservices
Product Team Using Microservices
Product Team Using Monolithic Delivery

29. Release Plan
Developer
Developer
Developer
Developer
Developer
QA Release
Integration
Ops Replace Old
With New
Release
Monolithic service updates
Works well with a small number
of developers and a single
language like php, java or ruby

30. Release Plan
Developer
Developer
Developer
Developer
Developer
QA Release
Integration
Ops Replace Old
With New
Release
Bugs
Monolithic service updates
Works well with a small number
of developers and a single
language like php, java or ruby

31. Release Plan
Developer
Developer
Developer
Developer
Developer
QA Release
Integration
Ops Replace Old
With New
Release
Bugs
Bugs
Monolithic service updates
Works well with a small number
of developers and a single
language like php, java or ruby

32. Use monolithic apps for small teams,
simple systems and when you must,
to optimize for efficiency and latency

33. Developer
Developer
Developer
Developer
Developer
Old Release Still
Running
Release Plan
Release Plan
Release Plan
Release Plan
Immutable microservice deployment
scales, is faster with large teams and
diverse platform components

34. Developer
Developer
Developer
Developer
Developer
Old Release Still
Running
Release Plan
Release Plan
Release Plan
Release Plan
Deploy
Feature to
Production
Deploy
Feature to
Production
Deploy
Feature to
Production
Deploy
Feature to
Production
Immutable microservice deployment
scales, is faster with large teams and
diverse platform components

35. Developer
Developer
Developer
Developer
Developer
Old Release Still
Running
Release Plan
Release Plan
Release Plan
Release Plan
Deploy
Feature to
Production
Deploy
Feature to
Production
Deploy
Feature to
Production
Deploy
Feature to
Production
Bugs
Immutable microservice deployment
scales, is faster with large teams and
diverse platform components

36. Developer
Developer
Developer
Developer
Developer
Old Release Still
Running
Release Plan
Release Plan
Release Plan
Release Plan
Deploy
Feature to
Production
Deploy
Feature to
Production
Deploy
Feature to
Production
Deploy
Feature to
Production
Bugs
Deploy
Feature to
Production
Immutable microservice deployment
scales, is faster with large teams and
diverse platform components

37. Developer Developer
Run What You Wrote
Developer Developer

38. Developer Developer
Run What You Wrote
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Developer Developer

39. Developer Developer
Run What You Wrote
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Developer Developer
Monitoring
Tools

40. DeveloperDeveloper Developer
Run What You Wrote
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Developer Developer
Monitoring
Tools

41. DeveloperDeveloper Developer
Run What You Wrote
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Developer Developer
Site
Reliability
Monitoring
Tools
Availability
Metrics
99.95% customer
success rate

42. DeveloperDeveloper Developer
Run What You Wrote
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Developer Developer
Manager Manager
Site
Reliability
Monitoring
Tools
Availability
Metrics
99.95% customer
success rate

43. DeveloperDeveloper Developer
Run What You Wrote
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Micro
service
Developer Developer
Manager Manager
VP
Engineering
Site
Reliability
Monitoring
Tools
Availability
Metrics
99.95% customer
success rate

44. Change One Thing
at a Time!

45. What Happened?
Rate of change
increased
Cost and size and
risk of change
reduced

46. Low Cost of Change Using Docker
Developers
• Compile/Build
• Seconds
Extend container
• Package dependencies
• Seconds
PaaS deploy Container
• Docker startup
• Seconds

47. Low Cost of Change Using Docker
Fast tooling supports continuous delivery of many tiny changes
Developers
• Compile/Build
• Seconds
Extend container
• Package dependencies
• Seconds
PaaS deploy Container
• Docker startup
• Seconds

48. Disruptor:
Continuous Delivery with
Containerized Microservices

49. Microservices

50. A Microservice Definition
Loosely coupled service oriented
architecture with bounded contexts

51. A Microservice Definition
Loosely coupled service oriented
architecture with bounded contexts
If every service has to be
updated at the same time
it’s not loosely coupled

52. A Microservice Definition
Loosely coupled service oriented
architecture with bounded contexts
If every service has to be
updated at the same time
it’s not loosely coupled
If you have to know too much about surrounding
services you don’t have a bounded context. See the
Domain Driven Design book by Eric Evans.

