the three stages of cloud adoption - rightscale compute 2013

© 2011 Forrester Research, Inc. Reproduction Prohibited1 © 2009 Forrester Research, Inc. Reproduction Prohibited

The Three Stages of Cloud Adoption

James Staten, Vice President, Principal Analyst

© 2011 Forrester Research, Inc. Reproduction Prohibited2

Scale out turns on the cloud.

Scaling down turns on cloud economics.


The basics of cloud economics are clear

• As demand rises, resources are added.• Requires apps to scale out• Load balancing governs

Elastic scale delivers just-in-time capacity.

• No upfront payment• No commitment whether you use it or not• Costs aligned to use

Pay-per-use keeps costs low.

• If you can build it, you can deploy it — fast.• API access drives use of automation.

Self-service fuels productivity.


Turning cloud economics to your favor is the key

• How quickly can you turn off resources no longer in use?• How small can you get your base footprint?• Are you leveraging caching as much as you can?

Elastic scale: Scaling down is key.

• Discrete component scaling drives efficiency.• Autoscaling cloud services often costs less.• Where traffic goes affects cost.

What you scale matters.

• Some cloud services have off-peak pricing concepts.• Some cloud services have better costs for certain services/uses.

When and where you do things matters.


Three stages of cloud economics


Leverage the tools of cloud economics

• When you do something• For how long you do itTime

• Parts that act independentlyComponentization

• What performs which actions• What economics it bringsService choice

• What doesn’t consume storage• What doesn’t consume bandwidthCaching

• How the code behaves• What the code does and how• Re-architect to activate cloud economics

Code optimization


How does Netflix use the cloud economics tools?

Profitability up by cloud optimization

Three key services

– Movie encoding (movies/watt)

Batch up encoding processes.

Not time sensitive; can tap spot instances.

Optimize storage formats to keep S3 costs low.

– Video streaming service

Store on S3, stream on multiple CDNs.

– Optimize CDN choice by stream type and customer.

Split customer login and device identification.

– Log analysis — feeds BI system

Leverage managed Hadoop from AWS.

Re-architected BI from the ground up for best

cloud efficiency

Key tools used:

Time

Componentization

Service choice

Code optimization

For more info, check out http://www.slideshare.net/adrianco/netflix-on-cloud-combined-slides-for-dev-and-ops and

Cloudscaling Blog (http://cloudscaling.com/blog/cloud-computing/cloud-innovators-netflix-strategy-reflects-google-

philosophy).


NVoicePay taps the cloud to expand its market

AP Assist: Enable electronic payments for SMBs

90% of B2B invoice payments are still by check.

Solution: SaaS-based vendor payment

– $0.40 per payment; free vendor sign-up

– Tight integration with key SMB systems (i.e., ADP for car

dealers, First Data payment clearing-house)

Rich but light client UI — Silverlight

Needed to push new information to customers quickly

– Needed elastic scalability, fast deploy, and iteration

Forecasted huge transaction volumes

Traditional hosting was more expensive — wouldn’t achieve scale.

– Needed reusable cloud services to speed development

Windows Azure AppFabric, storage, security services

– Needed effectively 100% availability for “Payment Dial Tone”

The alternative was to build a massively expensive data center.

“Building a mission-critical B2B

payment network would have been

nearly impossible without the low

cost, pay-as-you-go . . . Windows

Azure platform.”

— Karla Friede, CEO, NVoicePay

For more info, check out NVoicePay website (http://nvoicepay.com/wp-content/uploads/2010/10/NVoicePay-MS-Case-Study.pdf).


How is NVoicePay leveraging cloud economics?

What cloud economics tools are they using?

– Time

– Componentization

– Service choice

– Caching

– Code optimization



How NVoicePay activates cloud economics

If it’s running, it’s cutting into profitability.

Profits are pennies on each $0.40 transaction.

Mission: Lower the cost of each payment.

– Scale only when necessary.

Scale only what needs to be scaled when necessary.

– Chatter can cost you.

Traffic flowing out of the cloud costs $$.

Keep this traffic to a minimum using:

– Caching, Batching, Rich-client actions.

– Use cloud services to speed development.

