What Is Autoscaling? Mastering Autoscaling for Peak Efficiency

I once complimented a photographer friend by saying, “You must have a great camera.” I could tell he was offended, but I couldn’t tell why. Then he explained, “The camera does just 10 percent of the work.” That stuck with me. Now that I’m a web developer and a web hosting expert, his comment makes even more sense.

I’ve realized people love polished apps and sites but rarely think about what keeps them running. Sure, they know about servers, but do they understand the impact of features like autoscaling? This function can make or break your app or website.

Autoscaling, short for automatic scaling, refers to the ability of cloud servers to automatically adjust to the load placed on them. Autoscaling increases or decreases resources as needed.

Today, I’ll focus on this often-overlooked feature and how it influences performance. By the time you’re done reading this, you’ll have so much respect for autoscaling as a feature and see what it can do when set up correctly.

Every great lesson starts with the basics. Autoscaling 101 is no exception.

When I talk about auto-scaling, I’m referring to an automatic process. That explains the keyword “auto.” The biggest benefit here is that the system automatically adjusts its resources as needed.

For instance, you need more memory to accommodate high traffic. So instead of manually adjusting these resources, you automate everything. That way, you won’t need to stick around to monitor what resources to add and where. That also frees up your precious time.

Scaling isn’t just about throwing in more resources when needed. It’s actually a well-calculated process.

First, the system needs to figure out what resource to auto-scale. It could be the CPU, memory, or even latency. In this case, I’ll go with the CPU.

Your eCommerce website advertising discounts on Black Friday will likely get a lot of traffic around that time. You need more CPU to handle that traffic. Otherwise, the website risks crashing at any moment, leaving you with a lot of angry customers and lost profits.

In 2018, Lowes’ website crashed on Black Friday due to increased customer traffic. The company actually had to cancel online sales until the day after. It just didn’t have the systems to handle this traffic.

Was it unexpected? Yes or no, that doesn’t really matter here. Did the company miss out on potential customers? No doubt about that.

Imagine a situation where you have to close your online shop on a day you should’ve maximized your profits. And for a company of this size, we’re talking about millions of dollars — that’s not pocket change.

Autoscaling isn’t unidirectional. It can travel vertically or horizontally. Regarding logic, you can choose between predictive and scheduled autoscaling. Let me explain.

Horizontal Scaling (Scale-Out/In)

With horizontal scaling, you’re basically adding or removing servers. If you add more servers, that’s called scaling out. Scaling in is the opposite — you remove servers when they’re no longer needed.

The whole point of this system is to make sure you’re distributing resources on a needs-based basis. That way, you prevent waste.

Think of two cities within a county as examples. City A just witnessed a spike in crime. City B has been relatively safe. Say you’re the head of police in that county. You’ll obviously want more officers patrolling City A than City B. I’m not an expert in matters of crime, but I hope you get the whole point of needs-based resource allocation.

Vertical Scaling (Scale Up/Down)

Vertical scaling is the opposite. It focuses more on the power of an existing system.

You don’t add more servers. Instead, you increase the power of whatever setup you have in place. One way of doing this is by upgrading the memory, CPU, storage, or anything along those lines when you need it.

If you have a cloud hosting plan, you may be able to do this through your hosting provider‘s dashboard.

Of course, with some types of servers, such as shared or dedicated servers, there are limits to how high you can scale vertically. Eventually, you may need to scale out.

Predictive Autoscaling

The problem with vertical and horizontal scaling is that you must actively monitor the server to know when and what to scale. That’s not impossible, but you basically become a nanny to the server.

It’s like watching a pot of water on the stove. It’s my least favorite thing to do. You blink; it boils over. Even worse, you have to clean up the mess!

With predictive scaling, you don’t need to monitor the server. There’s a system that takes care of all that. It studies patterns from past data and then uses this information to predict when to scale.

Scheduled Autoscaling

Scheduled autoscaling works best in situations where traffic is pretty predictable. While working as a caregiver a few years ago, I was required to use an app called Therap to document my shift.

Most employees used the app between 7 AM and 11 PM, which were the normal working hours for a caregiving shift. Then, graveyard employees (the night shift staff) would clock in from 11 PM to 7 AM.