53. Coupling Concerns
http://en.wikipedia.org/wiki/Conway's_law
●Conway’s Law - organizational coupling
●Centralized Database Schemas
●Enterprise Service Bus - centralized message queues
●Inflexible Protocol Versioning

54. Speeding Up Deployments
Datacenter Snowflakes
• Deploy in months
• Live for years

55. Speeding Up Deployments
Datacenter Snowflakes
• Deploy in months
• Live for years
Virtualized and Cloud
• Deploy in minutes
• Live for weeks

56. Speeding Up Deployments
Datacenter Snowflakes
• Deploy in months
• Live for years
Virtualized and Cloud
• Deploy in minutes
• Live for weeks
Container Deployments
• Deploy in seconds
• Live for minutes/hours

57. Speeding Up Deployments
Datacenter Snowflakes
• Deploy in months
• Live for years
Virtualized and Cloud
• Deploy in minutes
• Live for weeks
Container Deployments
• Deploy in seconds
• Live for minutes/hours
AWS Lambda Events
• Respond in milliseconds
• Live for seconds

58. Speeding Up Deployments
Measuring CPU usage once a minute makes no sense for containers…
Coping with rate of change is the first challenge for monitoring tools.
Datacenter Snowflakes
• Deploy in months
• Live for years
Virtualized and Cloud
• Deploy in minutes
• Live for weeks
Container Deployments
• Deploy in seconds
• Live for minutes/hours
AWS Lambda Events
• Respond in milliseconds
• Live for seconds

59. Inspiration

60. Challenges for
Microservice
Platforms

61. Managing Scale

62. A Possible Hierarchy
Continents
Regions
Zones
Services
Versions
Containers
Instances
How Many?
3 to 5
2-4 per Continent
1-5 per Region
100’s per Zone
Many per Service
1000’s per Version
10,000’s
It’s much more challenging
than just a large number of
machines

63. Flow

64. Some tools can show
the request flow
across a few services

65. But interesting
architectures have a
lot of microservices!
Flow visualization is
a big challenge.
See http://www.slideshare.net/LappleApple/gilt-from-monolith-ruby-app-to-micro-service-scala-service-architecture

66. Failures

67. ELB Load Balancer
Zuul API Proxy
Karyon
Business
Logic
Staash Data
Access Layer
Priam Cassandra
Datastore
Simple NetflixOSS
style microservices
architecture on three
AWS Availability Zones

68. ELB Load Balancer
Zuul API Proxy
Karyon
Business
Logic
Staash Data
Access Layer
Priam Cassandra
Datastore
Simple NetflixOSS
style microservices
architecture on three
AWS Availability Zones

69. ELB Load Balancer
Zuul API Proxy
Karyon
Business
Logic
Staash Data
Access Layer
Priam Cassandra
Datastore
Simple NetflixOSS
style microservices
architecture on three
AWS Availability Zones
Zone partition/failure
What should you do?
What should monitors show?

70. ELB Load Balancer
Zuul API Proxy
Karyon
Business
Logic
Staash Data
Access Layer
Priam Cassandra
Datastore
Simple NetflixOSS
style microservices
architecture on three
AWS Availability Zones
Zone partition/failure
What should you do?
What should monitors show?
By design, everything works
with 2 of 3 zones running.
This is not an outage, inform
but don’t touch anything!
Halt deployments perhaps?

71. ELB Load Balancer
Zuul API Proxy
Karyon
Business
Logic
Staash Data
Access Layer
Priam Cassandra
Datastore
Simple NetflixOSS
style microservices
architecture on three
AWS Availability Zones
Zone partition/failure
What should you do?
What should monitors show?
By design, everything works
with 2 of 3 zones running.
This is not an outage, inform
but don’t touch anything!
Halt deployments perhaps?
Challenge: understand and
communicate common
microservice failure patterns.

