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AWS EMEA Online Summit - Blending Spot and On-Demand instances to optimizing costs
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Cost and capacity optimization
0 5
Cobus Bernard
Sr Developer Advocate
AWS
@cobusbernard
cobusbernard
cobusbernard
AWS EMEA Online Summit - Blending Spot and On-Demand instances to optimizing costs
AWS global platform
AWS global infrastructure
• 23 Regions with 73 Availability Zones
• 4 Regions coming soon: Indonesia, Italy,
Japan and Spain
216 CloudFront PoPs
• 205 edge locations
• 11 Regional edge caches
• 245 Countries and territories served
AWS global network
• Redundant 100 GbE network
• 100% encrypted between facilities
• Private network capacity between
all AWS Regions except China
SLA of
99.99% availability
Amazon EC2 instance characteristics
M5d.xlarge
Instance family
Instance
generation
Instance size
Instance type
CPU
Memory
Storage
Network performance
Additional
capabilities
Broadest and deepest platform choice
Workloads Capabilities Options
(AWS, Intel, AMD)
(up to 4.0 GHz)
(up to 24 TiB)
(HDD and NVMe)
(up to 100 Gbps)
(GPUs and FPGA)
(Nano to 32xlarge)
+ + =
270+instance types
Continued rapid pace of innovation
Instance growth
270+
Customer obsessed
of the roadmap originates with customer requests
and is designed to meet specific needs
90%
Optimizing Amazon
EC2 cost and capacity
Optimizing Amazon EC2 cost and capacity
We continue to innovate for our customers
Pricing Capacity Guidance
Optimizing Amazon EC2 cost and capacity
We continue to innovate for our customers
Pricing Capacity
Capacity management
made easy on the
broadest and deepest
compute platform
Guidance
Cost and capacity
recommendations
enable ease of use
and save time
the second
Amazon EC2 purchase options
savings of up to 90%a significant discount
more flexibility
To optimize Amazon EC2, combine purchase options
RIs or a Savings Plan
Spot for fault-tolerant,
flexible, stateless workloads
On-Demand
Deutsche Börse Group
… covers the entire value chain in securities and derivatives trading.
• Pre-IPO and listing
• Trading
• Clearing
• Settlement
• Custody
• Collateral and liquidity
management
• Market data
• Indices
• Technology
Other Instance Types can make the difference
0
0.2
0.4
0.6
0.8
1
r5.2xl r5a.2xl r5d.2xl m5.4xl r4.2xl m4.4xl
InstancePrice$/h
eu-central-1
spot price on-demand price
T7 and Cloud Use-Cases – SmokeTest termination rate
1521
2919
4532
3323
2459
20 25 128 29 71
0
1000
2000
3000
4000
5000
2019-Jan 2019-Feb 2019-Mar 2019-Apr 2019-May
Number of SmokeTest Spot Instances and Terminations
SmokeTest all SmokeTest terminated
Introducing Savings Plans
Easy to use
Receive discounted rates
automatically in exchange
for a monetary commitment
Flexible
Make a single commitment that
applies across multiple AWS
compute services, even as
your requirements change
Significant discounts
Select from two types of Savings
Plans to receive discounts of up
to 72% on EC2 Instance Savings Plans
and 66% on Compute Savings Plans
Flexible purchase option that offers up to 72% discounts
on Amazon EC2 and AWS Fargate usage
Types of Savings Plans
Provide the lowest prices, up to 72% off (same
as Standard RIs) on the selected instance family
(e.g., C5 or M5), in a specific AWS Region
Offer the greatest flexibility, up to 66% off
(same prices as Convertible RIs)
Flexible
across
 Instance family: e.g., Move from C5 to M5
 Region: e.g., Change from EU (Ireland) to EU
(London)
 OS: e.g., Windows to Linux
 Tenancy: e.g., Switch Dedicated tenancy to
Default tenancy
 Compute options: e.g., Move from EC2 to
Fargate
Flexible
across
 Size: e.g., Move from m5.xl to m5.4xl
 OS: e.g., Change from m5.xl Windows
to m5.xl Linux
 Tenancy: e.g., Modify m5.xl Dedicated
to m5.xl Default tenancy
Compute
Savings Plans
EC2 Instance
Savings Plans
Comparing RIs and Savings Plans
Savings Plans offer all the benefits of RIs as well as improved flexibility and reduced management
Compute
Savings Plans
EC2 Instance
Savings Plans
Convertible RIs* Standard RIs
Savings over On-Demand Up to 66% Up to 72% Up to 66% Up to 72%
Low price in exchange for
monetary commitment    
Pricing automatically applies
to any instance families    
Pricing automatically applies
to any instance size   ** **
Pricing automatically applies
to any tenancy or OS    
Automatically apply to
Fargate usage    
Pricing automatically applies
across any AWS Region
   
1- and 3-year term
length options
   
* Convertible RIs can be changed across instance families, sizes, OS, and tenancy – they require customers to manually perform exchanges
** Regional Convertible RIs and Regional Standard RIs provide instance size flexibility
Getting started with Savings Plans
Review your Savings
Plans recommendations
in AWS Cost Explorer
Customize
recommendations
based on your needs
(type of Savings Plan,
payment option,
term length)
Review hourly
commitment (e.g., $10/hr)
and add to cart
Eligible Amazon EC2
and AWS Fargate
usage is charged at a
discounted Savings
Plans rate up to your
commitment level
AWS Cost Explorer guides you through the purchasing process
Just like RIs, you can purchase Savings Plans via the RI operations team
Spot, On-Demand capacity reservations, and
Savings Plan together
On-Demand capacity
reservations
Savings Plan
Spot Instances
Cost-effective,
scalable compute
Capacity
Interruptions only
happen if OD
needs capacity
Pricing
Smooth, infrequent
changes, more predictable
Instances
Same infrastructure as
On-Demand and RIs
Usage
Choose different instance
types, sizes, and AZs in
a single fleet or EC2 Auto
Scaling group
Pricing is based on long-term supply and demand trends; no bidding!
Save up to 90% using EC2 Spot Instances
Low, predictable prices
Up to 90% discount over On-Demand prices
Faster results
Increase throughput up to 10x while staying in budget
Easy to use
Launch through AWS services (e.g., Amazon ECS, Amazon EKS,
AWS Batch, Amazon SageMaker, Amazon EMR) or integrated
third parties
Why Spot Instances?
