Learn from renowned author of “Cloud Capacity Management,” Navin Sabharwal (HCL Technologies) about the unique challenges of planning for capacity in hybrid cloud and virtualized environments. He reveals the capacity planning tools and processes needed to successfully plan for and predict the most cost-effective and reliable infrastructure needed in today’s cloud environments. For more information on DevOps solutions from CA Technologies, please visit: http://bit.ly/1wbjjqX

1. ca Opscenter
Case Study: HCL Technologies On Capacity Planning
for Cloud and Virtualized Environments
Navin Sabharwa
HCL Technologies
Practice Head Public Cloud, Cloud Mgmt, Automation, Analytics, DevOps
OCT60S #CAWorld

2. 3
Objective
o The goal of the Capacity Management
process is to ensure that cost-justifiable
IT capacity in all areas of IT
always exists and is matched to the
current and future agreed needs of
the business, in a timely manner.

3. 4
Traditional Capacity Model Concerns
 A pessimistic approach as there was focus on providing highest possible unit of capacity to
support applications to run desirably in peak hours. In off peak hours, procured resources sat
idle and were underutilized.
 On the other hand, constrained resources would lead to overutilization of available resources
leading to performance issues.
 There was a lack of balance between demand and capacity because capacity requirements
did not flow from business level to service level and then to component level.
 This was a short term approach (incident based) with focus on component capacity.
 A lack of proper planning resulted from an absence of an inter-process relationship for
capacity planning and forecasting.

4. 5
New Age/Cloud Capacity Solution
 The focus is on providing the smallest possible unit of capacity to support an application.
 The smallest possible unit for capacity has reduced from a complete Hardware Stack to a
Flexible Virtual Server which can be provisioned and de-commissioned based on need.
 Virtualization in cloud computing allows for workload migration and optimum capacity
utilization.
 Cloud computing provides scalable infrastructure which can be provisioned in minutes as
compared to weeks in traditional environments.
 Ensure services meet their performance targets with cost economies and flexibility to the
consumer to scale capacity.

5. 6
Delivery Process Interdependencies

6. 7
Support Process Interdependencies

7. 8
Iterative Capacity Management
Ongoing/Iterative capacity management procedures are required by service providers when existing service
needs to be implemented and optimized for business and performance.

8. 9
Capacity Management Meeting Demand
Capacity must be able to intelligently tune itself according to criticalities that may arise due to business dynamics, seasonal and irregular variations.

9. Capacity Aware Provisioning
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10. Capacity Demand Coupling
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 The ability of the cloud provider to anticipate the rise and fall in demand is the key
to being a successful cloud provider.
 In case of under capacity, the cloud consumers will not be able to provision
resources or the performance SLAs will suffer; resulting in customer dissatisfaction
and financial loss.

11. Resource Reclamation Process
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 This process identifies workload inefficiencies and validates the same.
 Identified underutilized resources are reclaimed.
 Environment is monitored for further resource and cost optimization.

12. Modeling and Forecasting
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13. Capacity Forecasting
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In optimistic capacity forecasting, we would reserve more capacity for redundancy and other factors.

14. Application Performance Simulation
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15. Workloads
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16. Hourly CPU Load
Optimized by time of day
17
14
12
10
8
6
4
2
0
25% Savings
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Load
Hour

17. Monthly Optimization
Optimized during a month
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RDS DB Servers
75% Savings Daily CPU Load
1 3 5 7 9 11 13 15 17 19 21 23
Days of the Month

18. Worst Case Scenario – AWS CloudFront
 http://www.reviewmylife.co.uk/blog/2011/05/19/amazon-cloudfront-and-s3-
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maximum-cost/
 Author calculated maximum possible charge:
– Used default limit of 1000 requests per second and 1000 megabits per second
– At the end of 30 days a maximum of 324TB of data could have been downloaded
(theoretically)
– $42,000 per month for a single edge location
– CloudFront has 30 edge locations

19. Stories And Lessons Learned
 Anecdotal user experience
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– Personal website hacked by file sharers
– Received bill for $10,000
 Note: AWS only charges for data out
– All data transfer in is at $0.000 per GB
– Mitigates costs – if you don’t respond to requests, it doesn’t cost you anything

20. CA Capacity Management
Increase Efficiency, Assure Delivery and Reduce Costs With Confidence
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DECISION SUPPORT FOR IT INVESTMENTS
Model growth
Assess capacity efficiency
across IT
Identify utilization impact
to business services
PREDICTIVE ANALYTICS
Anticipate potential issues before they impact the
customer experience
Strategic Business
Value

21. What is CCM in real-life?
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22. Looking Closer…Increased Visibility
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 Capacity across IT
 Capacity across facilities
 Unified view

23. Looking Closer…Increased Visibility
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 Capacity across IT
 Capacity across facilities
 Unified view
Monitor breaker used by VM clusters
View IT Utilization on same clusters
Alarm = power exceeds capacity rating
Power issues visible before they present a problem

24. Looking Closer…Increased Visibility
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 Capacity across IT
 Capacity across facilities
 Unified view
Indication of productive and
unproductive power usage
Unproductive power = potential to
reduce IT Equipment
Reduced IT equipment = increased
efficiency & less power

25. Looking Closer…Improved Provisioning Capabilities
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 Capacity analysis - What
 ‘What-if’ analysis - Where
 Deployment - How

26. Looking Closer…Reduced CapEx and OpEx
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 Optimization of software
 Optimization of hardware
 Risk mitigation

27. For More Information
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To learn more about DevOps, please visit:
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28. For Informational Purposes Only
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