Windows Azure AppFabric Service Bus and Access Control

Windows Azure Blob, Table, and Queue Storage

Windows Azure Connect — secure extra cloud connectivity

Silverlight client takes actions that ease cloud-premise

integration.

Key tools used:

Time

Componentization

Service choice

Caching



What code are we seeing in the cloud

1: Forklift 2: Elastic 3: Native

Redeploy Apps “As-

is” to the cloud

IAAS: EC2

PAAS: CloudBees,

Engineyard

• Simple

• Quick

• Low- initial cost

• Low elasticity

Load-balance, add

OSS infrastructure

IAAS: EC2

PAAS: Azure

• Let provider

auto-scale

• Some

refactoring

• OPEX savings

Designed with the

Internet as the OS

Service composition

• Facades

• State migrates to

edge of systems

• Designed for failure,

self healing

• Continuous

deployment

~70% ~20% ~10%


Why traditional applications struggle to run on true clouds

Cloud Platforms

Commodity, low HA HW

Assume all resources will be

shared

Assume shared use of the network

and no performance SLA

Can be run anywhere

And instance can take over

Are services to other elements

Traditional enterprise

applications

Assume stable, reliable HW

Assume sole ownership of the

resource

Assume uninterrupted network

access

Assume place

Assume they are primary

Have static interrelationships


App architectures must evolve to exploit scale out

November 2012 “Don’t Move Your Apps To The Cloud ”


A Digest of Evernote Architecture

150M HTTP requests via Apache

Thrift based Service API

Multiple failover points

90+ shards of Debian/Java/

Tomcat/Hibrnate/Ehcache/

Stripes/GWT/MySQL

VM images replicated to another

server

User based sharding reduces noisy

neighbor

Imaging search indexing service is

separated into different endpoint

Additional endpoint each have their

own scale

Cloud Native Elements


What powers Instagram

0-14M users in year 1

Ubuntu running on EC2

AWS Elastic Load Balancer + Route 53 +3 NGINX instances

Django(Python)+gunicorn for App servers

Sharded PostgreSQL for Data Storage

Photos stored to AWS S3

Cloudfront as CDN for acceleration

Redis for feeds, Memcache

Munin for monitoring



What powers Linked-in Mobile

Node.js for high scale

asynchronous eventing to clients

Mobile server intermediates

between client and LinkedIn

platform

Screen API (JSON) – Domain API

(Thrift)

Nginx for higher throughput of

messages

Cloud Native Elements iOS

JS/HTML + Native

Android

Native

Mobile Web

JS/HTML

Other

Wrap JS/HTML

Load Balancer

Nginx Nginx

Node JS

Server

Node JS

Server

LinkedIn Core Platform

Mongo DB

Logging Server

Tracking Server

Screen-based REST API

Domain REST APIM

ob

ile S

erv

er


The NPR API architecture

43M pageviews in 2010 to 88M

– 30M mobile

Following a COPE strategy

Appservers, Java, JSP, Struts

MySQL as data management

layer

NoSQL XML repo for API

staging

PHP 5 /Linux in the API

handling layer

Memcache to accelreate

reponses



What about legacy apps?


Cloud APIs

Mobile U

I

SOA enhancements SaaS in

tegrations

Link to the legacy, then cut it loose

Web infrastructure

Legacy


Recommendations

• How does it generate revenue?• What actions are taken to get that revenue?• What components are involved?• What does the transaction flow cost?

Understand the profitability profile of your cloud service.

• Can you improve its scalability model?• How can its action timing be improved?• What alternative means (services, caching, reusable

components) can be leveraged?

Analyze its performance profile.

• Improve cloud coding practices through architectural adherence.• Architect in how to use the cloud economic tools — not the

specific uses.• Revisit this process often — as the cloud matures quickly.

Bake the lessons learned into your cloud architecture.


What’s your cloud use case?

What defines success for this service?

What drives its profitability?

What cloud economics tools are you using today?

Discussion: How can you use them more

efficiently tomorrow? Cloud tools:

Time

Componentization

Service choice

Caching

Code optimization


© 2009 Forrester Research, Inc. Reproduction Prohibited

Thank you

James Staten+1 650.581.3824

Twitter: Staten7

www.forrester.com

the three stages of cloud adoption - rightscale compute 2013

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