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Optimizing Costs and Efficiency of AWS Services

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This session is a deep dive into techniques used by successful customers who optimized their use of AWS. Learn tricks and hear tips you can implement right away to reduce waste, choose the most efficient instance, and fine-tune your spending; often with improved performance and a better end-customer experience. We showcase innovative approaches and demonstrate easily applicable methods to save you time and money with Amazon EC2, Amazon S3, and a host of other services.

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Optimizing Costs and Efficiency of AWS Services

  1. 1. ©2015,  Amazon  Web  Services,  Inc.  or  its  affiliates.  All  rights  reserved Cost Optimization Tom Johnston Cloud Economics Team
  2. 2. Do you • Wear green eye-shades? • Work from the console?
  3. 3. You’re using AWS. You like it.
  4. 4. But maybe you are spending more than you planned
  5. 5. Or you’d just like to spend less
  6. 6. What should you do?
  7. 7. How about some frameworks
  8. 8. Some tools
  9. 9. Some best practices
  10. 10. And some examples from other customers like you
  11. 11. How are you controlling the provisioning of resources?
  12. 12. Control who can provision resources
  13. 13. Require tagging
  14. 14. “No tags? No instance” – Large financial services customer in Boston
  15. 15. Best practices: • Control who can provision resources via IAM • Require tagging – Inspect – Stop instances without tags
  16. 16. Savings potential 100% if you don’t start instances you don’t need!
  17. 17. Are you identifying and terminating idle resources?
  18. 18. How do I identify idle resources?
  19. 19. AWS Trusted Advisor runs 100+ configuration checks and recommends savings
  20. 20. We are using AWS Trusted Advisor to improve cost efficiency and to audit the configurations of service platforms
  21. 21. Trusted Advisor
  22. 22. How do I handle demand spikes?
  23. 23. Design for elasticity rather than deploy for peak
  24. 24. Use Auto Scaling or queue-based approaches to add resources when needed, and turn them off when not Amazon SQS Queue Auto Scaling
  25. 25. Problem: Workloads arrive asynchronously and require isolation Solution: Amazon Simple Queue Service and launch/ termination of instances Amazon SQS
  26. 26. Problem: How to scale up to accommodate sudden traffic spikes? Solution: Auto ScalingAuto Scaling
  27. 27. Auto Scaling real world example •  Baseline traffic: 300+ thousand pages / hour 130 TB/month outbound traffic •  Peak: 2.0+ million pages / hour (~7x baseline) •  Architectural changes: e.g., move from local SSDs to provisioned IOPS needed due to this scale •  Auto Scaling provides 23% savings over three years
  28. 28. Best practices •  Identify idle resources using Trusted Advisor •  Turn them off •  Design to scale up and scale down using Auto Scaling or a queue-based approach •  Match your usage and capacity—don’t pay for idle resources!
  29. 29. Are you using the most cost-effective instances or resources?
  30. 30. Match resources to workload
  31. 31. How can I tell if there’s a match or mismatch?
  32. 32. Use Amazon CloudWatch to collect and track metrics CloudWatch
  33. 33. Amazon CloudWatch monitoring Basic •  7 metrics for Amazon EC2 including: –  CPU utilization –  Data transfer –  Disk usage and more •  5-minute frequency •  Metrics for Amazon EBS, Amazon DynamoDB, Amazon RDS, etc. Detailed •  1-minute frequency •  Aggregation by instance type and AMI
  34. 34. Why does this matter?
  35. 35. Different instances cost different amounts
  36. 36. Instance families: Example Instance vCPU Mem (GiB) Monthly price (3-yr heavy RI ) Ideal use case c3.2xlarge 8 15 $121.22 Best price-compute performance m3.2xlarge 8 30 $161.15 Balanced r3.2xlarge 8 61 $189.66 Lowest cost per GiB RAM r3.xlarge 4 30.5 $94.83 Lowest cost per GiB RAM