Minimal interruptions
Check for 2-minute interruption notification via instance
metadata or Amazon CloudWatch events, and automate by
 Checkpointing
 Draining from ELB
 Using stop-start and hibernate to restart faster
Interruption handlers for Amazon ECS and Amazon EKS
Amazon Elastic
Kubernetes Service
(Amazon EKS)
 Connection between termination requests from AWS infrastructure to nodes
 Tasks running on Spot Instances will automatically be triggered for shutdown
before the instance terminates, and replacement tasks will be scheduled
elsewhere on the cluster
Amazon Elastic
Container Service
(Amazon ECS)
Handling Spot interruptions
Less than 5% of Spot Instances were interrupted in the last 3 months
Flexibility is key to successful Spot usage
Instance flexible Time flexible Region flexible
B2B Enterprise TechSports, Media & Entertainment Financial Services
Consumer AppsResearch AdTech & MarTech
Customers across different industries and verticals use Spot
Optimizing Amazon EC2 cost and capacity
We continue to innovate for our customers
Pricing
Achieve optimal
price/performance
with different
purchase models
Capacity Guidance
Cost and capacity
recommendations
enable ease of use
and save time
Amazon EC2 Cost Optimisation non-prod
100.0
71.4
35.7
29.8
0
20
40
60
80
100
24 x 7 24 x 5 12 x 5 10 x 5
% Running Time
Up to 70%
savings for non-
production
workloads
AWS Instance Scheduler
• AWS-provided solution
• Custom start & stop schedules
• Works with EC2 & RDS instances
• Deploy using CloudFormation
• Selectively tag instances to schedule
• Multiple schedules per instance
• 5-minute granularity
https://aws.amazon.com/answers/infrastructure-
management/instance-scheduler/
Using Amazon EC2 Auto Scaling
Automatically scale instances across instance families
and purchase options in a single ASG to optimize cost
Capacity-optimized
Prioritize deploying Spot Instances into greater Spot pool capacity
order to lower the chance of interruptions
Lowest cost
Prioritize cost by selecting a mix of On-Demand and Spot Instances
to launch based on the lowest available price
Prioritized list
Use a prioritized list for On-Demand instance types to scale capacity
during an urgent, unpredictable event to optimize performance
Amazon EC2
Auto Scaling
AZ1 and AZ2
Amazon EC2 Spot Instance pools explained
$0.27 $0.29$0.50
1b 1c1a
8XL
$0.30 $0.16$0.214XL
$0.07 $0.08$0.082XL
$0.05 $0.04$0.04XL
$0.01 $0.04$0.01L
C4
$1.76
On
demand
$0.88
$0.44
$0.22
$0.11
Each instance family
Each instance size
Each availability zone
In every Region
Is a separate Spot pool
R5
M4
M5
I3 C5R4
i3en R5a
R5d
ASG capacity-optimized allocation strategy
us-east-1a
Desired capacity: 12
SpotAllocationStrategy: capacity-optimized
OnDemandBaseCapacity: 0 OnDemandPercentageAboveCapacity: 0
r5.large
m4.large
m5.large
R5 R5
R5 R5
us-east-1b us-east-1c
Overrides: [“r5.large”, “m4.large”, ”m5.large”]
$$
$
$$$
r5.large
m4.large
m5.large
$$$
$$
$
r5.large
m4.large
m5.large
$
$$$
$$
ASG lowest-price allocation strategy
us-east-1a
Desired capacity: 12
SpotAllocationStrategy: lowest-price
OnDemandBaseCapacity: 0 OnDemandPercentageAboveCapacity: 0
r5.large
m4.large
m5.large
R5 R5
R5 R5
us-east-1b us-east-1c
Overrides: [“r5.large”, “m4.large”, ”m5.large”]
$$
$
$$$
r5.large
m4.large
m5.large
$$$
$$
$
r5.large
m4.large
m5.large
$
$$$
$$
Instance type overrides and allocation strategies
ASG adjusts to new configuration
as it scales up and down
As ASG scales up
Launch capacity according to the new
configuration
As ASG scales down
Prioritize terminating instances not
matching the new configuration
New termination policy:
AllocationStrategy
Instance type overrides: m4.large, m5.large
m4.large m5.large
Instance type overrides: m5.large, c5.large
m4.large m5.large c5.large
Instance type overrides: m5.large, c5.large
m4.large m5.large c5.large
To optimize Amazon EC2, combine purchase options
RIs or a Savings Plan
Spot for fault-tolerant,
flexible, stateless workloads
On-Demand
Before: Multiple ASGs to use Spot, On-Demand, and RIs together
m4.large Spot ASG Min: 1 Max: 10
m5.large Spot ASG Min: 1 Max: 10
c4.xlarge O-D ASG Min: 1 Max: 10
Availability
Zone 1
Availability
Zone 2
Availability
Zone 3
Before, with
three ASGs
—one for each
instance type/
purchase option
Then: Spot, On-Demand, and RIs in a single ASG
m4.large Spot Instances
m5.large Spot Instances
c4.xlarge On-Demand instances
The new way
combines purchase
options, instance
types, and AZs in
a single ASG
Single ASGAvailability
Zone 1
Availability
Zone 2
Availability
Zone 3
m4.xlarge Spot
Weight of 1
m4.2xlarge Spot
Weight of 2
m4.4xlarge On-Demand
Weight of 4
Availability
Zone 1
Availability
Zone 2
Availability
Zone 3
Different
instance types
contribute
differently to
total capacity
Now: Spot, On-Demand, and RIs in a single ASG with weights
Amazon EC2 Spot Instance pools explained
$0.27 $0.29$0.50
1b 1c1a
8XL
$0.30 $0.16$0.214XL
$0.07 $0.08$0.082XL
$0.05 $0.04$0.04XL
$0.01 $0.04$0.01L
C4
$1.76
On
demand
$0.88
$0.44
$0.22
$0.11
16
Weight
8
4
2
1
$0.11
Weighted
price
$0.11
$0.11
$0.11
$0.11
$0.017 $0.018$0.032
1b 1c1a
$0.038 $0.02$0.032
$0.016 $0.02$0.021
$0.025 $0.04$0.02
$0.01 $0.04$0.01
API parameters
"MixedInstancesPolicy": {
"LaunchTemplate": {
"LaunchTemplateSpecification": {
"LaunchTemplateName": "MyLaunchTemplate"
},
"Overrides": [
{
"InstanceType": "m4.xLarge",
"WeightedCapacity": "1"
},
{
"InstanceType": "m4.2xLarge",
"WeightedCapacity": "2"
},
{
"InstanceType": "m4.4xLarge",
"WeightedCapacity": "4"
}
]
},
"InstancesDistribution": {
"OnDemandAllocationStrategy": "prioritized",
"OnDemandBaseCapacity": 10,
"OnDemandPercentageAboveBaseCapacity": 50,
"SpotAllocationStrategy": “capacity-optimized",
"SpotInstancePools": 2
}
}
AZ1 and AZ2
Desired
Min
Max
On-Demand base
50% On-Demand
50% Spot
Minimum On-Demand (10)
API parameters
"MixedInstancesPolicy": {
"LaunchTemplate": {
"LaunchTemplateSpecification": {
"LaunchTemplateName": "MyLaunchTemplate"
},
"Overrides": [
{
"InstanceType": "m4.xLarge",
"WeightedCapacity": "1"
},
{
"InstanceType": "m4.2xLarge",
"WeightedCapacity": "2"
},
{
"InstanceType": "m4.4xLarge",
"WeightedCapacity": "4"
}
]
},
"InstancesDistribution": {
"OnDemandAllocationStrategy": "prioritized",
"OnDemandBaseCapacity": 10,
"OnDemandPercentageAboveBaseCapacity": 50,
"SpotAllocationStrategy": “capacity-optimized",
"SpotInstancePools": 2
}
}
AZ1 and AZ2
Desired
Min
Max
On-Demand base
50% On-Demand
50% Spot
Minimum On-Demand (10)
API parameters
"MixedInstancesPolicy": {
"LaunchTemplate": {
"LaunchTemplateSpecification": {
"LaunchTemplateName": "MyLaunchTemplate"
},
"Overrides": [
{
"InstanceType": "m4.xLarge",
"WeightedCapacity": "1"
},
{
"InstanceType": "m4.2xLarge",
"WeightedCapacity": "2"
},
{
"InstanceType": "m4.4xLarge",
"WeightedCapacity": "4"
}
]
},
"InstancesDistribution": {
"OnDemandAllocationStrategy": "prioritized",
"OnDemandBaseCapacity": 10,
