Downloaded 23 times
![© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
ASG capacity-optimizedallocation 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
$
$$$
$$](https://image.slidesharecdn.com/2020-04-09awswebinar6-costoptimisationonaws-200414093837/85/AWS-SSA-Webinar-Cost-optimisation-on-AWS-37-320.jpg)
![© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
ASG lowest-priceallocationstrategy
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
$
$$$
$$](https://image.slidesharecdn.com/2020-04-09awswebinar6-costoptimisationonaws-200414093837/85/AWS-SSA-Webinar-Cost-optimisation-on-AWS-38-320.jpg)
![© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
APIparameters
"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)](https://image.slidesharecdn.com/2020-04-09awswebinar6-costoptimisationonaws-200414093837/85/AWS-SSA-Webinar-Cost-optimisation-on-AWS-44-320.jpg)
![© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
APIparameters
"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)](https://image.slidesharecdn.com/2020-04-09awswebinar6-costoptimisationonaws-200414093837/85/AWS-SSA-Webinar-Cost-optimisation-on-AWS-45-320.jpg)
![© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
APIparameters
"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)](https://image.slidesharecdn.com/2020-04-09awswebinar6-costoptimisationonaws-200414093837/85/AWS-SSA-Webinar-Cost-optimisation-on-AWS-46-320.jpg)
![© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
APIparameters
"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)](https://image.slidesharecdn.com/2020-04-09awswebinar6-costoptimisationonaws-200414093837/85/AWS-SSA-Webinar-Cost-optimisation-on-AWS-47-320.jpg)
![© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
APIparameters
"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)](https://image.slidesharecdn.com/2020-04-09awswebinar6-costoptimisationonaws-200414093837/85/AWS-SSA-Webinar-Cost-optimisation-on-AWS-48-320.jpg)
![© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
APIparameters
"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)](https://image.slidesharecdn.com/2020-04-09awswebinar6-costoptimisationonaws-200414093837/85/AWS-SSA-Webinar-Cost-optimisation-on-AWS-49-320.jpg)
![© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
"MixedInstancesPolicy": {
"LaunchTemplate": {
"LaunchTemplateSpecification": {
"LaunchTemplateName": ”MyLaunchTemplate"
},
"Overrides": [
{ "InstanceType": "m4.4xlarge" },
{ "InstanceType": "m5.4xlarge" },
{ "InstanceType": "c5d.4xlarge" },
{ "InstanceType": "m5d.4xlarge" },
{ "InstanceType": "c4.4xlarge" }
]
},
"InstancesDistribution": {
"OnDemandAllocationStrategy": "prioritized",
"OnDemandBaseCapacity": 0,
"OnDemandPercentageAboveBaseCapacity": 30,
"SpotAllocationStrategy": "lowest-price",
"SpotInstancePools": 2
}
}
Producer fleetconfiguration
AZ1 and AZ2
Desired – 50
Min – 20
Max – 80
30% On-Demand (15)
70% Spot (35)](https://image.slidesharecdn.com/2020-04-09awswebinar6-costoptimisationonaws-200414093837/85/AWS-SSA-Webinar-Cost-optimisation-on-AWS-51-320.jpg)
![© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
"MixedInstancesPolicy": {
"LaunchTemplate": {
"LaunchTemplateSpecification": {
"LaunchTemplateName": ”MyLaunchTemplate"
},
"Overrides": [
{ "InstanceType": "m4.4xlarge" },
{ "InstanceType": "m5.4xlarge" },
{ "InstanceType": "c5d.4xlarge" },
{ "InstanceType": "m5d.4xlarge" },
{ "InstanceType": "c4.4xlarge" }
]
},
"InstancesDistribution": {
"OnDemandAllocationStrategy": "prioritized",
"OnDemandBaseCapacity": 250,
"OnDemandPercentageAboveBaseCapacity": 0,
"SpotAllocationStrategy": "lowest-price",
"SpotInstancePools": 2
}
}
Consumer fleetconfiguration
AZ1 and AZ2
Minimum On-Demand (250)
Min – 200
Max – 350
On-Demand Base – 250
0% On-Demand (0)
100% Spot (50)
Desired – 300](https://image.slidesharecdn.com/2020-04-09awswebinar6-costoptimisationonaws-200414093837/85/AWS-SSA-Webinar-Cost-optimisation-on-AWS-52-320.jpg)
Uploaded byCobus Bernard
AWS SSA Webinar - Cost optimisation on AWS
The document outlines various Amazon EC2 instances and cost optimization strategies. It highlights instances tailored for specific workloads, such as general-purpose, memory-optimized, storage-optimized, and accelerated computing. Additionally, it details cost-saving measures such as savings plans, instance scheduling, and the use of spot instances to achieve significant discounts on AWS services.