  37. 37. Do you need continuous compute or does bursting work for you?
  38. 38. t2.medium •  4 GiB RAM •  Baseline performance: 40% •  Bursts beyond this based on CPU credits •  Less than $18 per month •  Details: http://amzn.to/1sl2bKa •  Web servers, dev, small databases t2
  39. 39. t2 vs m3.medium or c3.large Instance vCPU Mem (GiB) Monthly price (3-yr heavy RI ) Ideal use case m3.medium 1 3.75 $20.16 Always available, balanced c3.large 2 3.75 $29.95 Always available, compute t2.medium 2 4 $17.87 Bursty workloads
  40. 40. Demo t2 CloudWatch
  41. 41. Consider switching to a t2?
  42. 42. Guidance •  Inspect your workloads (use CloudWatch!) •  Can your workload run on a t2? •  If not, is it memory intensive? àr3 •  Is it compute intensive? à c3 •  Others to consider: –  i2 for storage-optimized –  g2 for GPU
  43. 43. Savings potential If t2 works for you •  11%: Switch from m3.medium to t2.medium •  40%: Switch from c3.large to t2.medium Instance optimization •  25%: Switch to c3 for compute-intensive apps (m3.2xl -> c3.2xl) •  41%: Switch to r3 for memory-intensive apps (m3.2xl -> r3.xlarge)
  44. 44. Best practices: 1. Monitor instances with CloudWatch 2. Switch to t2 as possible 3. Use compute- and memory-focused instances as needed
  45. 45. Do you need long- term storage?
  46. 46. Amazon Glacier Extremely low-cost cloud storage service Amazon Glacier
  47. 47. Are you purchasing in the most cost- effective way?
  48. 48. Commitment and spot save $
  49. 49. Use Reserved Instances if possible: Up to 60% savings + capacity reservation
  50. 50. AWS Trusted Advisor identifies savings attainable via Reserved Instancess
  51. 51. Reserved Instance Marketplace
  52. 52. Use Spot Instances for non-stateful workloads Pricing starts at 90% off
  53. 53. Novartis, Cycle Computing & AWS Spot Instances
  54. 54. Goal: Screen 10 million compounds against a common cancer target in < 1 week
  55. 55. 10,600 AWS instances
  56. 56. 39 drug design years in 11 hours
  57. 57. 3 promising compounds identified
  58. 58. Cost of $4,232
  59. 59. Savings potential • 40%: 1-Year Reserved Instances • 60%: 3-Year Reserved Instances • Up to 90%: Spot Instances
  60. 60. Could you save by letting AWS do more work for you?
  61. 61. Focus on what you do best
  62. 62. Let AWS solve other problems for you
  63. 63. AWS’s higher- level services automate your work and save you money CloudFront DynamoDB Amazon RDS ElastiCache Amazon Redshift Amazon EMR Amazon Kinesis Amazon WorkSpaces
  64. 64. Are you modeling and monitoring your spend?
  65. 65. https:// calculator.s3.amazonaws.com/ index.html
  66. 66. Detailed billing reports and cost insights
  67. 67. Detailed Billing Reports http://amzn.to/1swNwLV
  68. 68. Cost Explorer http://amzn.to/1zHE2Fj
  69. 69. Demo CloudWatch
  70. 70. •  Control and verify •  Design for elasticity •  Match resources and workload •  Purchase for savings •  Use application services •  Model and track Frameworks to remember
  71. 71. Tools to use •  IAM, AWS console •  Auto Scaling, Amazon SQS •  Amazon CloudWatch •  AWS Trusted Advisor •  Detailed billing reports and Cost Explorer •  Partners
  72. 72. •  Turn off untagged resources •  Stop idle instances •  Use instances matched to task and less expensive ones •  Baseload with RIs •  Use Spot Instances for non- stateful workloads •  Model and track spending Best practices
  73. 73. SAN FRANCISCO
  74. 74. ©2015,  Amazon  Web  Services,  Inc.  or  its  affiliates.  All  rights  reserved Cost Optimization Tom Johnston Cloud Economics
  75. 75. SAN FRANCISCO ©2015,  Amazon  Web  Services,  Inc.  or  its  affiliates.  All  rights  reserved Enterprise Pre-Day
  76. 76. Trusted Advisor
  77. 77. m3.medium
  78. 78. t2.medium (bursting)
  79. 79. Cloudwatch: CPU Utilization

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