"OnDemandPercentageAboveBaseCapacity": 50,
"SpotAllocationStrategy": “capacity-optimized",
"SpotInstancePools": 2
}
}
AZ1 and AZ2
Desired
Min
Max
On-Demand base
50% On-Demand
50% Spot
Minimum On-Demand (10)
API parameters
"MixedInstancesPolicy": {
"LaunchTemplate": {
"LaunchTemplateSpecification": {
"LaunchTemplateName": "MyLaunchTemplate"
},
"Overrides": [
{
"InstanceType": "m4.xLarge",
"WeightedCapacity": "1"
},
{
"InstanceType": "m4.2xLarge",
"WeightedCapacity": "2"
},
{
"InstanceType": "m4.4xLarge",
"WeightedCapacity": "4"
}
]
},
"InstancesDistribution": {
"OnDemandAllocationStrategy": "prioritized",
"OnDemandBaseCapacity": 10,
"OnDemandPercentageAboveBaseCapacity": 50,
"SpotAllocationStrategy": “capacity-optimized",
"SpotInstancePools": 2
}
}
AZ1 and AZ2
Desired
Min
Max
On-Demand base
50% On-Demand
50% Spot
Minimum On-Demand (10)
API parameters
"MixedInstancesPolicy": {
"LaunchTemplate": {
"LaunchTemplateSpecification": {
"LaunchTemplateName": "MyLaunchTemplate"
},
"Overrides": [
{
"InstanceType": "m4.xLarge",
"WeightedCapacity": "1"
},
{
"InstanceType": "m4.2xLarge",
"WeightedCapacity": "2"
},
{
"InstanceType": "m4.4xLarge",
"WeightedCapacity": "4"
}
]
},
"InstancesDistribution": {
"OnDemandAllocationStrategy": "prioritized",
"OnDemandBaseCapacity": 10,
"OnDemandPercentageAboveBaseCapacity": 50,
"SpotAllocationStrategy": “capacity-optimized",
"SpotInstancePools": 2
}
}
AZ1 and AZ2
Desired
Min
Max
On-Demand base
50% On-Demand
50% Spot
Minimum On-Demand (10)
API parameters
"MixedInstancesPolicy": {
"LaunchTemplate": {
"LaunchTemplateSpecification": {
"LaunchTemplateName": "MyLaunchTemplate"
},
"Overrides": [
{
"InstanceType": "m4.xLarge",
"WeightedCapacity": "1"
},
{
"InstanceType": "m4.2xLarge",
"WeightedCapacity": "2"
},
{
"InstanceType": "m4.4xLarge",
"WeightedCapacity": "4"
}
]
},
"InstancesDistribution": {
"OnDemandAllocationStrategy": "prioritized",
"OnDemandBaseCapacity": 10,
"OnDemandPercentageAboveBaseCapacity": 50,
"SpotAllocationStrategy": “capacity-optimized",
"SpotInstancePools": 2
}
}
AZ1 and AZ2
Desired
Min
Max
On-Demand base
50% On-Demand
50% Spot
Minimum On-Demand (10)
AWS and third-party integrations with Spot Instances
and EC2 Auto Scaling
Amazon EC2
Auto Scaling
Amazon EC2
fleet
Amazon EMR AWS
CloudFormation
AWS
Batch
AWS Thinkbox
Amazon Elastic
Container Service
Amazon Elastic
Kubernetes Service
AWS
Fargate
Amazon
SageMaker
AWS Elastic
Beanstalk
Optimizing Amazon EC2 cost and capacity
We continue to innovate for our customers
Pricing
Achieve optimal
price/performance
with different
purchase models
Capacity
Capacity management
made easy on the
broadest and deepest
compute platform
Guidance
AWS Compute Optimizer
Recommends optimal instances for Amazon EC2 and Amazon EC2 Auto
Scaling groups from 140+ instances from M, C, R, T, and X families
Applies insights Saves timeLower costs
performance
Simplifying compute optimization
AWS Compute
Optimizer
Identify optimal
AWS compute resources
for your workloads
Mettle scans your AWS
infrastructure and uses
machine learning to
automatically identify
optimal AWS resources
for your workloads
Identifies workload
characteristics and
profile based on the
data gathered
Matches the resource
requirements of your
workloads to optimal
AWS resources with
recommendations
Amazon
CloudWatch
metrics
EC2 Instance
EC2 Auto
Scaling groups
Helps you visualize
what-if scenarios
based on the
recommended
resources
AWS resources
metadata
Easy to choose with AWS Compute Optimizer
New services that recommend optimal AWS compute resources to reduce costs up to 25%
Recommends optimal EC2 instances
Optimizes performance and reduces costs by
making recommendations to help you
right-size compute to your workloads
Analyzes Amazon CloudWatch metrics and
considers Auto Scaling group configuration for
intuitive and actionable recommendations
Up to three recommendations per workload
Available at no additional charge
Workloads
on AWS
Analytics and big data
Databases
DevOps—CI/CD
Enterprise applications
IoT
Machine learning
Storage
Websites and web applications
Machine learning
DevOps—CI/CD
Websites and web applications
Workloads
on AWS
Machine learning (ML)
Get ML solutions to market faster with access to built-in algorithms,
ML frameworks, and custom models
Save up to 90% in training costs
with managed spot training
Automatically manages Spot
capacity on your behalf
All instance types, training models,
and configurations
Amazon SageMaker
managed spot training
Machine learning
DevOps—CI/CD
Websites and web applications
Workloads
on AWS
DevOps—CI/CD
Or, run Jenkins build jobs inside your Kubernetes
clusters and cost-optimize with Spot node groups
https://ec2spotworkshops.com/amazon-ec2-spot-cicd-workshop.html
CI/CD reference architecture
https://github.com/awslabs/ec2-spot-jenkins-plugin/
Users Application
Load Balancer
Jenkins master
(OD or RI)
Jenkins master
(OD or RI)
Jenkins agent
(Spot)
Jenkins agent
(Spot)
Spot fleet
Availability Zone B
Availability Zone A
VPC
Jenkins agent
(Spot)
Jenkins agent
(Spot)
Machine learning
DevOps—CI/CD
Websites and web applications
Workloads
on AWS
Websites and web applications
Run web services ranging from ad servers to real-time bidding servers
Deploy web applications or services on containers and scale clusters at a fraction of the cost
Use Auto Scaling with Amazon ECS or Amazon EKS to run any containerized workload,
including a web application
Amazon EC2
Auto Scaling
Amazon Elastic
Container Service
Amazon Elastic
Kubernetes Service
Scale in real time, pay per second, save up to 90%
AWS
Fargate
AWS Fargate with EC2 Spot
Up to 70% off over regular
Fargate tasks
Only pay for the resources you use
by automatically scaling based on
tasks, vCPUs, and memory
VM-level isolation by design
Run containers without managing servers or clusters
AWS Fargate
To tie it all together…
Key takeaways
Get technical
guidance in an AWS
Immersion Day
4
Experiment and test
at a lower cost to
innovate faster
1
How to automate
cost and capacity
optimization
2
Optimize your
workloads by using
best practices
3
CI/CD, analytics,
big data, machine
learning, and
web services
Spot Instances Auto Scaling and
Savings Plans
AWS Compute
Optimizer
Schedule an AWS
Immersion Day
AWS experts are here to help,
and it’s free!
Learn compute with AWS Training and Certification
20+ free digital courses cover topics related to cloud compute,
including introduction to the following services:
Resources created by the experts at AWS to help you build cloud compute skills
Compute is also covered in the classroom offering, Architecting
on AWS, which features AWS expert instructors and hands-on
activities
• Amazon Elastic Compute Cloud
(Amazon EC2)
• Amazon EC2 Auto Scaling
• AWS Systems Manager
• AWS Inferentia and Amazon EC2
Inf1 instances
Visit the learning library at https://aws.training
Thank you!
