When your datacenter starts reaching capacity, before building the next datacenter look to cloud computing for options.

1. Outgrowing your Data Center?
Look to the Cloud for Relief

2. About the Presenter
@madgreek65
mikekavis
madgreek65
VP/Principal Architect @ Cloud Technology Partners
Mike Kavis
Agile Development
madgreek65

3. Data Center Trends

4. Exponential Growth
 Data center traffic will grow 4X by
2016 by 6.6 ZB annually
 Majority of traffic is caused by
datacenter and cloud workloads
invisible to the end users
Source: Cisco Global Cloud Index: 2011-2016

5. Exponential Growth
 Growth drives demand in physical
infrastructure
 Datacenters often growing at rates
much faster than originally
estimated
 Driving demand for disk, servers,
network infrastructure, bandwidth,
cooling, floor space, etc.
Source: Cisco Global Cloud Index: 2011-2016

6. Exponential Growth
Source: http://irfansalam.wordpress.com/2012/08/21/zetabyte-era/

7. Green & Carbon Initiatives
 Large infrastructure investments to
get cleaner and greener
 Apple, Google, Amazon, eBay are
investing
 Should companies whose core
competency is not building
datacenters be spending here?
Proposed datacenter from Google & Duke energy

8. Disaster Recovery Investments

9. Disaster Recovery Investments

10. Cost Reductions and Speed to
Market

11. Cost Reductions and Speed to
Market

12. Common Cloud Use Cases

13. Data Storage & Archiving

14. Data Storage & Archiving
Backup infrastructure is expensive and is not elastic

15. Data Storage & Archiving

16. Cloud Bursting

17. Cloud Bursting

18. Cloud Bursting
Bursting Strategies
-Round Robin
-All Peak Loads
-Platinum Customers
-Packet Size Oriented
-API Oriented
-SLA Oriented

19. Big Data
Data
Sources
Extract
System
Extract
System
Transform
System
Transform
System
Load
System
Load
System
Data
Warehouse
Data
Warehouse
Enterprise ETL Design*
Extract
System
Extract
System
Transform
Function
Transform
Function
Load
Function
Load
Function
* three distinct systems
Hadoop
Big Data ETL Design**
* * two (or one?) distinct systems

20. Big Data
Data
Sources
Extract
System
Extract
System
Transform
System
Transform
System
Load
System
Load
System
Data
Warehouse
Data
Warehouse
Enterprise ETL Design
Extract
System
Extract
System
Transform
Function
Transform
Function
Load
Function
Load
Function
Hadoop
Big Data ETL Design
Private
DC/Cloud
Private
DC/Cloud
Public
Cloud
Public
Cloud
Public or
Private Cloud
Public or
Private Cloud

21. Disaster Recovery
RTO (Recovery Time Objective)
“Time to be back up & running”
RPO (Recovery Point Objective)
“Maximum time in which data is lost”
Value
“How much money is recovery worth?”

22. Disaster Recovery Strategies
The “Gang of Three”

23. Disaster Recovery Strategies

24. Disaster Recovery Strategies

25. Disaster Recovery Strategies

26. Disaster Recovery Strategies

27. Dev & QA Environments
Dev QA Stage Production
One of the biggest time wasters is dealing with inconsistent target environments
“It worked on my laptop!”

28. Dev & QA Environments
Standard Environments
- Devs get consistent, patched, secure
images in all environments
Create self-service provisioning
- Stop being the bottleneck and enable the
development teams
Automate everything
-Application Deployments
-Environment Provisioning
-Test Data Provisioning
-Acceptance Testing

29. Summary
Before building or expanding the next datacenter
- Look to the cloud for hybrid use cases
Common Patterns
-File Storage and Archiving
-Cloud Bursting
-Big Data
-Disaster Recovery
-Dev & QA Environments
Cloud Technology Partners can help
www.cloudtp.com