This document summarizes a presentation given by James Watters, SVP of Products at Pivotal, about Pivotal's cloud native R&D strategy. Some key points include: - Pivotal's focus is on transforming how software is built through products like Cloud Foundry, Spring, and Spring Cloud. - Trends like low-cost computing and ubiquitous connectivity are enabling cloud native software companies to reshape the world. - Pivotal is investing in technologies like container orchestration, multi-cloud cloud orchestration, and open source API-driven cloud integration. - Spring Boot is driving adoption for its benefits of high productivity, direct support for microservices through the JVM,

1. JPMC Glasgow Symposium
James Watters, SVP, Products

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Major Projects
• Open Solaris
• Scale out x86
• Cloud Foundry Since start 2010
• Spring, Boot generation
CF 2011 Core Team
Bio
Today
• Product leader at Pivotal
• 50/50 time split between
Customers and product
development

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Our mission is to transform
how the world builds software

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75%
of application development supporting digital
business will be built not bought by 2020
Source: Gartner

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Near-free
computing costs
Mobile devices
and connectivity
Ubiquity of
embedded sensors
Global scale
of operations
KEY TRENDS
The cost and ubiquity of computing
enables cloud native software
companies to reshape the world

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CASE STUDY: DAIMLER
The engine behind the
Mercedes-Benz connected car

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CASE STUDY: GENERAL ELECTRIC
The engine behind
GE Predix

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Is Cloud Native Enterprise Happening?
• Friday, London: Global Banking giant, building new digital
banking application for 27 country roll out
• Monday, Frankfurt: Stock Exchange looking to rebuild
whole stack cloud native
• Tuesday, Munich: Insurance giant 5 new agile projects
apps on platform
• Wednesday, Thursday: Co-Launch of Bosch’s industrial
cloud for connected services …

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Pivotal’s Cloud Native R&D Strategy?
• 350 Engineers on PCF, Spring growing to 500…
• Where should we go?
• Why?

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“How much value do
you get from managing
operating systems”

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Focus On First Principles
• Everything run as a service before it’s shipped as software
• Zero human intervention in individual machines through
lifecycle; immutable platform infrastructure
• Service automation’s goal is to ship holistic cloud services not
ad-hoc automation simplicity
• Multi-cloud primitives vs. AWS only features etc
• Industry standard Linux containers for speed; Virtual
machines when needed for advanced data/state management

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Container Orchestration
• Multi-Tenant
• Self Service
• Self Healing

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Multi-cloud Cloud Orchestration
and automation
• Builds of each supported cloud
• Configuration dialects != service delivery differentiation
• Run everything as a service on multiple clouds before
shipping as software
• Automation exists to deliver cloud native services not for
ad-hoc human intervention

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OSS API driven cloud integration drives
broad industry collaboration
Azure CPI donation
VMware on team 2 engineers on CPI
Openstack CPI helped by Pivotal, SAP, IBM
Google green light to contribute to CPI

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• Simplified Manifest
Generation
• On Demand Service
Provisioning
• Dedicated & Shared plans
Bosh 2.0 – Coming Soon

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First Principles
• Velocity and autonomy of delivery for large teams in enterprise
environments
• Enterprise safety/security with startup cycle time (simple
repeatable deploys)
• Optimized for Cloud Native applications
• “Velocity on the JVM is the Killer App”

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Accelerating Velocity of Iterations
Ford Connected Car Microservices on Concourse
• Continuous delivery key to
iterative development
process
• Cloud Foundry is the first
platform built assuming CD
as the prime directive
• Deploy without risk of
failure, fully instrumented,
and optimized to support
rapid change

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Innovation Speed in Java WINS
“Velocity on
the JVM is the
Killer App”
Andy Glover (Netflix Cloud Ops)
@ SpringOne2GX 2014 Keynote –
Early Spring Boot adopters
Youtube Link to Video

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• Dynamic language productivity with maturity
of enterprise Java
• Cloud Native: Direct support for
Microservices, NetflixOSS++
• Fully automated app server configuration
and deployment
• Production ready Ops metrics out of the
box, with a switch
Spring Boot

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THEN NOW
• Java was verbose, complex
• Manual, SOAP based SOA
• App server sprawl, manual
• Complex Ops, Admin, Manage
• Low productivity for
Development and ops
• Dynamic language productivity
• Dynamic, REST microservices
• Autoconfig & embed app server
• Prod Ops features with a switch
• High productivity shared
• DevOps responsibility