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Cobus Bernard
Sr Developer Advocate
Amazon Web Services
@cobusbernard
cobusbernard
cobusbernard

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AWS EMEA Online Summit - Blending Spot and On-Demand instances to optimizing costs

  • 2. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved. Cost and capacity optimization 0 5 Cobus Bernard Sr Developer Advocate AWS @cobusbernard cobusbernard cobusbernard
  • 4. AWS global platform AWS global infrastructure • 23 Regions with 73 Availability Zones • 4 Regions coming soon: Indonesia, Italy, Japan and Spain 216 CloudFront PoPs • 205 edge locations • 11 Regional edge caches • 245 Countries and territories served AWS global network • Redundant 100 GbE network • 100% encrypted between facilities • Private network capacity between all AWS Regions except China SLA of 99.99% availability
  • 5. Amazon EC2 instance characteristics M5d.xlarge Instance family Instance generation Instance size Instance type CPU Memory Storage Network performance Additional capabilities
  • 6. Broadest and deepest platform choice Workloads Capabilities Options (AWS, Intel, AMD) (up to 4.0 GHz) (up to 24 TiB) (HDD and NVMe) (up to 100 Gbps) (GPUs and FPGA) (Nano to 32xlarge) + + = 270+instance types
  • 7. Continued rapid pace of innovation Instance growth 270+
  • 8. Customer obsessed of the roadmap originates with customer requests and is designed to meet specific needs 90%
  • 10. Optimizing Amazon EC2 cost and capacity We continue to innovate for our customers Pricing Capacity Guidance
  • 11. Optimizing Amazon EC2 cost and capacity We continue to innovate for our customers Pricing Capacity Capacity management made easy on the broadest and deepest compute platform Guidance Cost and capacity recommendations enable ease of use and save time
  • 12. the second Amazon EC2 purchase options savings of up to 90%a significant discount more flexibility
  • 13. To optimize Amazon EC2, combine purchase options RIs or a Savings Plan Spot for fault-tolerant, flexible, stateless workloads On-Demand
  • 14. Deutsche Börse Group … covers the entire value chain in securities and derivatives trading. • Pre-IPO and listing • Trading • Clearing • Settlement • Custody • Collateral and liquidity management • Market data • Indices • Technology
  • 15. Other Instance Types can make the difference 0 0.2 0.4 0.6 0.8 1 r5.2xl r5a.2xl r5d.2xl m5.4xl r4.2xl m4.4xl InstancePrice$/h eu-central-1 spot price on-demand price
  • 16. T7 and Cloud Use-Cases – SmokeTest termination rate 1521 2919 4532 3323 2459 20 25 128 29 71 0 1000 2000 3000 4000 5000 2019-Jan 2019-Feb 2019-Mar 2019-Apr 2019-May Number of SmokeTest Spot Instances and Terminations SmokeTest all SmokeTest terminated
  • 17. Introducing Savings Plans Easy to use Receive discounted rates automatically in exchange for a monetary commitment Flexible Make a single commitment that applies across multiple AWS compute services, even as your requirements change Significant discounts Select from two types of Savings Plans to receive discounts of up to 72% on EC2 Instance Savings Plans and 66% on Compute Savings Plans Flexible purchase option that offers up to 72% discounts on Amazon EC2 and AWS Fargate usage
  • 18. Types of Savings Plans Provide the lowest prices, up to 72% off (same as Standard RIs) on the selected instance family (e.g., C5 or M5), in a specific AWS Region Offer the greatest flexibility, up to 66% off (same prices as Convertible RIs) Flexible across  Instance family: e.g., Move from C5 to M5  Region: e.g., Change from EU (Ireland) to EU (London)  OS: e.g., Windows to Linux  Tenancy: e.g., Switch Dedicated tenancy to Default tenancy  Compute options: e.g., Move from EC2 to Fargate Flexible across  Size: e.g., Move from m5.xl to m5.4xl  OS: e.g., Change from m5.xl Windows to m5.xl Linux  Tenancy: e.g., Modify m5.xl Dedicated to m5.xl Default tenancy Compute Savings Plans EC2 Instance Savings Plans
  • 19. Comparing RIs and Savings Plans Savings Plans offer all the benefits of RIs as well as improved flexibility and reduced management Compute Savings Plans EC2 Instance Savings Plans Convertible RIs* Standard RIs Savings over On-Demand Up to 66% Up to 72% Up to 66% Up to 72% Low price in exchange for monetary commitment     Pricing automatically applies to any instance families     Pricing automatically applies to any instance size   ** ** Pricing automatically applies to any tenancy or OS     Automatically apply to Fargate usage     Pricing automatically applies across any AWS Region     1- and 3-year term length options     * Convertible RIs can be changed across instance families, sizes, OS, and tenancy – they require customers to manually perform exchanges ** Regional Convertible RIs and Regional Standard RIs provide instance size flexibility
  • 20. Getting started with Savings Plans Review your Savings Plans recommendations in AWS Cost Explorer Customize recommendations based on your needs (type of Savings Plan, payment option, term length) Review hourly commitment (e.g., $10/hr) and add to cart Eligible Amazon EC2 and AWS Fargate usage is charged at a discounted Savings Plans rate up to your commitment level AWS Cost Explorer guides you through the purchasing process Just like RIs, you can purchase Savings Plans via the RI operations team
  • 21. Spot, On-Demand capacity reservations, and Savings Plan together On-Demand capacity reservations Savings Plan Spot Instances Cost-effective, scalable compute
  • 22. Capacity Interruptions only happen if OD needs capacity Pricing Smooth, infrequent changes, more predictable Instances Same infrastructure as On-Demand and RIs Usage Choose different instance types, sizes, and AZs in a single fleet or EC2 Auto Scaling group Pricing is based on long-term supply and demand trends; no bidding! Save up to 90% using EC2 Spot Instances
  • 23. Low, predictable prices Up to 90% discount over On-Demand prices Faster results Increase throughput up to 10x while staying in budget Easy to use Launch through AWS services (e.g., Amazon ECS, Amazon EKS, AWS Batch, Amazon SageMaker, Amazon EMR) or integrated third parties Why Spot Instances?
