Autoscaling Explained: A Complete Guide

This article will explain auto-scaling’s operation, significance, and typical applications for businesses. We’ll also examine the most popular Autoscaling solutions available, as well as any possible implementation-related difficulties. Lastly, we’ll go over some crucial factors to take into account when successfully developing an auto-scaling architecture.

What is Autoscaling?

Autoscaling represents a dynamic scaling method utilized for workloads deployed within a cloud environment. Leveraging the scalability inherent in cloud hosting, Autoscaling takes this flexibility to the next level. This approach allows for the automatic adjustment of allocated resources in response to fluctuations in workload demand, ensuring that performance requirements are consistently met. As demand for a particular workload fluctuates over time, Autoscaling seamlessly adapts the allocated resources, thereby optimizing resource utilization and enhancing overall efficiency.

Before the advent of auto-scaling, scaling workloads presented significant challenges. Manual allocation of resources to support a workload was prone to errors due to the unpredictable nature of demand fluctuations. It was challenging to accurately predict changes in demand or determine the precise amount of resources required to accommodate these changes. This uncertainty often resulted in either costly over-provisioning or potential service disruptions caused by under-provisioning. Auto-scaling addresses these issues by automatically adjusting the allocation of resources to match changes in demand. As demand increases or decreases, auto-scaling dynamically scales resources up or down, ensuring optimal resource utilization and maintaining consistent performance levels.

How does Autoscaling work?

Prior to understanding auto-scaling, keep in mind that there are two different kinds of scaling that might occur:

1. Horizontal scaling

Horizontal scaling involves adjusting the number of nodes or Kubernetes pods involved in a workload, either increasing or decreasing them as needed. This method offers several advantages, including the ability to add significant new capacity without impacting existing nodes or causing downtime. Additionally, horizontal scaling typically provides a faster means of increasing capacity compared to vertical scaling. However, it’s important to note that not all applications or workloads are suitable for horizontal scaling, as some may have constraints that limit their scalability in this manner.

2. Vertical scaling

Vertical scaling involves adjusting the available memory and/or processing power for existing nodes rather than increasing the number of nodes. For instance, you can vertically scale two server nodes from 16 GB of RAM and 4 vCPUs to 64 GB of RAM and 16 vCPUs each. This method is particularly useful for certain scenarios, such as relational databases without sharding, where horizontal scaling may not be feasible.

However, vertical scaling has limitations, particularly in terms of automation compared to horizontal scaling. Therefore, this article will primarily focus on horizontal auto-scaling, which is more commonly utilized in automated scaling scenarios. Vertical scaling is often handled manually by teams due to its less automated nature.

When does Autoscaling Occur?

Auto-scaling typically involves configuring resources to scale automatically based on specific events or metric thresholds defined by your organization. Engineers select these events and thresholds based on their correlation with degraded performance.

For instance, a developer might set a threshold of 70 percent memory usage sustained for more than four minutes. They could further specify a response to this threshold by triggering the launch of two additional instances whenever it’s met or exceeded. Additionally, they could establish both a minimum and maximum limit for scaling. This entails determining the minimum acceptable number of nodes required to run the workload effectively and, conversely, defining the maximum limit to avoid over-provisioning resources.

In addition to event-based triggers or metric thresholds, auto-scaling can also be configured based on a predefined schedule. This approach is particularly useful for companies and services with predictable or cyclical load demands. By setting up a schedule, you can proactively scale your infrastructure in anticipation of increased demand, ensuring that you have adequate resources available when needed. Once the peak period passes, you can then scale back the infrastructure to optimize resource utilization and minimize costs.

What Makes Auto-scaling Vital?

Auto-scaling plays a crucial role in enhancing the customer experience by ensuring that services remain available and responsive, irrespective of fluctuations in demand. By automatically adjusting resource allocation based on workload requirements, auto-scaling helps organizations maintain a responsive infrastructure while optimizing costs. This proactive approach ensures that customers receive consistent performance levels, leading to higher satisfaction and retention rates.

