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Best Practices for AWS Cloud Cost Optimization

Best Practices for AWS Cloud Cost Optimization



Cloudyn CEO, Sharon Wagner's presentation at the Silicon Valley Cloud Computing group meetup in Mountain View, on April 3, 2013.

Cloudyn CEO, Sharon Wagner's presentation at the Silicon Valley Cloud Computing group meetup in Mountain View, on April 3, 2013.



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    Best Practices for AWS Cloud Cost Optimization Best Practices for AWS Cloud Cost Optimization Presentation Transcript

    • Best Practices in CloudOptimizationLessons 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.
    • About Cloudyn™Cloudyn analyzes, diagnoses andoptimizes cloud deployments.• SaaS-based, non-intrusive• Cloud analytics & predictive insights• Sizing, location & pricing optimization• Actionable recommendations• Cloud Orchestration integration
    • Effective deployments require consistent monitoringWhat should be monitored?• 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 deployment optimizationWhat can be optimized?• 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?
    • 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 : StorageSurprise. 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: EBSBad 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 / DatabaseOne m1.large cappuccino withextra 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 : ComputeBy looking at CPU, Memory, I/O, Network:Most instances are significantlyunderutilized.• Average yearly CPU utilization of 17%• Max RAM utilization of 64%• As instance size increase, utilization decreases 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: ComputeComparing m1. large to m1.xlarge for RDBMS:Spec m1.large m1.xlargeRAM 7.5Gib 15 GibCPU 4 EC2 CU 8 EC2 CUStorage 850 GB 1690 GBI/O Perf Moderate High• m1.large EBS-optimized + 500 Mbps provisioned IOPS performed better than single m1.xlarge
    • Pricing OptimizationCloud vendors lovecharging less… Yep, this is not a typo, and you don’t really leverage it.
    • Price optimizationWhy they love charging you less?• Capacity planning• Customer satisfaction• The Jevons paradox• The upfront payment Goal: Fast ROI, low cost per compute unit using reserved capacity (AKA RIs).
    • Pricing Trend – Reserved, On-Demand, SpotRIs - known and unknown facts:• Requires one time payment• Resource availability is guaranteed• Pay less per hour• 71% of instances run on-demand, 26% run reserved 93% of the on-demand instances should be reserved.
    • Common mistake – breakeven point and commitment pointRI’s breakeven point and commitmentare 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 M1.large Linux instance in Virginia for 1 year Savings Breakeven after 2.5mon, 30% Runtime
    • Common RI mistake Optimal RI Purchasing Safe RI Purchasing
    • Unused Reservation and MarketplaceReuse / 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!
    • www.cloudyn.com