Best Practices in Cloud Optimization Lessons learned from 450 AWS cloud deployments

Cloud Computing Meetup, Silicon Valley April 2013

About me

• Co-Founder & CEO, Cloudyn™

• Sr. Principal, Cloud BU, CA Technologies

• Sr. Director, Products, Oblicore (Acquired by CA)

Cloud Economics

@cloudyn_buzz

Cloudyn.com | blog.cloudyn.com

sharon@cloudyn.com

New clouds, old challenges

Dynamic environments result in over-

provisioning, wasted resources and

budget violations.

• SaaS-based, non-intrusive

• Cloud analytics & predictive insights

• Sizing, location & pricing optimization

• Actionable recommendations

• Cloud Orchestration integration

About Cloudyn™

Cloudyn analyzes, diagnoses and

optimizes cloud deployments.

• Usage: Who is using what, where and when?

• Performance: What is the utilization rate?

• Cost: How much does it cost us?

• Life-cycle: What has been changed and when?

• Business metrics: How is it related to our business activities?

Effective deployments require consistent monitoring

What should be monitored?

• Usage: Can we retire or reuse existing resources?

• Performance: Can we size resources better (up or down)?

• Cost: Can we pay less for each compute unit we use?

Effective deployment optimization

What can be optimized?

How can we find optimization opportunities?

Bringing real cloud usage data from 450 AWS cloud customers into the mix:

~2.5m Virtual instances, thousands of databases and billions of storage objects monitored in the survey.

Yearly Spend % of customers +1M 4%

500K-1M 2%

100K-500K 22%

50K-100K 11%

50K 61%

Usage trend : Storage

Surprise. You have storage (S3, EBS)

• Typically represents 14% of the cloud spend.

• Only 12% is using cheaper storage (Glacier) options

Usage optimization : S3 / Glacier

• Object Size best practice:

• Store large objects on Glacier (40K overhead / Obj)

• Object pricing best practice:

• Store long term (+3m) objects on Glacier

• Penalty for early deletion!

• Daily backups best practice:

• Keep on standard storage for 1 week

• Move to Glacier afterwards

• Using S3 versioned buckets?

• Nearly 10% of them have hidden objects

Usage optimization: EBS

Bad habits are hard to break…

Does it make sense to keep the light on when

you leave the room? Why do that to your EBS

Volumes?

• 16% of EBS volumes are unattached and subject

to deletion or change (S3, Glacier)

• In some cases (0.5% of EBS), EBS volumes

reported as attached but are not connected at all.

Usage trends : Compute / Database

One m1.large cappuccino with

extra espresso shot please… Coffee customization, Starbucks @ AWS Re:Invent

If you do it for your coffee, why not treat

your instances the same? It’s 20% of your

monthly bill.

Usage trend : Compute

Most instances are significantly

underutilized. • Average yearly CPU utilization of 17%

• Max RAM utilization of 64%

• As instance size increase, utilization decreases

By looking at CPU, Memory, I/O, Network:

Size % of Spend CPU Util. m1.large 27.5% 9%

m2.4xlarge 17.5% 6%

c1.xlarge 7.7% 9%

m1.xlarge 9.9% 14%

Optimization example: Compute

Comparing m1. large to m1.xlarge for RDBMS:

Spec m1.large m1.xlarge RAM 7.5Gib 15 Gib

CPU 4 EC2 CU 8 EC2 CU

Storage 850 GB 1690 GB

I/O Perf Moderate High

• m1.large EBS-optimized + 500 Mbps provisioned IOPS performed better than single m1.xlarge

Pricing Optimization

Cloud vendors love

charging less…

Yep, this is not a typo, and you don’t

really leverage it.

Price optimization

Why they love charging you less?

Goal: Fast ROI, low cost per compute

unit using reserved capacity (AKA RIs).

• Capacity planning

• Customer satisfaction

• The Jevons paradox

• The upfront payment

Pricing Trend – Reserved, On-Demand, Spot

RIs - known and unknown facts:

93% of the on-demand instances

should be reserved.

• Requires one time payment

• Resource availability is guaranteed

• Pay less per hour

• 71% of instances run on-demand, 26% run reserved

Common mistake – breakeven point and commitment point

RI’s breakeven point and commitment

are not the same.

• Breakeven point :

• The point you receive a return on your upfront payment

and start to save on compute hours

• Commitment :

• The cloud vendor’s commitment for resource availability

• Saving :

• End of year On-Demand <MINUS >Reserved Instance cost

Breakeven point best practice

Breakeven after 2.5mon, 30% Runtime

Savings

M1.large Linux instance in Virginia for 1 year

Common RI mistake

Safe RI Purchasing

Optimal RI Purchasing

Unused Reservation and Marketplace

Reuse / Recycle what you don’t need.

• 31% of Reservation are unused:

• Relocate On-demand Instances

• Sell on the marketplace

• Note:

• On demand prices drop every quarter

• Reserved instances drop every year

• You always sell at your original purchase price!

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