ca Opscenter

Case Study: HCL Technologies On Capacity Planning for Cloud and Virtualized Environments

Navin Sabharwa

OCT60S #CAWorld

HCL Technologies Practice Head Public Cloud, Cloud Mgmt, Automation, Analytics, DevOps

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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.

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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.

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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.

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Delivery Process Interdependencies

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Support Process Interdependencies

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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.

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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.

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Capacity Aware Provisioning

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Capacity Demand Coupling

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.

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Resource Reclamation Process

This process identifies workload inefficiencies and validates the same.

Identified underutilized resources are reclaimed.

Environment is monitored for further resource and cost optimization.

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Modeling and Forecasting

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Capacity Forecasting

In optimistic capacity forecasting, we would reserve more capacity for redundancy and other factors.

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Application Performance Simulation

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Workloads

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Hourly CPU Load Optimized by time of day

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2

4

6

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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

25% Savings

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RD

S D

B S

erv

ers

Days of the Month

1 3 5 7 9 11 13 15 17 19 21 23

75% Savings Daily CPU Load

Monthly Optimization Optimized during a month

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Worst Case Scenario – AWS CloudFront

http://www.reviewmylife.co.uk/blog/2011/05/19/amazon-cloudfront-and-s3-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

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Stories And Lessons Learned

Anecdotal user experience – 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

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CA Capacity Management Increase Efficiency, Assure Delivery and Reduce Costs With Confidence

DECISION SUPPORT FOR IT INVESTMENTS

Model growth Assess capacity efficiency

across IT Identify utilization impact

to business services

PR

EDIC

TIV

E A

NA

LYTI

CS

Anticipate potential issues before they impact the customer experience

Strategic Business Value

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What is CCM in real-life?

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Looking Closer…Increased Visibility

Capacity across IT

Capacity across facilities

Unified view

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Looking Closer…Increased Visibility

Capacity across IT

Capacity across facilities

Unified view

Monitor breaker used by VM clusters

Power issues visible before they present a problem

View IT Utilization on same clusters

Alarm = power exceeds capacity rating

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Looking Closer…Increased Visibility

Capacity across IT

Capacity across facilities

Unified view

Unproductive power = potential to reduce IT Equipment

Reduced IT equipment = increased efficiency & less power

Indication of productive and unproductive power usage

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Looking Closer…Improved Provisioning Capabilities

Capacity analysis - What

‘What-if’ analysis - Where

Deployment - How

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Looking Closer…Reduced CapEx and OpEx

Optimization of software

Optimization of hardware

Risk mitigation

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For More Information

To learn more about DevOps, please visit:

http://bit.ly/1wbjjqX

Insert appropriate screenshot and text overlay from following “More Info Graphics” slide here;

ensure it links to correct page DevOps

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For Informational Purposes Only

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