Table of Contents
Instance Strategy ๐ป
Selecting the Right Instance Type
# Get instance type recommendations based on CloudWatch metrics
aws compute-optimizer get-ec2-instance-recommendations \
--instance-arns arn:aws:ec2:region:account-id:instance/i-1234567890abcdef0
# List available instance types with specific criteria
aws ec2 describe-instance-types \
--filters "Name=processor-info.supported-architecture,Values=arm64" \
--query "InstanceTypes[?MemoryInfo.SizeInMiB>=8192]"
Generation Comparison Matrix:
Feature | M5 | M6i | M6g (Graviton2) |
---|---|---|---|
vCPUs | Up to 96 | Up to 128 | Up to 64 |
Memory | Up to 384 GB | Up to 512 GB | Up to 256 GB |
Network | Up to 25 Gbps | Up to 37.5 Gbps | Up to 25 Gbps |
Price/Hour* | $0.192 | $0.208 | $0.168 |
*Prices are example only, check current AWS pricing
Graviton Adoption Strategy
# CloudFormation template for mixed-architecture deployment
Resources:
GravitonLaunchTemplate:
Type: AWS::EC2::LaunchTemplate
Properties:
LaunchTemplateData:
InstanceType: c6g.large
ImageId: ami-0123456789abcdef0 # ARM64 AMI
UserData:
Fn::Base64: !Sub |
#!/bin/bash
# Optimize for ARM64
amazon-linux-extras install epel
yum install -y tuned
tuned-adm profile throughput-performance
Cost Optimization ๐ฐ
Savings Plans vs Reserved Instances
import boto3
def analyze_usage_patterns():
client = boto3.client('ce')
response = client.get_cost_and_usage(
TimePeriod={
'Start': '2024-01-01',
'End': '2024-01-31'
},
Granularity='MONTHLY',
Metrics=['UnblendedCost', 'UsageQuantity'],
GroupBy=[
{'Type': 'DIMENSION', 'Key': 'INSTANCE_TYPE'},
{'Type': 'DIMENSION', 'Key': 'REGION'}
]
)
return response
Spot Strategy Implementation
{
"SpotFleetRequestConfig": {
"IamFleetRole": "arn:aws:iam::account-id:role/spot-fleet-role",
"AllocationStrategy": "capacityOptimized",
"TargetCapacity": 10,
"SpotPrice": "0.50",
"LaunchSpecifications": [
{
"InstanceType": "c5.large",
"WeightedCapacity": 1
},
{
"InstanceType": "c5n.large",
"WeightedCapacity": 1
},
{
"InstanceType": "c6i.large",
"WeightedCapacity": 1
}
]
}
}
Performance Tuning โก
EBS Optimization
# Enable EBS optimization
aws ec2 modify-instance-attribute \
--instance-id i-1234567890abcdef0 \
--ebs-optimized "Value=true"
# Create optimized volume
aws ec2 create-volume \
--volume-type io2 \
--iops 50000 \
--size 1000 \
--availability-zone us-west-2a \
--encrypted
Network Performance Configuration
# Enable enhanced networking
aws ec2 modify-instance-attribute \
--instance-id i-1234567890abcdef0 \
--sriov-net-support simple
# Configure placement group
aws ec2 create-placement-group \
--group-name HighPerformanceCompute \
--strategy cluster
Architecture Patterns ๐๏ธ
Multi-AZ High Availability Setup
# Terraform configuration
resource "aws_autoscaling_group" "web_asg" {
name = "web-asg"
desired_capacity = 6
max_size = 10
min_size = 4
target_group_arns = [aws_lb_target_group.web_tg.arn]
vpc_zone_identifier = [
aws_subnet.private_1a.id,
aws_subnet.private_1b.id,
aws_subnet.private_1c.id
]
mixed_instances_policy {
instances_distribution {
on_demand_base_capacity = 2
on_demand_percentage_above_base_capacity = 50
spot_allocation_strategy = "capacity-optimized"
}
launch_template {
launch_template_specification {
launch_template_id = aws_launch_template.web_lt.id
version = "$Latest"
}
override {
instance_type = "c6i.large"
}
override {
instance_type = "c6a.large"
}
override {
instance_type = "c6g.large"
}
}
}
}
Monitoring & Analytics ๐
CloudWatch Dashboard Configuration
{
"widgets": [
{
"type": "metric",
"properties": {
"metrics": [
["AWS/EC2", "CPUUtilization", "InstanceId", "i-1234567890abcdef0"],
[".", "NetworkIn", ".", "."],
[".", "NetworkOut", ".", "."],
[".", "DiskReadOps", ".", "."],
[".", "DiskWriteOps", ".", "."]
],
"period": 300,
"stat": "Average",
"region": "us-west-2",
"title": "EC2 Performance Metrics"
}
}
]
}
Cost Anomaly Detection
def setup_cost_anomaly_detection():
client = boto3.client('ce')
response = client.create_anomaly_monitor(
MonitorName="EC2CostMonitor",
MonitorType="DIMENSIONAL",
MonitorDimension="SERVICE",
MonitorSpecification={
"Services": ["Amazon Elastic Compute Cloud - Compute"]
}
)
client.create_anomaly_subscription(
SubscriptionName="EC2CostAlerts",
Threshold=200.0,
ThresholdExpression="ABSOLUTE_VALUE",
Frequency="DAILY",
Subscribers=[
{
'Type': 'EMAIL',
'Address': '[email protected]'
}
]
)
Best Practices Checklist โ
Performance Optimization
- [ ] Use enhanced networking for high throughput
- [ ] Implement proper placement groups
- [ ] Configure EBS IOPS appropriately
- [ ] Enable monitoring and alerting
- [ ] Use latest generation instances
Cost Management
- [ ] Implement proper tagging strategy
- [ ] Use Savings Plans for predictable workloads
- [ ] Leverage Spot Instances for flexible workloads
- [ ] Schedule dev/test environments
- [ ] Regular right-sizing analysis
Case Study: High-Traffic Web Application
Scenario: E-commerce platform handling 1M daily users
Solution Architecture:
- 1. Multi-AZ deployment with Auto Scaling
- 2. Mix of On-Demand and Spot Instances
- 3. Graviton2 instances for API servers
- 4. Reserved Instances for baseline capacity
- 5. CloudWatch monitoring with custom metrics
Results:
- 40% cost reduction
- 99.99% availability
- 30% performance improvement
- Enhanced disaster recovery capability
Future Considerations ๐ฎ
1. Graviton3 Migration
- 25% better compute performance
- 60% better price-performance ratio
- Native ARM64 application support
2. Sustainability Optimization
- Use Graviton for better energy efficiency
- Implement proper instance scheduling
- Choose regions with renewable energy
3. Emerging Technologies
- AWS Nitro Enclaves for security
- AWS Wavelength for edge computing
- AWS Outposts for hybrid deployments
Additional Resources ๐
1. AWS Documentation
2. Tools
Keep optimizing! ๐