Day shifts were busier than night shifts, hence the increased number of employees at the facility. That makes sense since there wasn’t so much to do at night when the residents were asleep.

As expected, the app received more traffic during the day than at night. Because the traffic was predictable, the company would plan for more resources during the day as opposed to the night. That’s an example of scheduled autoscaling. You know what to expect and plan for it.

Autoscaling offers more than just stability. Let’s take a look.

• Cost optimization: In autoscaling, there’s something called pay-as-you-go pricing. What this means is that you only pay for the resources you actually need. That way, you won’t waste money on resources you didn’t use. It also prevents you from overequipping your systems with resources they won’t need.
• Improved application availability: This is probably the biggest advantage of autoscaling. I just couldn’t leave it out, even though I’ve talked about it already. Downtime or crashing is the least of your concerns when you set up autoscaling. It may happen, but that’ll likely have nothing to do with autoscaling.
• Better performance: This is basically what happens when your application is always available to its users. And it’s not just about availability; it’s about making sure that there are enough resources to handle the incoming traffic. Autoscaling achieves both objectives.
• Flexibility and elasticity: This is the byproduct of not needing any manual intervention. The server is free to decide when and how far to scale.
• Resilience and fault tolerance: Technical issues are bound to happen when dealing with technology. But when you have an automated scaling system, you can rest easy knowing that it’ll handle most, if not all, challenges.

The bottom line is that system stability is the most important benefit of autoscaling, but it’s definitely not the only one.

We’ve seen what autoscaling does. Now, let’s focus on where.

eCommerce Websites

Most eCommerce businesses can be unpredictable. One moment traffic is low, and the next thing you know, your product is the hottest thing in town, and your website needs to accommodate a huge spike in visitors.

For perspective, I already know where I’m planning to shop during this year’s Black Friday, and I know I’m not the only one making early plans to get the best deals when that time comes.

I can bet my last dollar that most major platforms already have some sort of autoscaling setup. So when traffic spikes around that time, these platforms should have enough resources to allow online shoppers to browse and checkout smoothly without experiencing slow load times or crashes.

As we saw earlier, once traffic slows down, autoscaling scales back resources. This also means that these businesses won’t need to pay for extra servers or resources when they don’t need them anymore.

Streaming Services

Streaming services like Netflix or Hulu also have some form of autoscaling. This happens mostly during peak times like weekends or when a new show goes viral.

Remember when weekends were for Netflix and chill? I can guarantee you that the folks over there at Netflix knew they were expecting more eyeballs over the weekend. And they had autoscaling already in place to prepare for the increased traffic. Then came Mondays. Ughh! I wish Mondays had a dislike button.

For streaming services, most Mondays are considered off-peak times, especially between mornings and afternoons. With fewer users online, autoscaling kicks in to reduce the number of active servers.

That’s how these streaming services keep operational costs low while maintaining insane profit margins.

SaaS Applications

SaaS applications are everywhere these days, and it’s no surprise to me that IT teams use autoscaling to manage their fluctuating resource needs.

Slack is an example of a Software as a Service (SaaS) that I use quite frequently and during a very specific period. It’s basically used for communication and file sharing between teams.

When I’m collaborating with a team, I usually log in anywhere from 9 AM to 5 PM, Monday through Friday. That’s the standard working hours across America.

Since my usage pattern is pretty predictable, Slack ensures that there are enough resources to keep the platform up and running during these hours.

Mobile and Web Applications

We interact with mobile applications differently and at different periods. Just think of how you use DoorDash or Uber, for example.

Most Uber drivers will tell you that the peak hours are usually morning and evening. For DoorDash or even Uber Eats, most people tend to order food during lunch and dinner hours.

Autoscaling helps these platforms handle the increased activity during these peak times. That way, the app remains responsive. Once the rush is over, by now you already know what happens.

IoT Platforms

The Internet of Things includes things like sensors, smart home gadgets, and industrial machines. They usually collect data from whatever devices they’re connected to.

If you’re wearing a smartwatch right now, that’s an IoT device. If you have a wireless security camera at your residence, that’s another example.

But here’s the thing about the Internet of Things — the frequency of data collection isn’t always constant. It depends on many factors.

