AWS remains the go-to choice for over half of all cloud users worldwide, thanks to its unmatched breadth of services. With more than 200 fully-featured offerings, AWS provides businesses with the tools to build, scale, and innovate with ease.
However, managing these resources effectively while keeping cloud costs in check is easier said than done. What starts as a manageable deployment can quickly spiral into complexity and inefficiency as operations scale, leading to wasted spend and underutilized capacity.
This is where AWS Auto Scaling steps in. By automating resource management, it ensures your cloud deployments remain optimized, minimizing waste while maintaining performance.
In this guide, we’ll explore how AWS Auto Scaling works, its key features, and actionable steps to utilize it for both cost control and operational efficiency.
What Is AWS Auto Scaling?
Image Source: AWS user guide
AWS Auto Scaling is a built-in service that automatically monitors and adjusts your cloud resource capacity in real time, eliminating the need for manual intervention.
Using advanced data analytics and historical usage predictions, AWS Auto Scaling dynamically scales resources up or down to maintain reliable performance at the lowest possible costs.
AWS Autoscaling supports a variety of AWS workloads, including EC2 instances, Spot Fleets, containerized applications, and other compute services. With customizable scaling plans, organizations can ensure their entire cloud architecture operates efficiently.
Is AWS Auto Scaling free?
Yes, AWS Auto Scaling itself is free to use. However, the resources it manages like EC2 instances, Spot Fleets, or other services are billed as per their usage.
This means that while you won’t incur any direct costs for using the scaling service, it will influence your overall AWS bill by adjusting resource usage in response to demand. The good news is that AWS Auto Scaling is designed to optimize cloud costs by right-sizing resources, helping you achieve a balance between performance and expenses.
Benefits of Using Auto Scaling
With the help of automated application monitoring and dynamic resource allocation, businesses get a range of benefits via AWS auto scaling, including:
Scalability and performance
With AWS Auto Scaling, your applications can handle traffic spikes more effectively. Autoscaling automatically adjusts the number of resources to keep performance steady and CPU usage optimized.
Whether it’s a promotional event or a sudden surge in demand, it ensures your workloads don’t get overloaded and your users always have a smooth experience.
Improved application availability
AWS Auto Scaling keeps your applications running smoothly by detecting unhealthy instances and replacing them automatically, no manual intervention needed. By distributing instances across multiple Availability Zones and using load balancers, Auto Scaling minimizes the risk of downtime and ensures high availability, even during unexpected failures. Automated health checks and strategic resource distribution mean your services stay online, delivering a consistent user experience and peace of mind.
Efficient cost optimization with automation
Rather than needing to manually calculate resource needs or analyze multiple management accounts, Auto Scaling ensures you’re only paying for the compute capacity you actually need at any given time.
Below are some of the cost optimization benefits Auto Scaling provides:
- Reduced cloud waste: Overprovisioning your cloud services quickly adds up over time, meaning you end up paying more than you need to. Auto Scaling automatically scales resource availability based on your business’s current demands, minimizing the collection of idle resources that lead to higher cloud bills.
- Predictable cost management: Planning cloud budgets long-term is difficult without actively tracking your resource needs month-to-month. Auto Scaling, in combination with tools like AWS Cost Explorer, simplifies this process, giving you the visibility you need to better understand and react to your resource usage patterns.
Once you’ve defined your scaling policies and consumption targets, Auto Scaling takes on the heavy lifting of regularly monitoring and adjusting your launch configuration if and when needed.
The Types of AWS Auto Scaling Plans
Choosing the right scaling plan can make all the difference in balancing performance and cost efficiency, let’s break down how AWS Auto Scaling’s options help you achieve that:
Dynamic scaling
Dynamic scaling is a configuration within AWS Auto Scaling that continuously adjusts resource capacity based on real-time demand, as monitored by Amazon CloudWatch metrics. Users define a minimum and maximum capacity range for their resources, creating a boundary within which dynamic scaling operates.
When CloudWatch metrics, such as CPU utilization or request rate, exceed or fall below predefined thresholds, dynamic scaling automatically triggers scaling activities. For example, if usage surpasses the defined upper threshold, additional EC2 instances are launched to handle the load. Conversely, if usage drops below the lower threshold, instances are terminated to reduce excess capacity.
Dynamic scaling ensures responsiveness by constantly evaluating metric changes and implementing scaling actions without user intervention, using rules defined by scaling policies. It operates in near real-time, leveraging thresholds and alarms to maintain resources within the defined limits, ensuring optimal performance and infrastructure efficiency.
Image Source: AWS user guide
When configuring EC2 Auto Scaling, cloud users have multiple types of dynamic scaling policies and automation permissions they can choose from:
- Target tracking scaling: This type of dynamic scaling focuses on establishing and maintaining a specified target metric value. In this policy type, users identify a target cloud resource value – for example, establishing a fixed CPU load percentage regardless of application load changes. Auto Scaling will then scale out or increase capacity as needed to maintain that target.
- Simple Scaling: Simple scaling policies are basic Auto Scaling plans that require users to define only a single triggerable action based on a specific CloudWatch alarm. While easy to define, these policies are best suited for applications with predictable traffic patterns due to their limited configurations.
- Step scaling: Step scaling policies offer users more granular control over how Auto Scaling executes triggers. Instead of only having one predefined parameter, users can create multiple actions based on different metric ranges. For example, businesses may want to stipulate adding only one instance if resource consumption is at 70-80% or multiple instances if it reaches 80-95%.