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AWS SSA Webinar - Cost optimisation on AWS
- 1. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Cost optimisation onAWS O n l i n e W e b i n a r – 2 0 2 0 / 0 4 / 0 9 Cobus Bernard Sr Developer Advocate Amazon Web Services @cobusbernard cobusbernard cobusbernard
- 2. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved.
- 3. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved.
- 4. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Amazon EC2 instance characteristics M5d.xlarge Instance family Instance generation Instance size Instance type CPU Memory Storage Network performance Additional capabilities
- 5. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Broadest and deepestplatformchoice 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
- 6. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Amazon EC2 general-purpose instances M5 instances Balance of compute, memory, and network resources 4:1 memory-to-vCPU ratio A1 instances Workloads that can scale out across multiple cores, fit within memory, and run on ARM instructions T3 instances Baseline level of CPU performance with the ability to burst above the baseline for workloads that don’t require sustained performance
- 7. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Amazon EC2 memory-optimized instances R5 instances Accelerate performance for workloads that process large datasets in memory 8:1 memory-to-vCPU ratio High-memory instances Extreme memory needs Certified to run SAP HANA From 6 to 24 TB of memory X1 and X1e instances For memory-intensive workloads and very large in-memory workloads 16:1 and 32:1 memory-to-vCPU ratios
- 8. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Amazon EC2storage-optimized instances I3/I3en instances I/O optimized for high-transaction workloads, low-latency workloads H1 instances Designed for applications that require low cost, high-disk throughput and high-sequential-disk I/O access to very large datasets More vCPUs and memory per TB of disk than D2 D2 instances Lowest cost per storage ($/GB) Supports high-sequential-disk throughput D2
- 9. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Amazon EC2acceleratedcomputing instances GPU compute instances for use cases including deep learning training, HPC simulations, financial computing, and batch rendering Feature latest NVIDIA high-end GPUs including Volta V100 Customer programmable FPGAs that provide dramatic performance improvements for applications such as financial computing, genomics, accelerated search, and image processing Feature Xilinx Virtex UltraScale+ VU9P FPGAs in a single instance Programmable via VHDL, Verilog, or OpenCL GPU graphics instances designed for workloads such as 3D rendering, remote graphics workstations, video encoding, and AR/VR Feature NVIDIA midrange GPUs such as Turing T4 GPUs, with GRID Virtual Workstation features and license P series P2/P3 instances G series G3/G4 instances FPGA instances F1 instances
- 10. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Instance Discovery New search and discovery experience to easily find EC2 instance types Quicker and easier for you to find and compare different instance types and project costs Announcing
- 11. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved.
- 12. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved.
- 13. Amazon EC2CostOptimisation non-prod 100.0 71.4 35.7 29.8 0 20 40 60 80 100 24x 7 24 x 5 12 x 5 10 x 5 % RunningTime Up to 70% savings for non- production workloads
- 14. AWS InstanceScheduler • AWS-providedsolution • 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/
- 15. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved.
- 16. VPC Endpoints • Egresstraffic incurs a cost • UseVPC Enpoints for: • S3 • DynamoDB https://docs.aws.amazon.com/vpc/latest/userguide/v pc-endpoints.html
- 17. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved.
- 18. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. SavingsPlans 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
- 19. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. TypesofSavings 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.xlWindows to m5.xl Linux Tenancy: e.g., Modify m5.xl Dedicated to m5.xl Default tenancy Compute Savings Plans EC2 Instance Savings Plans
- 20. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Getting startedwithSavingsPlans 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. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved.