  • 24. Minimal interruptions Check for 2-minute interruption notification via instance metadata or Amazon CloudWatch events, and automate by  Checkpointing  Draining from ELB  Using stop-start and hibernate to restart faster Interruption handlers for Amazon ECS and Amazon EKS Amazon Elastic Kubernetes Service (Amazon EKS)  Connection between termination requests from AWS infrastructure to nodes  Tasks running on Spot Instances will automatically be triggered for shutdown before the instance terminates, and replacement tasks will be scheduled elsewhere on the cluster Amazon Elastic Container Service (Amazon ECS) Handling Spot interruptions Less than 5% of Spot Instances were interrupted in the last 3 months
  • 25. Flexibility is key to successful Spot usage Instance flexible Time flexible Region flexible
  • 26. B2B Enterprise TechSports, Media & Entertainment Financial Services Consumer AppsResearch AdTech & MarTech Customers across different industries and verticals use Spot
  • 27. Optimizing Amazon EC2 cost and capacity We continue to innovate for our customers Pricing Achieve optimal price/performance with different purchase models Capacity Guidance Cost and capacity recommendations enable ease of use and save time
  • 28. Amazon EC2 Cost Optimisation non-prod 100.0 71.4 35.7 29.8 0 20 40 60 80 100 24 x 7 24 x 5 12 x 5 10 x 5 % Running Time Up to 70% savings for non- production workloads
  • 29. AWS Instance Scheduler • AWS-provided solution • Custom start & stop schedules • Works with EC2 & RDS instances • Deploy using CloudFormation • Selectively tag instances to schedule • Multiple schedules per instance • 5-minute granularity https://aws.amazon.com/answers/infrastructure- management/instance-scheduler/
  • 30. Using Amazon EC2 Auto Scaling Automatically scale instances across instance families and purchase options in a single ASG to optimize cost Capacity-optimized Prioritize deploying Spot Instances into greater Spot pool capacity order to lower the chance of interruptions Lowest cost Prioritize cost by selecting a mix of On-Demand and Spot Instances to launch based on the lowest available price Prioritized list Use a prioritized list for On-Demand instance types to scale capacity during an urgent, unpredictable event to optimize performance Amazon EC2 Auto Scaling AZ1 and AZ2
  • 31. Amazon EC2 Spot Instance pools explained $0.27 $0.29$0.50 1b 1c1a 8XL $0.30 $0.16$0.214XL $0.07 $0.08$0.082XL $0.05 $0.04$0.04XL $0.01 $0.04$0.01L C4 $1.76 On demand $0.88 $0.44 $0.22 $0.11 Each instance family Each instance size Each availability zone In every Region Is a separate Spot pool R5 M4 M5 I3 C5R4 i3en R5a R5d
  • 32. ASG capacity-optimized allocation strategy us-east-1a Desired capacity: 12 SpotAllocationStrategy: capacity-optimized OnDemandBaseCapacity: 0 OnDemandPercentageAboveCapacity: 0 r5.large m4.large m5.large R5 R5 R5 R5 us-east-1b us-east-1c Overrides: [“r5.large”, “m4.large”, ”m5.large”] $$ $ $$$ r5.large m4.large m5.large $$$ $$ $ r5.large m4.large m5.large $ $$$ $$
  • 33. ASG lowest-price allocation strategy us-east-1a Desired capacity: 12 SpotAllocationStrategy: lowest-price OnDemandBaseCapacity: 0 OnDemandPercentageAboveCapacity: 0 r5.large m4.large m5.large R5 R5 R5 R5 us-east-1b us-east-1c Overrides: [“r5.large”, “m4.large”, ”m5.large”] $$ $ $$$ r5.large m4.large m5.large $$$ $$ $ r5.large m4.large m5.large $ $$$ $$
  • 34. Instance type overrides and allocation strategies ASG adjusts to new configuration as it scales up and down As ASG scales up Launch capacity according to the new configuration As ASG scales down Prioritize terminating instances not matching the new configuration New termination policy: AllocationStrategy Instance type overrides: m4.large, m5.large m4.large m5.large Instance type overrides: m5.large, c5.large m4.large m5.large c5.large Instance type overrides: m5.large, c5.large m4.large m5.large c5.large
  • 35. To optimize Amazon EC2, combine purchase options RIs or a Savings Plan Spot for fault-tolerant, flexible, stateless workloads On-Demand
  • 36. Before: Multiple ASGs to use Spot, On-Demand, and RIs together m4.large Spot ASG Min: 1 Max: 10 m5.large Spot ASG Min: 1 Max: 10 c4.xlarge O-D ASG Min: 1 Max: 10 Availability Zone 1 Availability Zone 2 Availability Zone 3 Before, with three ASGs —one for each instance type/ purchase option
  • 37. Then: Spot, On-Demand, and RIs in a single ASG m4.large Spot Instances m5.large Spot Instances c4.xlarge On-Demand instances The new way combines purchase options, instance types, and AZs in a single ASG Single ASGAvailability Zone 1 Availability Zone 2 Availability Zone 3
  • 38. m4.xlarge Spot Weight of 1 m4.2xlarge Spot Weight of 2 m4.4xlarge On-Demand Weight of 4 Availability Zone 1 Availability Zone 2 Availability Zone 3 Different instance types contribute differently to total capacity Now: Spot, On-Demand, and RIs in a single ASG with weights
  • 39. Amazon EC2 Spot Instance pools explained $0.27 $0.29$0.50 1b 1c1a 8XL $0.30 $0.16$0.214XL $0.07 $0.08$0.082XL $0.05 $0.04$0.04XL $0.01 $0.04$0.01L C4 $1.76 On demand $0.88 $0.44 $0.22 $0.11 16 Weight 8 4 2 1 $0.11 Weighted price $0.11 $0.11 $0.11 $0.11 $0.017 $0.018$0.032 1b 1c1a $0.038 $0.02$0.032 $0.016 $0.02$0.021 $0.025 $0.04$0.02 $0.01 $0.04$0.01
  • 40. API parameters "MixedInstancesPolicy": { "LaunchTemplate": { "LaunchTemplateSpecification": { "LaunchTemplateName": "MyLaunchTemplate" }, "Overrides": [ { "InstanceType": "m4.xLarge", "WeightedCapacity": "1" }, { "InstanceType": "m4.2xLarge", "WeightedCapacity": "2" }, { "InstanceType": "m4.4xLarge", "WeightedCapacity": "4" } ] }, "InstancesDistribution": { "OnDemandAllocationStrategy": "prioritized", "OnDemandBaseCapacity": 10, "OnDemandPercentageAboveBaseCapacity": 50, "SpotAllocationStrategy": “capacity-optimized", "SpotInstancePools": 2 } } AZ1 and AZ2 Desired Min Max On-Demand base 50% On-Demand 50% Spot Minimum On-Demand (10)
  • 41. API parameters "MixedInstancesPolicy": { "LaunchTemplate": { "LaunchTemplateSpecification": { "LaunchTemplateName": "MyLaunchTemplate" }, "Overrides": [ { "InstanceType": "m4.xLarge", "WeightedCapacity": "1" }, { "InstanceType": "m4.2xLarge", "WeightedCapacity": "2" }, { "InstanceType": "m4.4xLarge", "WeightedCapacity": "4" } ] }, "InstancesDistribution": { "OnDemandAllocationStrategy": "prioritized", "OnDemandBaseCapacity": 10, "OnDemandPercentageAboveBaseCapacity": 50, "SpotAllocationStrategy": “capacity-optimized", "SpotInstancePools": 2 } } AZ1 and AZ2 Desired Min Max On-Demand base 50% On-Demand 50% Spot Minimum On-Demand (10)
  • 42. API parameters "MixedInstancesPolicy": { "LaunchTemplate": { "LaunchTemplateSpecification": { "LaunchTemplateName": "MyLaunchTemplate" }, "Overrides": [ { "InstanceType": "m4.xLarge", "WeightedCapacity": "1" }, { "InstanceType": "m4.2xLarge", "WeightedCapacity": "2" }, { "InstanceType": "m4.4xLarge", "WeightedCapacity": "4" } ] }, "InstancesDistribution": { "OnDemandAllocationStrategy": "prioritized", "OnDemandBaseCapacity": 10, "OnDemandPercentageAboveBaseCapacity": 50, "SpotAllocationStrategy": “capacity-optimized", "SpotInstancePools": 2 } } AZ1 and AZ2 Desired Min Max On-Demand base 50% On-Demand 50% Spot Minimum On-Demand (10)
  • 43. API parameters "MixedInstancesPolicy": { "LaunchTemplate": { "LaunchTemplateSpecification": { "LaunchTemplateName": "MyLaunchTemplate" }, "Overrides": [ { "InstanceType": "m4.xLarge", "WeightedCapacity": "1" }, { "InstanceType": "m4.2xLarge", "WeightedCapacity": "2" }, { "InstanceType": "m4.4xLarge", "WeightedCapacity": "4" } ] }, "InstancesDistribution": { "OnDemandAllocationStrategy": "prioritized", "OnDemandBaseCapacity": 10, "OnDemandPercentageAboveBaseCapacity": 50, "SpotAllocationStrategy": “capacity-optimized", "SpotInstancePools": 2 } } AZ1 and AZ2 Desired Min Max On-Demand base 50% On-Demand 50% Spot Minimum On-Demand (10)
  • 44. API parameters "MixedInstancesPolicy": { "LaunchTemplate": { "LaunchTemplateSpecification": { "LaunchTemplateName": "MyLaunchTemplate" }, "Overrides": [ { "InstanceType": "m4.xLarge", "WeightedCapacity": "1" }, { "InstanceType": "m4.2xLarge", "WeightedCapacity": "2" }, { "InstanceType": "m4.4xLarge", "WeightedCapacity": "4" } ] }, "InstancesDistribution": { "OnDemandAllocationStrategy": "prioritized", "OnDemandBaseCapacity": 10, "OnDemandPercentageAboveBaseCapacity": 50, "SpotAllocationStrategy": “capacity-optimized", "SpotInstancePools": 2 } } AZ1 and AZ2 Desired Min Max On-Demand base 50% On-Demand 50% Spot Minimum On-Demand (10)