72. Testing

73. Testing monitoring tools at scale
gets expensive quickly…

74. Simulation

75. Simulated Microservices
Model and visualize microservices
Simulate interesting architectures
Generate large scale configurations
Eventually stress test real tools
See github.com/adrianco/spigo
Simulate Protocol Interactions in Go
Visualize with D3
ELB Load Balancer
Zuul API Proxy
Karyon
Business
Logic
Staash
Data
Access
Layer
Priam Cassandra
Datastore
Three
Availability
Zones

76. Definition of an architecture
{
"arch": "netflixoss",
"description":"A very simple Netflix service. See http://netflix.github.io/ to decode the package names",
"version": "arch-0.0",
"victim": "homepage",
"services": [
{ "name": "cassSubscriber", "package": "priamCassandra", "count": 6, "regions": 1, "dependencies": ["cassSubscriber", "eureka"]},
{ "name": "evcacheSubscriber","package": "store", "count": 3, "regions": 1, "dependencies": []},
{ "name": "subscriber", "package": "staash", "count": 6, "regions": 1, "dependencies": ["cassSubscriber", "evcacheSubscriber"]}
{ "name": "login", "package": "karyon", "count": 18, "regions": 1, "dependencies": ["subscriber"]},
{ "name": "homepage", "package": "karyon", "count": 24, "regions": 1, "dependencies": ["subscriber"]},
{ "name": "wwwproxy", "package": "zuul", "count": 6, "regions": 1, "dependencies": ["login", "homepage"]},
{ "name": "www-elb", "package": "elb", "count": 0, "regions": 1, "dependencies": ["wwwproxy"]},
{ "name": "www", "package": "denominator", "count": 0, "regions": 0, "dependencies": ["www-elb"]}
]
}
Header includes
chaos monkey victim
New tier
name
Tier
package
Region
count: 1
Node
count
List of tier
dependencies

77. Why Build Spigo/Simianviz?
Generate test microservice configurations at scale
Stress monitoring tools display capabilities
Eventually (i.e. not implemented yet)
Dynamically vary configuration: autoscale, code push
Chaos monkey for microservice, zone, region failures
D3 websocket dynamic browser interface

78. Simulated Game
Day Testing and
Training…

79. Conference Driven Development…
OSCON Microservices Workshop

80. What’s Next?

81. Developer Concerns
Agile, Lean & Rugged
(Faster, Cheaper and Safer)
See GOTO London Sept 2015

82. Forward Thinking

83. Forward Thinking

84. Any Questions?
Disclosure: some of the companies mentioned may be Battery Ventures Portfolio Companies
See www.battery.com for a list of portfolio investments
● Battery Ventures http://www.battery.com
● Adrian’s Tweets @adrianco and Blog http://perfcap.blogspot.com
● Slideshare http://slideshare.com/adriancockcroft
● Monitorama Opening Keynote Portland OR - May 7
th
, 2014
● GOTO Chicago Opening Keynote May 20
th
, 2014
● Qcon New York – Speed and Scale - June 11
th
, 2014
● Structure - Cloud Trends - San Francisco - June 19th, 2014
● GOTO Copenhagen/Aarhus – Fast Delivery - Denmark – Sept 25
th
, 2014
● DevOps Enterprise Summit - San Francisco - Oct 21-23rd, 2014 #DOES14
● GOTO Berlin - Migrating to Microservices - Germany - Nov 6th, 2014
● AWS Re:Invent - Cloud Native Cost Optimization - Las Vegas - November 14th, 2014
● O’Reilly Software Architecture Conference - Fast Delivery, Monitoring Challenge - Boston March 16th 2015

85. Security
Visit http://www.battery.com/our-companies/ for a full list of all portfolio companies in which all Battery Funds have invested.
Palo Alto Networks
Enterprise IT
Operations &
Management
Big DataCompute
Networking
Storage