- 22. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Low utilization High utilization Opportunity: Mostinstancesaren’tverybusy
- 23. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. AWSComputeOptimizer 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
- 24. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Simplifyingcompute optimization AWS Compute Optimizer Identify optimal AWS compute resources for your workloads Mettle scans yourAWS infrastructure and uses machine learning to automatically identify optimalAWS resources for your workloads Identifies workload characteristics and profile based on the data gathered Matches the resource requirements of your workloads to optimalAWS 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
- 25. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Easytochoose withAWSComputeOptimizer 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
- 26. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved.
- 27. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. OptimizingAmazon EC2costand capacity Pricing Capacity Capacity management made easy on the broadest and deepest compute platform
- 28. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. the second Amazon EC2purchaseoptions savings of up to 90%a significant discount more flexibility
- 29. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. TooptimizeAmazon EC2,combine purchaseoptions RIs or a Savings Plan Spot for fault-tolerant, flexible, stateless workloads On-Demand
- 30. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. 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! Saveup to 90%using EC2Spot Instances
- 31. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. 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 WhySpot Instances?
- 32. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. FlexibilityiskeytosuccessfulSpotusage Instance flexible Time flexible Region flexible
- 33. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Minimal interruptions Check for 2-minute interruption notification via instance metadata orAmazonCloudWatch events, and automate by Checkpointing Draining from ELB Using stop-start and hibernate to restart faster InterruptionhandlersforAmazonECSandAmazonEKS 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) HandlingSpot interruptions Less than 5% of Spot Instances were interrupted in the last 3 months
- 34. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. OptimizingAmazon EC2costand capacity Pricing Achieve optimal price/performance with different purchase models Capacity
- 35. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. UsingAmazon EC2AutoScaling 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 https://ec2spotworkshops.com/running-amazon-ec2-workloads-at-scale.html
- 36. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Amazon EC2Spot Instancepools 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
- 37. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. ASG capacity-optimizedallocation 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 $ $$$ $$
- 38. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. ASG lowest-priceallocationstrategy 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 $ $$$ $$
- 39. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. TooptimizeAmazon EC2,combine purchaseoptions RIs or a Savings Plan Spot for fault-tolerant, flexible, stateless workloads On-Demand
- 40. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Before:MultipleASGstouseSpot,On-Demand,andRIstogether m4.large SpotASG 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
- 41. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Then:Spot,On-Demand,andRIsinasingleASG 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 singleASG SingleASGAvailability Zone 1 Availability Zone 2 Availability Zone 3
- 42. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. 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,andRIsinasingleASGwithweights
- 43. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Amazon EC2Spot Instancepools 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
- 44. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. APIparameters "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. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. APIparameters "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. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. APIparameters "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)
- 47. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. APIparameters "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)
- 48. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. APIparameters "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)
- 49. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. APIparameters "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)
- 50. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Worker fleetreference architecture Producer node (OD or RI) Producer node (Spot) Producer fleet Amazon EC2 Auto Scaling Consumer node (OD or RI) Consumer node (Spot) Consumer fleet Amazon EC2 Auto Scaling Amazon Simple Queue Service
- 51. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. "MixedInstancesPolicy": { "LaunchTemplate": { "LaunchTemplateSpecification": { "LaunchTemplateName": ”MyLaunchTemplate" }, "Overrides": [ { "InstanceType": "m4.4xlarge" }, { "InstanceType": "m5.4xlarge" }, { "InstanceType": "c5d.4xlarge" }, { "InstanceType": "m5d.4xlarge" }, { "InstanceType": "c4.4xlarge" } ] }, "InstancesDistribution": { "OnDemandAllocationStrategy": "prioritized", "OnDemandBaseCapacity": 0, "OnDemandPercentageAboveBaseCapacity": 30, "SpotAllocationStrategy": "lowest-price", "SpotInstancePools": 2 } } Producer fleetconfiguration AZ1 and AZ2 Desired – 50 Min – 20 Max – 80 30% On-Demand (15) 70% Spot (35)