  • 45. API parameters "MixedInstancesPolicy": { "LaunchTemplate": { "LaunchTemplateSpecification": { "LaunchTemplateName": "MyLaunchTemplate" }, "Overrides": [ { "InstanceType": "m4.xLarge", "WeightedCapacity": "1" }, { "InstanceType": "m4.2xLarge", "WeightedCapacity": "2" }, { "InstanceType": "m4.4xLarge", "WeightedCapacity": "4" } ] }, "InstancesDistribution": { "OnDemandAllocationStrategy": "prioritized", "OnDemandBaseCapacity": 10, "OnDemandPercentageAboveBaseCapacity": 50, "SpotAllocationStrategy": “capacity-optimized", "SpotInstancePools": 2 } } AZ1 and AZ2 Desired Min Max On-Demand base 50% On-Demand 50% Spot Minimum On-Demand (10)
  • 46. AWS and third-party integrations with Spot Instances and EC2 Auto Scaling Amazon EC2 Auto Scaling Amazon EC2 fleet Amazon EMR AWS CloudFormation AWS Batch AWS Thinkbox Amazon Elastic Container Service Amazon Elastic Kubernetes Service AWS Fargate Amazon SageMaker AWS Elastic Beanstalk
  • 47. Optimizing Amazon EC2 cost and capacity We continue to innovate for our customers Pricing Achieve optimal price/performance with different purchase models Capacity Capacity management made easy on the broadest and deepest compute platform Guidance
  • 48. AWS Compute Optimizer Recommends optimal instances for Amazon EC2 and Amazon EC2 Auto Scaling groups from 140+ instances from M, C, R, T, and X families Applies insights Saves timeLower costs performance
  • 49. Simplifying compute optimization AWS Compute Optimizer Identify optimal AWS compute resources for your workloads Mettle scans your AWS infrastructure and uses machine learning to automatically identify optimal AWS resources for your workloads Identifies workload characteristics and profile based on the data gathered Matches the resource requirements of your workloads to optimal AWS resources with recommendations Amazon CloudWatch metrics EC2 Instance EC2 Auto Scaling groups Helps you visualize what-if scenarios based on the recommended resources AWS resources metadata
  • 50. Easy to choose with AWS Compute Optimizer New services that recommend optimal AWS compute resources to reduce costs up to 25% Recommends optimal EC2 instances Optimizes performance and reduces costs by making recommendations to help you right-size compute to your workloads Analyzes Amazon CloudWatch metrics and considers Auto Scaling group configuration for intuitive and actionable recommendations Up to three recommendations per workload Available at no additional charge
  • 51. Workloads on AWS Analytics and big data Databases DevOps—CI/CD Enterprise applications IoT Machine learning Storage Websites and web applications
  • 52. Machine learning DevOps—CI/CD Websites and web applications Workloads on AWS
  • 53. Machine learning (ML) Get ML solutions to market faster with access to built-in algorithms, ML frameworks, and custom models Save up to 90% in training costs with managed spot training Automatically manages Spot capacity on your behalf All instance types, training models, and configurations Amazon SageMaker managed spot training
  • 54. Machine learning DevOps—CI/CD Websites and web applications Workloads on AWS
  • 55. DevOps—CI/CD Or, run Jenkins build jobs inside your Kubernetes clusters and cost-optimize with Spot node groups https://ec2spotworkshops.com/amazon-ec2-spot-cicd-workshop.html
  • 56. CI/CD reference architecture https://github.com/awslabs/ec2-spot-jenkins-plugin/ Users Application Load Balancer Jenkins master (OD or RI) Jenkins master (OD or RI) Jenkins agent (Spot) Jenkins agent (Spot) Spot fleet Availability Zone B Availability Zone A VPC Jenkins agent (Spot) Jenkins agent (Spot)
  • 57. Machine learning DevOps—CI/CD Websites and web applications Workloads on AWS
  • 58. Websites and web applications Run web services ranging from ad servers to real-time bidding servers Deploy web applications or services on containers and scale clusters at a fraction of the cost Use Auto Scaling with Amazon ECS or Amazon EKS to run any containerized workload, including a web application Amazon EC2 Auto Scaling Amazon Elastic Container Service Amazon Elastic Kubernetes Service Scale in real time, pay per second, save up to 90% AWS Fargate
  • 59. AWS Fargate with EC2 Spot Up to 70% off over regular Fargate tasks Only pay for the resources you use by automatically scaling based on tasks, vCPUs, and memory VM-level isolation by design Run containers without managing servers or clusters AWS Fargate
  • 60. To tie it all together…
  • 61. Key takeaways Get technical guidance in an AWS Immersion Day 4 Experiment and test at a lower cost to innovate faster 1 How to automate cost and capacity optimization 2 Optimize your workloads by using best practices 3 CI/CD, analytics, big data, machine learning, and web services Spot Instances Auto Scaling and Savings Plans AWS Compute Optimizer
  • 62. Schedule an AWS Immersion Day AWS experts are here to help, and it’s free!
  • 63. Learn compute with AWS Training and Certification 20+ free digital courses cover topics related to cloud compute, including introduction to the following services: Resources created by the experts at AWS to help you build cloud compute skills Compute is also covered in the classroom offering, Architecting on AWS, which features AWS expert instructors and hands-on activities • Amazon Elastic Compute Cloud (Amazon EC2) • Amazon EC2 Auto Scaling • AWS Systems Manager • AWS Inferentia and Amazon EC2 Inf1 instances Visit the learning library at https://aws.training
  • 64. Thank you! © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved. Cobus Bernard Sr Developer Advocate Amazon Web Services @cobusbernard cobusbernard cobusbernard

Editor's Notes

  1. So how are we going to do this? Overview of what drives us to innovate at AWS Automate cost and capacity management – Savings Plan, Compute Optimizer, EC2 Auto Scaling and Spot Instances Workload examples – CI/CD, Containerized Web Apps, and Big Data, analytics and AI/ML. Wrap up with next steps
  2. 1/ First, it all starts with our foundation. As you look at the Gartner IaaS MQ, Gartner calls our the breadth of our offering and the strength of our infrastructure, including the unmatched reliability and availability we provide. 3/ The AWS Cloud spans 69 Availability Zones within 22 geographic Regions around the world, with announced plans for 9 more Availability Zones and four more Regions in, Cape Town, Jakarta, and Milan. global network of 191 Points of Presence (180 Edge Locations and 11 Regional Edge Caches) in 73 cities across 33 countries.  4/ Amazon CloudFront uses a global network of 187 Points of Presence (176 Edge Locations and 11 Regional Edge Caches) in 69 cities across 30 countries 5/ Our AWS geographical regions are comprised of availability zones (AZ’s) that are set of data centers isolated from failures and low latency connectivity providing natively high availability. 6/ All supported by the AWS global network which connects all of our regions. A network that's been built specifically for the cloud, and we continue to iterate on it.
  3. Every servers has 4 hey computing resources– CPU, Memory, Storage, Network capabilities Some workloads are more CPU intensive, and more memory intensive, So we created different SKUs or familes – that’s the first letter to the right As we added new technology to our instances, we realized we wanted to expose these innovations – so we introduced generations – what CPU capabilities and cjupsets, network capabilities Last one is size – pretty simple tshirt – still have the same ratio, chipset, and but each size has twice the CPU, memory and storage of the previous size – enabling to scale up your workloads
  4. What does all of this mean? More choices enables better performance for specific workloads Faster processors from Intel, processor choice with Graviton (ARM) and AMD, instances for accelerated computing with our partner Nvidia – Network offerings up to 100GBps performance Elastic Graphics or Elastic Inference and of course Elastic Block Store for greater performance and storage flexibility. We will have nearly 300 instances by the end of the year to support virtually every workload and business need.