Predictive scaling
Image Source: AWS user guide
Predictive scaling is another type of Auto Scaling plan that leverages historical load data to predict ongoing cloud capacity needs. Auto Scaling proactively adjusts instance configurations before consumption needs shift by measuring past workload patterns and pre-established seasonal demands.
The main difference between predictive and dynamic Auto Scaling plans is in the data they use to execute their automation. While dynamic scaling leverages real-time cloud data when monitoring for triggerable capacity changes, predictive scaling uses past data to forecast demand needs and execute necessary provision changes proactively.
Predictive scaling is most applicable for applications and services that have cyclical traffic patterns or take a longer time to initialize. It reduces the lead time for capacity changes and avoids potential bottlenecks that can happen with reactive scaling methods.
Scheduled Scaling
Scheduled scaling allows businesses to plan resource adjustments at specific times based on expected demand. For instance, if high traffic is anticipated during weekday mornings, resources can be scaled out in advance and scaled back during off-peak hours. This method is ideal for predictable workloads where demand patterns are well understood.
While these three are the primary types of Auto Scaling plans, additional configurations, like manual scaling or scaling based on custom metrics, provide further flexibility to meet unique workload requirements. Read more about them here: choosing your scaling method.
How Does AWS Auto Scaling Work?
Below, we’ll walk you through exactly how AWS Auto Scaling works.
Define your scaling targets
First, you identify your scalable targets, which are the resources AWS Auto Scaling can adjust. For example, this should be an AWS Auto Scaling group or the individual tasks within an Amazon ECS service. You decide the minimum and maximum group size for these targets to maintain the balance between performance and cost.
You can choose from various scaling options, like schedule-based scaling for predictable workload changes, demand-based scaling responding to real-time use, and proactive scaling using predictive analytics.
Monitoring and decision-making
AWS Auto Scaling uses Amazon CloudWatch to monitor your application’s metrics in real time. You create CloudWatch alarms that trigger a response when specific thresholds are reached. Based on these alarms and the predefined scaling strategies, Auto Scaling determines when to scale out (add resources) and scale in (remove resources).
This process hinges on the effective monitoring of key performance indicators like CPU usage, network traffic, and application response times.
Auto Scaling assesses these metrics against your set thresholds to make informed decisions about resource adjustments. When your CloudWatch metrics exceed or fall below these thresholds, it triggers scaling actions, either adding resources to handle increased load or removing excess resources to optimize costs.
Scaling actions
Following your scaling plan, AWS Auto Scaling performs scaling activities. When there is an increase in demand or load, it’ll automatically scale out. When the demand decreases, it will scale in.
AWS Auto Scaling actions are guided by predefined scaling policies and templates. These templates detail instance configurations, ensuring newly launched instances during scaling out meet the specific requirements of your workload.
On the other hand, when scaling in, these templates help determine which number of instances to terminate, based on criteria like least CPU utilization or oldest launch time.
Termination policies
Image Source: AWS user guide
Finally, AWS Auto Scaling uses termination policies to determine which instances to terminate during a decrease in capacity. Such policies help make sure the instances terminated are the ones that minimize any negative impact on your application’s performance or availability.
Termination policies in AWS Auto Scaling are designed to maintain an optimal balance within your environment. When scaling in, these policies carefully select instances for termination based on criteria such as instance age, lifecycle, or utilization rates.
This strategic approach ensures the most critical or best-performing instances remain operational, preserving the integrity and efficiency of your application.
How AWS Auto Scaling Enhances Other AWS Services
Transitioning to cloud-based infrastructures, you may notice that workloads often exhibit cyclical patterns, where the demand for resources fluctuates. AWS Auto Scaling has proven itself to be crucial for managing these variances.
Auto Scaling helps users extract the most value out of their entire cloud computing stack while maximizing the performance and efficiency of each layer of their cloud architecture. AWS services supported by Auto Scaling include:
Amazon EC2 Auto Scaling
Amazon EC2 Auto Scaling lets businesses automatically add or remove compute capacities based on shifting needs. This means businesses don’t need to manually calculate their usage based on cloud billing reports or analyze ongoing changes based on daily, weekly, or monthly consumption changes.
Amazon Aurora
For database needs, particularly with Amazon Aurora, businesses can meet their Aurora workload requirements by applying Auto Scaling policies to various DB clusters. Aurora’s Auto Scaling triggers are easy to manage directly through the AWS Management Console, AWS CLI, or the Aurora Auto Scaling API.
Amazon RDS
Amazon RDS (Relational Database Service) gives AWS users an easy way to build and maintain relational databases. However, when managing multiple read-only workloads (“read replicas”) across an expansive infrastructure, manually maintaining and optimizing performance is both time-consuming and inefficient. Auto Scaling greatly improves this process, automating read replica optimization and making sure all resource-heavy applications have the resources they need to function properly.
Amazon RDS auto scaling works with both older and newer database instances and lets cloud users avoid having to manually reconfigure their database storage allotments over time. After establishing minimum/maximum storage thresholds, Auto Scaling takes care of the rest, keeping your databases running smoothly.
By optimizing your usage patterns and scaling appropriately, you can tap into savings for cyclical workloads. Each enhancement provides a direct tie-back to efficiency and reliability within AWS’s ecosystem, ensuring that resources precisely align with your needs.
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