- 52. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. "MixedInstancesPolicy": { "LaunchTemplate": { "LaunchTemplateSpecification": { "LaunchTemplateName": ”MyLaunchTemplate" }, "Overrides": [ { "InstanceType": "m4.4xlarge" }, { "InstanceType": "m5.4xlarge" }, { "InstanceType": "c5d.4xlarge" }, { "InstanceType": "m5d.4xlarge" }, { "InstanceType": "c4.4xlarge" } ] }, "InstancesDistribution": { "OnDemandAllocationStrategy": "prioritized", "OnDemandBaseCapacity": 250, "OnDemandPercentageAboveBaseCapacity": 0, "SpotAllocationStrategy": "lowest-price", "SpotInstancePools": 2 } } Consumer fleetconfiguration AZ1 and AZ2 Minimum On-Demand (250) Min – 200 Max – 350 On-Demand Base – 250 0% On-Demand (0) 100% Spot (50) Desired – 300
- 53. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Machine learning DevOps—CI/CD Websites and web applications Workloads onAWS
- 54. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Machinelearning (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
- 55. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Machine learning DevOps—CI/CD Websites and web applications Workloads onAWS
- 56. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. 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
- 57. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. CI/CDreference 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)
- 58. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Machine learning DevOps—CI/CD Websites and web applications Workloads onAWS
- 59. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Websitesand 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
- 60. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Running webapplications withAmazon ECSon EC2Spot Session state data Availability Zone A Availability Zone B Amazon EC2 Auto Scaling ECS container 1 ECS container 2 ECS container 1 ECS container 2
- 61. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. AWS FargatewithEC2Spot 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
- 62. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. Key takeaways 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 Spot Instances Auto Scaling and Savings Plans AWS Compute Optimizer
- 63. © 2020, AmazonWeb Services, Inc. or its affiliates. All rights reserved. LearncomputewithAWSTrainingandCertification 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
- #3 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
- #5 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
- #6 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.
- #11 1/ Previously, you had to reference multiple data sources and test multiple instance types before selecting the best instance type for your workload. You had to repeat this selection process as workloads evolved and new EC2 instance types and features were released. 2/Now you have a single source of truth for the latest instance types, attributes, regional and zonal offerings, and pricing. 3/ You can get started by defining your hardware requirements and reviewing the set of instance types which meet these requirements. You can further compare the hardware attributes, pricing, and availability of each instance type if needed. Then you can select and launch an instance, aliased by creating an SSM parameter, or saved in a launch template to be launched later or referenced in existing automation. 4/ This new experience makes it quicker and easier for us to find and compare different instance types, project costs, and select an instance type that you are confident will give you the performance within budget
- #14 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/
- #19 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.
- #20 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.
- #21 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.
- #23 Mention the T-family here on how they are burstable.
- #24 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.
- #25 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.
- #26 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
- #28 How to purchase EC2 How to optimize compute for savings and scale
- #29 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
- #30 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
- #31 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
- #32 Launching Spot workshop for a demo: Demo: https://ec2spotworkshops.com/launching_ec2_spot_instances.html 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
- #33 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
- #34 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/
- #35 Pay for what you need, but have the option to scale in and out when needed
- #36 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”
- #38 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
- #40 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
- #41 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
- #42 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
- #43 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
- #46 Prioritised is the only option, it will use the first instance in list, try to fill, then only moves to 2nd type, etc
- #50 How many of the specified overrides to use as pools
- #54 Let’s take a look at few real-life scenarios. See concrete examples to get started with cost and capacity optimization.
- #55 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
- #56 [Poll]How many of you run your CI/CD pipeline on AWS [Poll] What build tools are you using today? Jenkins? Bamboo?
- #57 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
- #58 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
- #59 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?
- #60 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
- #61 Architecture of a web app running on containers behind an elastic load balancer The ELB automatically routes incoming web traffic across a dynamically changing number of instances. Optimize Auto Scaling Group depending on application demand Use Spot to address fluctuations - with your base of RIs and a bit of On-Demand
- #62 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.
- #63 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
- #64 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