  5. Significant improvements optimizing underlying technology for performance and price. Making improvements in CPU, SSD, Networking, and other components available to customer We are providing you with new choice of processor and architecture.
  6. Compute and cloud innovations are grounded in customer obsession. 90% of our product roadmap is based on customer feedback – what you’ve told us that you need. Customers push the boundaries of what is possible today, driving us to enable new scenarios and capabilities.
  7. Now lets see how we have made it easy for you to automate capacity management and cost optimization
  8. By focusing on customers we have evolved over time. How we evolve our product strategy to enable you to innovate faster Pricing and capacity optimizations, guidance (three pillars)
  9. How to purchase EC2 How to optimize compute for savings and scale
  10. Four different ways to purchase compute On-Demand: Pay-as-you-go, no commitments, best for fluctuating workloads Reserved Instance: Long term commitments that offer big savings over On-Demand prices. Best for always on workloads Introducing Savings Plan: Just like Reserved Instances, but monetary commitment based and compute can be used across Fargate and EC2 Spot Instances: Same as pay-as-you-go pricing as On-Demand, but at up to 90% off. EC2 can reclaim with a 2 minute warning. Best for stateless or fault tolerant workloads All four purchasing options use the same underlying EC2 instances and AWS infrastructure across 22 Regions [Poll] How many of you use Spot Instances? Excited to announce New Spot integrations Updates to EC2 Auto Scaling that make it easier than ever to incorporate Spot Customer initiated Start/Stop for EC2 Spot
  11. So, when should you use Spot, On-Demand or RIs? Picking just one option is the wrong solution. Use all three to optimize cost and capacity
  12. Leading European Financial Infrastructure provider and –Marketplace with roots since 1585 Early adopter: Fully automated electronic trading since 1990 (Derivatives); 1997 (Cash) Operating lowest latency network & technology Largest European Data Centre due to DBG network effects ~2,000 IT staff organized in Product Organization Most DBG business applications own developed and maintained Core element of European and Global Capital Market – therefore under highest regulatory supervision
  13. On-Demand capacity reservations are a perfect fit for steady state workloads
  14. So why should you use Savings Plans? First, they’re super to easy to use. Customers no longer have to make commitments to specific instance configurations and can easily save money just by committing to a $ spend. Secondly they provide significant savings, up to 72% off OD, just RIs. Finally they provide a ton of flexibility. With a Savings Plan, all you have to do is make a simple commitment to a spend/hour and you will save money on your usage automatically, even as that usage changes from one region to another, or from instance type to another or even if you move from EC2 to Fargate. All without having to perform exchanges or modifications.
  15. AWS offers two types of Savings Plans - EC2 Instance Savings Plans and Compute Savings Plans Compute Savings Plans provide the most flexibility and help reduce usage costs by up to 66%, just like Convertible RIs. These plans automatically apply to EC2 instance usage regardless of instance family, size, AZ, region, OS or tenancy, as well as Fargate usage. For example, with Compute Savings Plans, you can switch from C4 to M5 instances, shift a workload from EU (Ireland) to EU (London), or move a workload from EC2 to Fargate at any time and automatically continue to receive discounts. EC2 Instance Savings Plans provide the lowest prices, in exchange for a commitment to usage of individual instance families in a region (e.g. commit to a consistent level of M5 usage in N. Virginia). This automatically provides you with savings of up to 72% off the On-Demand price of the selected instance family in that region regardless of AZ, size, OS or tenancy. EC2 Instance Savings Plans allows you to change your usage between instances within a family in that region. For example, you can move from c5.xlarge running Windows to c5.2xlarge running Linux, and automatically benefit from the Savings Plans prices.
  16. The table compares RIs and Savings Plans. Savings Plans provides all the benefits of RIs, - significant savings, similar payment options but also provides increased flexibility. The most flexible discount product available, Compute savings plans automatically apply to whatever eligible usage you have in your account without any of the effort required to exchange and totally eliminates true up charges as the discounts automatically apply. Compute Savings Plans offers upto 66% off, just like Convertible RIs but a) it applies automatically (no need to worry about what to exchange, when to exchange, what to exchange into, how to optimize true-up charges), b) applies across regions and c) applies to Fargate usage.   EC2 Instance savings plans offer upto 72% off just like Standard RI, but in addition to automatically applying across sizes, they also apply across OS and tenancy.
  17. Savings Plans is the easiest way to save on compute. Customers can sign up for Savings Plans in a few simple steps using the AWS Cost Explorer. Now lets take a look at these steps in detail.
  18. Combine savings plan and on-demand capacity reservations for steady state workloads and add Spot Instances to maximize savings and scalability.
  19. Leverage the scale of AWS at a fraction of the cost Simplified pricing model, no more bidding. Spot is only interrupted when the EC2 needs to reclaim Spot for On-Demand capacity. No need to worry about your bidding strategy. Spot prices gradually adjust based on long-term supply and demand trends. Spot is a reward for good architecture
  20. Not only save big, but get results faster Use Spot across a number of AWS services and third parties. Will share more about these integrations later in the presentation
  21. K8s interruption handler: https://aws.amazon.com/about-aws/whats-new/2019/11/aws-supports-automated-draining-for-spot-instance-nodes-on-kubernetes/ ECS interruption handler: https://aws.amazon.com/about-aws/whats-new/2019/09/amazon-ecs-supports-automated-draining-for-spot-instances-running-ecs-services/
  22. Two main kind of workloads: Time sensitive: Web services, analytics, grid computing, containers Time insensitive: ML training, Genomics analysis, development, testing, one-time queries Instance flexibility: (Time sensitive workloads) Mix instance types with similar capabilityes: num of vCPUs / Memory Time flexible: (time insensitive workloads) Workloads that require specific instance types, but can be flexible on completion times (e.g. batch Jobs with no SLA, ML training Jobs…) Region flexible: large size / very instance specific kind of workloads e.g. real time rendering with a specific g3 instance, can benefit of increased region flexibility
  23. Pay for what you need, but have the option to scale in and out when needed
  24. Non production can make up to 90% of the capacity of some workloads and commonly over 50% It doesn’t need to scale dynamically in response to demand. 10x5 is a common development pattern Anything running below 75% of the time can be considered for better cost optimization than RIs https://aws.amazon.com/premiumsupport/knowledge-center/stop-start-instance-scheduler/
  25. Specify different percents of Spot and On-Demand using EC2 Auto Scaling. RI and Savings Plan instance discounts automatically applied * New - Capacity Optimized is Spot pool capacity aware, limiting chance of interruption Example - Specify launching C5large across us-east-1, us-east-2 and us-west-1. ASG will launch Spot in deepest capacity pools Also specify scale based on ”Lowest Cost” or “Prioritized List”
  26. This time, we have the exact same ASG represented, but using the capacity-optimized SpotAllocationStrategy. In this case we don’t have SpotInstancePools as that parameter is specific to lowest-Price. And if we look at the instances, ASG will launch instances on the deepest pools on each AZ, which may not always be the cheapest, but are from the deepest pools at instance launch time and reduce the likelihood of interruptions
  27. So, when should you use Spot, On-Demand or RIs? Picking just one option is the wrong solution. Use all three to optimize cost and capacity
  28. Before: build custom logic, leverage multiple APIs No clean way to leverage Spot Instances, On-Demand and RIs in a single Auto Scaling group. Complex complex code to discover capacity, be price aware across different instance types and Availability Zones, and scale capacity in different pools Create three different auto scaling groups for c4.xlarge On-Demand, m5.large Spot, and another m4.large Spot ASG
  29. Then: One ASG to scale across c4.xlarge On-Demand instances, m5.large Spot Instances, and m4.large Spot Instances. Scaling in and out with EC2 Auto Scaling ensured base capacity fulfilled with On-Demand instances and additional capacity with Spot instances or a specified percentage mix of On-Demand or Spot instances If AZ1 becomes unavailable, Auto Scaling launches instances in AZ2 or AZ3 to compensate all within a single AZ Optimizing capacity management and cost optimization became easier
  30. Introducing instance type weights Configure weight to scale in and out based on previous gen instances or vCPUs across multiple AZs Distribute Capacity evenly between availability zones for On-Demand and Spot separately
  31. <Including these to explain in more detail how this split / balance works> Overrides let you specify additional instance types to consider
  32. Prioritised is the only option, it will use the first instance in list, try to fill, then only moves to 2nd type, etc “The first instance type in the array is prioritized higher than the last. If all your On-Demand capacity cannot be fulfilled using your highest priority instance, then the Auto Scaling groups launches the remaining capacity using the second priority instance type, and so on.”
