Autoscaling Based on Business Hours: Getting Ahead of Traffic

In the world of cloud computing, most autoscaling reacts only after CPU or memory thresholds are hit. Instead of waiting until your system is under stress, imagine being able to scale up your resources in anticipation of a traffic surge. This proactive strategy (schedule based autoscaling) can significantly improve performance and save money.

If your business exhibits predictable traffic patterns, such as team logins at 9 AM or customer activity during lunch breaks, you have an opportunity to align your scaling with these trends. Rather than keeping your entire infrastructure running at full capacity 24/7, you can reduce costs by scaling down during low-traffic periods and ramping up when needed.

Why Autoscaling Based on Business Hours Makes Sense

Typically, autoscaling solutions respond only when real-time demand spikes, which means your system is already working hard. In contrast, planning ahead by scheduling scaling events based on your known business hours can prevent overload and optimize resource usage. For instance, if your development team logs off at 6 PM, why continue running all development servers overnight? Shutting them down for 12 hours can cut your EC2 instance costs by 50%!

Key advantages to autoscaling schedules include:

• Cost Efficiency: Reducing the number of active instances during off-hours avoids unnecessary expenses.

• Optimized Performance: Ensuring resources are available ahead of peak demand maintains smooth operations.

• Proactive Resource Management: Anticipating usage patterns allows you to prepare for demand rather than reacting to it.

This approach aligns your infrastructure with actual needs, ensuring that resources are available when necessary and idle when they are not.

A Real-World Example: Scaling Down Dev and Sandbox Environments

Consider a scenario in which your production environment runs continuously while your development and sandbox environments only operate during business hours. During the day, these environments support code tests and debugging sessions. Once work is done, they remain active and continue to incur costs, even if no one is using them.

For example, assume your development environment consists of 50 m5.large instances at around $0.096 per hour. Running these instances 24 hours a day results in:

• 50 instances x $0.096/hour x 24 hours = $115.20 per day

• Approximately $3,456 per month

Now, if autoscaling based on business hours reduces the runtime to 9 hours a day (from 9 AM to 6 PM), the cost drops significantly:

• 50 instances x $0.096/hour x 9 hours = $43.20 per day

• Roughly $1,296 per month

This change saves about $2,160 per month on development resources alone. Similarly, imagine a sandbox environment with 20 c5.large instances at $0.085 per hour. Running 24/7 would cost:

• 20 instances x $0.085/hour x 24 hours = $40.80 per day (approximately $1,224 per month)

Scaling down to 9 hours per day reduces that to:

• 20 instances x $0.085/hour x 9 hours = $15.30 per day (around $459 per month)

This adjustment saves about $765 per month for sandbox resources, resulting in significant overall cost savings.

How to Set It Up in Terraform

Implementing business-hours-based autoscaling is simpler than you might think. Start by defining your autoscaling group to manage the scaling of your instances according to your schedule.

resource "aws_autoscaling_group" "example_asg" {
  launch_configuration = aws_launch_configuration.example_lc.id
  min_size             = 2
  max_size             = 50
  desired_capacity     = 10
  vpc_zone_identifier  = ["subnet-xxxxxxx"]

  tag {
    key                 = "Name"
    value               = "autoscaling-example"
    propagate_at_launch = true
  }
}

Next, create a scaling schedule. In this example, the system scales up at 9 AM and scales down at 6 PM (using UTC time).

resource "aws_autoscaling_schedule" "scale_up" {
  scheduled_action_name  = "scale-up-for-business"
  min_size               = 10
  max_size               = 50
  desired_capacity       = 10
  recurrence             = "0 9 * * *"  # Scale up at 9 AM UTC
  autoscaling_group_name = aws_autoscaling_group.example_asg.id
}

resource "aws_autoscaling_schedule" "scale_down" {
  scheduled_action_name  = "scale-down-after-hours"
  min_size               = 1
  max_size               = 10
  desired_capacity       = 1
  recurrence             = "0 18 * * *"  # Scale down at 6 PM UTC
  autoscaling_group_name = aws_autoscaling_group.example_asg.id
}

This configuration automatically adjusts the number of instances based on your business hours. You can further customize these settings to match your unique requirements or extend this setup to other environments such as production or batch processing.

Why Schedule Based Autoscaling Makes Sense

Autoscaling based on business hours offers multiple benefits beyond just cost savings. It allows you to match your resource allocation with actual usage patterns, ensuring that you are prepared for peak demand without wasting money on idle infrastructure (especially non-production infrastructure!).

Additional advantages include:

• Cost Efficiency: By reducing active resources during off-peak hours, you avoid overspending.

• Optimized Performance: Scaling up in advance prevents performance issues during high-traffic periods.

• Proactive Resource Management: Planning ahead based on known business patterns improves overall system reliability.

By shifting from a reactive to a proactive scaling model, you not only enhance performance but also achieve significant operational savings.

Final Thoughts On Schedule Based Scaling

Adopting business-hours-based autoscaling represents a smart, proactive approach to managing cloud infrastructure. Rather than reacting to sudden traffic spikes, you prepare your system in advance, ensuring that resources align with real-world usage. This strategy leads to both cost savings and improved performance.

Thanks to Terraform, implementing this dynamic autoscaling is straightforward. Experiment with various schedules and monitor your savings as your infrastructure becomes more efficient and responsive. Start scaling smarter and experience the benefits of a leaner, more optimized cloud setup.