How do you think about computing resources in a world where you can launch and terminate computational capacity in minutes? Amazon EC2 provides a powerful platform to access vast computational resources at the click of a button or a simple API call. It is also very different from operating your own data center or having to managed fix assets in a co-location facility. This talk walks you through examples of how the cloud enables more efficient capacity planning, provides guidance in how developers and organizations can manage thousands of instances efficiently, and highlights tools that make it easy for you to plan your capacity needs, even when those needs might require you to provision the equivalent of a small data center at short notice.

1. My Datacenter Has Walls That Move
Deepak Singh
Principal Product Manager - Amazon EC2
#reinvent

3. http://www.wired.com/wiredenterprise/2011/12/nonexistent-supercomputer/

5. Image: Windsordi

6. Image: wdr3

7. NewCo

8. Small biotech

9. New business unit
Small biotech

10. Image: EMSL

13. Algorithms

14. Algorithms
Bioinformatics

15. Algorithms
Bioinformatics
Scientific Analysis

16. More servers?

17. Space?

18. Power?

19. Risk

20. Capital

22. Big project

23. Good problem to have?

24. Image: Joriel Jimenez

26. Constraints

29. CPU
Memory
Disk
Networking
Power
Cooling

30. Credit: Pieter Musterd

31. Remove constraints
Credit: Pieter Musterd

32. Embrace change

34. Not constrained by ...

35. Space

36. Power & Cooling

37. Capital

38. Agents of change

39. 1. On Demand computing

40. For when you need
For whom you need

46. 2. Elasticity

51. Rinse, Repeat

53. 3. Flexibility

54. Choose the right instance type

55. For when you need
For whom you need
For what you need

56. Most Apps
Low Cost
M1 Standard
M3 Standard Most Apps
Fast
High CPU
Scale-out
Compute
Memory-intensive
High Memory Apps
Compute + Network Througput
Micro Inexpensive
Cluster Compute Memory-intensive
Cluster Computing
Cluster High Memory
Lots of low-latency Cluster GPU Parallel GPGPU Computing
IOPS
High I/O Petabyte Scale
Lots of Throughput
High Storage

57. Choose the right price

58. For when you need
For whom you need
For what you need
For how much you need

59. Reserved Instances

60. Light Utilization
Medium Utilization
Heavy Utilization

63. Spot Instances

64. 100%
On-demand
Reserved capacity

66. What is the value of my data?

67. How much am I willing to pay
for a compute cycle?

68. Ask yourself the
right questions

69. NewCo

70. What is the baseline for
each team?
Image: Mad African

71. What is the length of a
typical project?
Image: Peter Morville

72. Are we using the right
instance type(s)?

73. Can we leverage Spot
Instances?

74. Adapt over time

77. Remove constraints
Credit: Pieter Musterd

79. Making it real

80. Source: Adrian Cockroft (Netflix)

81. Multiple applications

82. Different utilization

83. Source: Adrian Cockroft (Netflix)

84. Spiky

85. Long term forecast?

86. Not easy

87. Find the right
instance type(s)

88. c1?
m3?
cc2?

89. Cover your baseline

90. Light Utilization RI

91. On Demand
+
Spot Instances

92. Source: Adrian Cockroft (Netflix)

93. Daily usage

94. Occasional spikes

95. Elastic MapReduce
+
Reserved Instances
+
Spot Instances

96. Source: Adrian Cockroft (Netflix)

97. Growth over time

98. Planning horizon

99. Months

100. Weeks

101. Days

102. Less risk

103. Accurate forecasts

104. Better
Accurate forecasts

105. Global infrastructure

106. 9 regions
25 availability zones
38 edge locations

107. International expansion

110. Disaster recovery

111. Geographic replication

112. Hot spare

114. In Conclusion

115. Think beyond the data center

116. What you need

117. When you need

118. Where you need

120. We are sincerely eager to
hear your feedback on this
presentation and on re:Invent.
Please fill out an evaluation
form when you have a
chance.