Post on 28-Jan-2015
description
transcript
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
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!