Smart thermostats, for instance, may need to adjust their settings simultaneously when there’s a sudden weather change. Autoscaling would kick in to provide more resources for these devices and then scale down once the update is complete.

Think about it for a second. Autoscaling is just an action. In the English language, it’s a verb. It’s something servers do, but there are some tools and technologies responsible for the doing, if that makes sense. Let’s go into the details.

AWS Auto Scaling

Amazon Web Service is the world’s leading cloud service provider, so it’s not surprising that AWS Auto Scaling opens my list. It offers features like Application Auto Scaling, Elastic Load Balancing, and EC2 Auto Scaling.

These are tools that work together to automatically scale resources across your AWS ecosystem. You’ll find them in web apps, databases, containers — you name it.

Microsoft Azure Autoscale

Microsoft Azure Autoscale can scale vertically, horizontally, or even predictively. It also has features like Virtual Machine (VM) Scale Sets and Azure Kubernetes Service (AKS). Sounds confusing? No worries, I got you.

A VM is basically like one house. It comes with its own space and resources. The resources here are things like CPU, memory, and storage. You can run a full operating system inside this house. And since it’s isolated from others, it’s like having a private bedroom.

The isolation and independence are great for running one big application. You can even decide to go in this direction if you want to run multiple smaller apps. If you choose the latter, you’ll manage each VM individually.

Now let’s cross over to Kubernetes. It’s like managing a whole neighborhood. Instead of individual houses, you’re now managing lots of tiny apartments (we call these containers), all within different buildings.

The whole point of Kubernetes is to organize and manage all of these containers. You want to make sure they’re working as they should. If something goes wrong, you’ll automatically move them around.

Unlike virtual machines, Kubernetes is more focused on running lots of small, lightweight apps. You can even use them to run parts of an app that you can easily shift around.

Now back to Azure Autoscale, it works with Azure Monitor to monitor how your app is performing. It then automatically adjusts the resources based on certain limits you’ve set. For instance, if the app is using too much CPU or memory, the system automatically adds more resources so you won’t have to do it manually.

Google Cloud Autoscaler

Enter Google with its Cloud Autoscaler tool. It’s a little bit different from the others I’ve covered. Here’s why:

While other tools like AWS and Azure Autoscale can handle both virtual machines and containers, Google Cloud works best with Kubernetes. That alone makes it the perfect fit for apps that rely on lots of small, lightweight parts.

Fun fact: The idea of Kubernetes originally came from three engineers at Google: Joe Beda, Craig McLukie, and Brendan Burns. The trio wanted to create an open-source container management system to automate, scale, and manage software deployment.

Kubernetes Horizontal Pod Autoscaler (HPA)

The HPA in Kubernetes is like an automatic worker manager for your app. If your app is getting too busy and using up a lot of computer power, HPA steps in and adds more pods to help handle the extra load. These pods are what you’d consider extra workers.

When things slow down, it removes some workers to save resources. It’s perfect for apps where the work can change a lot throughout the day. Think along the lines of Netflix, Max Amazon Prime, and Hulu. We’ve seen that most traffic comes in the evenings or on weekends.

Third-Party Tools

There are also other options like KEDA or HashiCorp Nomad that you can use for autoscaling.

KEDA is like an add-on for Kubernetes. It helps applications automatically scale based on real-time events. The addon watches specific triggers, and then when something big happens, it scales up the resources to handle it. The trigger could be an incoming message, a database query, or any kind of workload.

Then there’s HashiCorp Nomad for managing and scaling apps across multiple servers. Note that it’s not just for Kubernetes; it works with different kinds of environments. Because of its flexibility, it’s often used by companies that have complex setups or use different types of cloud services.

Make no mistake; autoscaling is not smooth sailing. It’s also not just a “set it and forget it” solution. Let’s look at some possible challenges and considerations.