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source: oss.sonatype.org
Monthly Maven downloads 2.25M
Spring Boot Adoption

29. Spring Integration survey data from
DZone 2014: ESB/EIP market leader
Industry Leadership

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31. Spring Cloud

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• Runtimes and their client libraries for:
– Service Discovery
– Configuration Management
– Circuit Breakers
– Intelligent Client Side Routing
– Reverse Proxies
– ….and much more
• Security (encrypt, SSO, roles)
• Connectors for CF, Heroku, AWS
Spring Cloud
application coordination patterns

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Service Registration / Discovery

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• Fully integrated to Cloud Foundry Service
– dramatically lower operational complexity
• Enterprise Security
– OAUTH2, HTTPS, CF UAA integration, RBAC
• Simple provisioning and setup
– Lower operational costs and time to provision
Spring Cloud Services for PCF
NetflixOSS-as-a-service in Pivotal Cloud Foundry

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Startups and Cloud Natives are leading
adoption of Spring Cloud
NDA Only for startup user list

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Spring Cloud
Services for .Net

38. Steel Toe

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Steel Toe is an open-source project to make the Spring Cloud
server components consumable from .NET applications with an
idiomatic .NET experience
• Config Server + Auto-refresh
• Service Registration/Discovery (Eureka)
• Circuit Breaker (Hystrix)
What is Steel Toe?

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• .NET Core on Linux
– Currently in RC1 (via the ASP.NET 5 buildpack)
– This is the target framework for the project
• .NET Core on Windows 2012r2
– On a Diego Cell
– This also seems to work
Supported Frameworks

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Netflix Atlas: Primary Telemetry Platform
• Who is building Atlas for the cloud
native enterprise?
• Canary deployment metrics
integration
• Advanced metrics + Lifecycle
automation = ?

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PCF Metrix BETA
• Fast feedback loop that helps app
devs better understand the health
and performance of their apps
• Live stream and 24 hours of data
• Works without an embedded agent
• Metrics include:
– HTTP requests and errors
– Avg. response latency
– CPU, memory and disk
– App events like start, stop, scale,
update, and crash

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Operational Visibility: Distributed Tracing
• Latency visibility into a request’s end-to-end call graph
• Quickly identify a problematic service in a distributed system
• Zipkin is a open source distributed tracing system. It helps gather timing data
needed to troubleshoot latency problems in microservice architectures.
• Pivotal is investing in Zipkin to solve distributed tracing use cases
– Apache 2.0 License
– Created by Twitter in 2012.
– In 2015, OpenZipkin became the primary fork
Zipkin Tracing

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openzipkin

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47. • PCF Developers can redirect application traffic to a desired request
path in order to use logging, authentication or rate limiting systems
that exist outside of PCF
• PCF’s Service API will introduce a new field: route_service_url
• Developers will create a routing service instance and bind it to a
route (not an app)
– Service Instance can be created by a Service Broker or can
be a user-provided service instance
• Router is configured with and forwards requests to the URL
contained in the route_service_url field
• The route service is expected to forward the request back to the
route
• Knowing the request has already been forwarded to the route
service, the Router forwards to the associated applications
Route Services
client
load
balancer
CF router
CF app
route
service
1
2
3
4
5
6

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49. Data Flow Developer Experience
dataflow:> module register --name uppercase --type processor --coordinates group:artifact:version
dataflow:> stream create demo --definition
"http --server.port=9000 | uppercase | file --directory=/tmp/devnexus"
1: Implement Spring Cloud Stream Microservice App:
2: Build and Install:
$ mvn clean install
3: Register Module with Data Flow:
4: Define Stream via DSL:
@EnableBinding(Processor.class)
public class UpperCase {
@Transformer(inputChannel = Processor.INPUT, outputChannel=Processor.OUTPUT)
public String process(String message) {
return message.toUpperCase();
}
}

50. Wire Tap
dataflow:> stream create tap --definition ":demo.http > counter --store=redis"
dataflow:> stream create demo --definition
"http --server.port=9000 | uppercase | file --directory=/tmp/devnexus"

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Orchestrate Composable Data Microservices
HOS
T
Spring Cloud
Stream Modules
Cloud Foundry
Spring Cloud Data Flow
YARN X

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Concourse CI

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• Software product that is always ready to ship
• Always current visibility into product and requirements status
• 100% focus on development, no effort wasted below the value line
• Ongoing assessment of priorities and resource allocation to
optimize for highest impact

56. Cloud Native Enterprise Culture?