How can I configure the cluster to handle the peak loads efficiently during the time minimizing the cost?

195    Asked by debbieJha in AWS , Asked on Apr 22, 2024

 I am currently developing a scalability web-based application on the platform of AWS which can experience varying traffic patterns throughout the day. Discuss the factors that I should consider when selecting AWS EC2 Instance types for creating a resilient and cost-effective auto-scaling cluster. How can I configure the cluster to handle the peak loads efficiently while the time of minimizing the cost during low traffic periods? 

Answered by David

 In the context of AWS, here is the approach given which would discuss factors for selecting AWS EC2 instance types and configuring a resilient and cost-effective auto-scaling cluster.

Factors for selecting AWS EC2 Instance types

You should consider the workload characteristics. For this, you can consider COU, memory, and the requirements of Input and output of your system.

You should also consider traffic patterns. For this, you can analyze the expected traffic patterns to determine the scaling needs.

You should also consider the cost efficiency. You can balance the performance requirements with costs by choosing Instance types based on workload demand.

These are some factors that should kept in mind by anyone for selecting AWS EC2 instance types and configuring a resilient and cost-effective auto-scaling cluster.

Configuration of a resilient and cost-effective auto-scaling cluster

You can use the AWS AWS auto-scaling to automatically adjust the number of EC2 Instances based on the demand.

You should also define the scaling policies based on metrics like CPU utilization, network traffic, or custom-based application metrics.

You can configure ASG to use multiple instance types Instances for low-traffic, and traffic Instance by using the mixed Instance policy.

You can consider using spot Instances for cost savings during non-peak hours, combined with demands or reserved instances for stability during the time of Peak times.

You can implement the health check to monitor the instance status and replace unhealthy Instances automatically.

Import boto3
# Initialize AWS Auto Scaling client
Autoscaling = boto3.client(‘autoscaling’)
# Define launch configuration for different instance types
Launch_configurations = [
    {
        ‘InstanceType’: ‘t3.micro’, # Small instance type for low traffic
        ‘SpotPrice’: ‘0.005’, # Spot price for cost savings
        ‘WeightedCapacity’: 1, # Weight for mixed instances policy
        # Other configuration parameters (e.g., AMI, security groups)
    },
    {
        ‘InstanceType’: ‘c5.large’, # Large instance type for peak traffic
        ‘SpotPrice’: ‘0.02’, # Higher spot price for stability
        ‘WeightedCapacity’: 4, # Weight for mixed instances policy
        # Other configuration parameters
    },
]
# Create launch configurations
For config in launch_configurations:
    Response = autoscaling.create_launch_configuration(
        LaunchConfigurationName=f’lc-{config[“InstanceType”]}’,
        ImageId=’ami-12345678’, # Replace with actual AMI ID
        InstanceType=config[‘InstanceType’],
        SpotPrice=config[‘SpotPrice’],
        # Add other configuration options as needed
    )
# Create Auto Scaling group with mixed instances policy
Response = autoscaling.create_auto_scaling_group(
    AutoScalingGroupName=’my-asg’,
    LaunchConfigurationName=’lc-t3.micro’, # Default launch configuration
    MinSize=1,
    MaxSize=10,
    DesiredCapacity=2,
    MixedInstancesPolicy={
        ‘LaunchTemplate’: {
            ‘LaunchTemplateSpecification’: {
                ‘LaunchTemplateName’: ‘lt-mixed-instances’,
                ‘Version’: ‘1’
            }
        },
        ‘InstancesDistribution’: {
            ‘OnDemandPercentageAboveBaseCapacity’: 50,
            ‘SpotAllocationStrategy’: ‘lowest-price’
        }
    }
)

Your Answer

Interviews

Parent Categories