• Overprovisioning and costs: You’re probably wondering why the issue of overprovision is even a thing if autoscaling is an automatic process. Yes, it is. But don’t forget that you need to set autoscaling properly. If you don’t, you might end up with way more resources than you need. That means also spending more than you should’ve.
• Response time and latency: Again, autoscaling is automatic, but that doesn’t mean it’s instant. I’ve seen cases of delays between when a traffic surge hits and when autoscaling kicks in. This delay can slow down your website or application or even crash it. Having a buffer of extra resources can help prevent this.
• Complexity in managing state: By “state,” I mean any important data an app or service needs to remember. A good example is a database keeping track of user info. Scaling stateful services is trickier because you have to make sure that data stays safe. On the other hand, stateless services don’t need to hold onto information. Therefore, the latter is much easier to scale.
• Testing and predicting traffic patterns: It’s not always easy to predict how traffic will behave. This is especially true during sudden spikes. Humans sometimes don’t get such predictions right, let alone machines. You never know when your next TikTok marketing video will go viral. Your website’s server has no idea you even have such a video on TikTok. So when you’re dealing with cases like flash crowds or unexpected events, it’s important to make sure that your scaling policies can handle them without breaking your app or overloading your servers.
• Monitoring and observability: We talked about triggers. These are events that signal the automation process to begin. You need good monitoring tools to trigger the automation. The tools should track metrics like CPU usage, memory, and traffic patterns. If they can’t observe these aspects, they definitely won’t fine-tune the autoscaling process. That also means they won’t catch any potential problems before they become major.

My point? Autoscaling is powerful. However, it needs teamwork.

I’ll say this again: autoscaling itself can’t work magic if you don’t fine-tune it. What I’m about to discuss below will prove this point.

Start With Conservative Scaling Policies

It’s wise to begin with cautious scaling settings. The goal here is to make sure you don’t accidentally scale too much too fast. You don’t want to deal with unexpected expenses.

You should probably start with small, careful changes. This will let you find the right balance for your system without risking overspending.

Set Proper Thresholds

Next, shift your focus to server resources, such as CPU usage and memory. You want to make sure that the thresholds you set will trigger scaling at the right moments.

The ideal threshold should be high enough to avoid premature scaling. At the same time, it should also be low enough to prevent performance issues.

It’s all about finding the perfect balance. You want your system to autoscale when needed, not just randomly and for no good reason.

Use Predictive and Scheduled Autoscaling

You don’t need to be a genius to figure out predictive autoscaling. Some things or events are pretty much predictable. For example, it doesn’t take a genius to predict a spike in sales during specific events such as Christmas.

And it’s not just about predicting high-traffic events either; knowledge of the opposite is equally important. For example, it’s common knowledge that you’ll sell more winter jackets close to the winter than during the summer.

Once you’ve predicted when you’ll need to auto-scale, the next step is to figure out the schedule. Yes, autoscaling is great, but only with perfect timing. Scheduled autoscaling prepares your system for known busy times. The peak hours for DoorDash or Uber Eats I discussed earlier are good examples.

First, you predict these hours. Second, you schedule auto-scaling around these hours.

Monitor and Adjust Regularly

You don’t just set up autoscaling and go on with your business. I mean, you can, but it likely won’t get you the results you’re hoping for.

If you’ve ever planted anything, you know it takes more than just placing seeds into the soil. You need to know when and how frequently you should take care of the plant. You’ll need to plan for watering, pruning, fertilizing, and so on.

It’s the same story with autoscaling. Yes, predicting and scheduling work, but it’s not enough to get you the desired results.

You should continuously monitor how your system performs and then adjust your scaling policies based on historical data.

Remember, traffic patterns change over time. You wouldn’t even know this without monitoring your server.

Right-Sizing Instances

For starters, right-sizing is very different from conservative scaling. I know that’s a little bit confusing since they sound more or less like the same thing, but there’s a difference.

With conservative scaling, you’re being cautious with how you configure the autoscaling process itself. You just don’t go all the way in. You start slow and then make small adjustments along the way. The reason for using this tactic is to prevent you from adding too many resources too quickly.

Of course, having too many resources is way better than having little or no resources at all. But also keep in mind that the more resources there are, the higher the cost. You shouldn’t have to use resources where and when you don’t need them.

I hope you now see that scaling isn’t just about sliding a scale to add more or less resources. It involves a lot of optimization and fine-tuning to get it right. It’s one of those things I strongly recommend you get expert help with (if you’re new to it).

However, once you’ve figured it out, it can make your application far more performant while saving you the pain of wasted resources and downtime. Through my guide, you have a better understanding of what it takes to achieve automated scaling and, more importantly, the desired results.