  33. Define a base capacity to use on—demand for
  34. Then define the split between on-demand / spot (percentage that is base, e.g. 20% for desired of 10 would be 2)
  35. Then pick capacity optimized, or price optimised
  36. How many of the specified overrides to use as pools for spot
  37. You don’t have to do the heavy lifting yourself to combine different purchase options for your workloads. You have the option to use multiple purchase options built-in EC2 Fleet, EC2 Auto Scaling, ECS, EKS, Thinkbox, EMR, CloudFormation, or Batch Making Spot easier to use in existing applications: Auto Scaling now integrated with ECS, New EKS interruption handler for nodes running on Spot, Spot is now integrated with Elastic Beanstalk - automate the deployment and scaling of applications while taking advantage of savings offered with Spot Use 3rd party tools, services, or frameworks like Terraform, CloudBees Jenkins, Qubole or Kubernetes and now IBM Spectrum Symphony
  38. Now to introduce an exciting new product to help you choose the right instance for your workload
  39. 1/ AWS Compute Optimizer uses machine learning models trained on millions of workloads to help customers optimize their compute resources for cost and performance across all of workloads they run. You can take advantage of the recommendations in Compute Optimizer to reduce costs by up to 25%. 2/ AWS Compute Optimizer delivers instance type and auto scaling groups recommendations, making it even easier for customers to choose the right compute resources for specific workloads. 3/ AWS Compute Optimizer analyzes the configuration, resource utilization, and performance data of a workload to identify dozens of defining characteristics, such as whether the workload is CPU-intensive and whether it exhibits a daily pattern. Compute Optimizer then uses machine learning to process these characteristics to predict how the workload would perform on various hardware platforms, delivering resource recommendations. 4/ AWS Compute Optimizer delivers up to 3 recommended options for each AWS resource analyzed to right size and improve workload performance. Compute Optimizer predicts the expected CPU and memory utilization of your workload on various EC2 instance types. This helps you understand how your workload would perform on the recommended options before implementing the recommendations.
  40. 1/ Mettle uses machine learning models trained on millions of workloads to help customers optimize their compute resources for cost and performance across all of workloads they run. You can take advantage of the recommendations in Mettle to reduce costs by up to 25%. 2/ Mettle delivers instance type and auto scaling groups recommendations, making it even easier for customers to choose the right compute resources for specific workloads. 3/ Mettle analyzes the configuration, resource utilization, and performance data of a workload to identify dozens of defining characteristics, such as whether the workload is CPU-intensive and whether it exhibits a daily pattern. Mettle then uses machine learning to process these characteristics to predict how the workload would perform on various hardware platforms, delivering resource recommendations. 4/ Mettle delivers up to 3 recommended options for each AWS resource analyzed to right size and improve workload performance. Mettle predicts the expected CPU and memory utilization of your workload on various EC2 instance types. This helps you understand how your workload would perform on the recommended options before implementing the recommendations.
  41. How does this work? Predictive Scaling’s machine learning algorithms leverage data from billions of traffic patterns in1/ Mettle uses machine learning models trained on millions of workloads to help customers optimize their compute resources for cost and performance across all of workloads they run. You can take advantage of the recommendations in Mettle to reduce costs by up to 25%. 2/ Mettle delivers instance type and auto scaling groups recommendations, making it even easier for customers to choose the right compute resources for specific workloads. 3/ Mettle analyzes the configuration, resource utilization, and performance data of a workload to identify dozens of defining characteristics, such as whether the workload is CPU-intensive and whether it exhibits a daily pattern. Mettle then uses machine learning to process these characteristics to predict how the workload would perform on various hardware platforms, delivering resource recommendations. 4/ Mettle delivers up to 3 recommended options for each AWS resource analyzed to right size and improve workload performance. Mettle predicts the expected CPU and memory utilization of your workload on various EC2 instance types. This helps you understand how your workload would perform on the recommended options before implementing the recommendations Amazon.com to predict future changes. The pre-trained model then processes last 2 weeks of load metrics to forecasts the load metric for the next two days The model also performs regression analysis between load metric and scaling metric, schedules scaling actions for the next two days, hourly, and then repeats this process every day
  42. As you know, you can run any workload on AWS Compute Services - from Databases to DevOps to IoT to Machine learning
  43. Let’s take a look at few real-life scenarios. See concrete examples to get started with cost and capacity optimization.
  44. With Managed Spot Training, SageMaker manages Spot instances on your behalf, no need to build additional tooling. Can be used to train machine learning models, using the built-in algorithms with SageMaker, your own custom algorithms, and those available in AWS Marketplace. Built-in algorithms and frameworks automatically save model checkpoints periodically. Training jobs to pause and resume reliably as and when Spot capacity becomes available. Available in all regions and SageMaker instances
  45. [Poll]How many of you run your CI/CD pipeline on AWS [Poll] What build tools are you using today? Jenkins? Bamboo?
  46. Continuous Integration with Jenkins is a perfect use case for cost optimization. All the worker nodes in the cluster can leverage Spot and provide savings of up to 90%.  Jenkins plug-in will launch Spot instances as worker nodes for the CI server and automatically scale capacity with the load
  47. Simplified reference architecture. Jenkins Master and agents are running in the VPC. The Jenkins Master is behind an Application Load Balancer EC2 Jenkins plugin launches Spot instances as Agents for Jenkins CI server You can specify the scaling limits in your cloud settings of your plug-in. Jenkins will try to scale EC2 Fleet up or down depending on the state of your nodes
  48. Now moving on to Websites and apps on Containers. [Poll] How many of you use containers today? How many of you use ECS? EKS or Kubernetes natively on AWS?
  49. Containers are often stateless and fault-tolerant – a no-brainer for using Spot and Auto Scaling Groups ECS and EKS: Two highly scalable, high-performance container orchestration services, Run microservices, like a mapping API, or a real time bidding service on containers, on top of EC2 Instances – and have them managed by Fleet or by Auto Scaling. This is a super easy way to optimize your containers for both price and performance
  50. Deploy and manage applications, not infrastructure With Spot save up to 70% off Control how you scale based on tasks, vCPUs and memory VM-level boundary enabling workload isolation and improved security as each task or pod runs on its own kernel.
  51. Lower cost, innovate faster with Spot Instances Maximize capacity with capacity optimized EC2 Auto Scaling and Savings Plan to lock in deep discounts for steady state workloads Use Compute Optimizer for workload optimization Schedule time an Immersion Day for hands-on from an AWS expert
  52. Here to help Happy to sit down, understand your workloads
  53. If you’re ready to continue learning, check out our library of free digital courses, including introductory primers on a range of services You can also take classroom training to get hands on practice and learn directly from an instructor. Visit the learning library for the full list of courses