Auto-scaling plays a pivotal role in ensuring a positive experience for customers utilizing auto-scaled applications. Customer experience stands as the paramount metric for evaluating application performance, and maintaining a robust customer experience amid surges in demand represents a primary objective of scaling. Auto-scaling facilitates this objective by enabling organizations to swiftly adapt to fluctuations in demand with greater agility than manual scaling processes typically afford.

Furthermore, auto-scaling enables organizations to maintain cost efficiency, particularly during unexpected surges in workload demand. Prior to the availability of auto-scaling, businesses had to invest in additional infrastructure, often resulting in unused resources, to preemptively accommodate potential spikes in popularity. However, with auto-scaling, organizations only incur costs for the resources actively utilized at any given time. This not only minimizes unnecessary expenses but also enhances the organization’s capacity to manage sudden and unforeseen increases in demand efficiently.

When is Autoscaling Applicable?

Some of the most typical use cases for auto-scaling are demonstrated by the examples that follow:

1. E-Commerce

Engineers have the flexibility to configure their frontend and ordering systems to automatically scale out during peak hours and scale back in during off-peak hours. This aligns with the behavior of most online shoppers who tend to do their shopping during the day. Similarly, auto-scaling assists teams in preparing for holidays or other anticipated periods of increased demand. By adjusting resource allocation based on expected fluctuations in workload, organizations can ensure optimal performance and cost-efficiency throughout various timeframes.

2. Streaming

When media companies release new content, there are instances where demand can surpass even the most optimistic expectations and skyrocket. In such scenarios, known as content that “goes viral,” auto-scaling becomes invaluable by swiftly providing essential resources and bandwidth to meet the surge in demand. This ensures that the content remains accessible and delivers a seamless experience to users, even during periods of exceptionally high traffic.

3. Startups

For small companies looking to attract a large customer base, managing costs while preparing for potential rapid growth has historically been a significant challenge. Auto-scaling has emerged as a solution to this dilemma by enabling startups to minimize expenses while mitigating the risk of application server crashes during sudden spikes in demand. This capability allows businesses to maintain financial stability and ensure uninterrupted service delivery, even in the face of unforeseen surges in user traffic.

What Types of Cloud Service Models Support Auto-Scaling?

Cloud providers offer auto-scaling capabilities for workloads hosted at different levels of abstraction. For instance, auto-scaling features are available for infrastructure-as-a-service (IaaS) platforms like EC2 in AWS, Virtual Machine Scale Sets in Azure, and managed instance groups in GCP. Configuring auto-scaling on these IaaS platforms involves some manual setup, such as defining minimum and maximum capacity limits and specifying dynamic scaling policies to determine scaling triggers. Managed Kubernetes platforms such as EKS, AKS, and GKE leverage native Kubernetes functionalities to auto-scale pods or nodes. While these managed Kubernetes platforms require significant manual configuration, it is generally less than what’s needed when building your Kubernetes infrastructure on top of IaaS.

Conversely, less configuration is usually needed to implement auto-scaling for containerized workloads on serverless container-as-a-service (CaaS) platforms like ECS with AWS Fargate or Azure Container Apps. Moreover, at the highest levels of abstraction, serverless function-as-a-service (FaaS) platforms such as AWS Lambda and certain Azure Functions plans offer auto-scaling seamlessly. With these services, capacity is automatically provisioned and deprovisioned in the background to align with demand without requiring manual intervention.

Which Difficulties Are Associated with Auto-Scaling?

Despite the myriad benefits that auto-scaling offers, it is not a panacea. Auto-scaling does not provide a foolproof solution where software performance remains optimal under all conditions without any ongoing attention. Engineers must be mindful of potential limitations when designing their systems, paying particular attention to how cloud infrastructure-as-a-service (IaaS) and container workloads respond to exceptionally high demand.

The following are some possible difficulties engineers run across while putting auto-scaling into practice:

  • Setting up auto-scaling can often be complex in practice. For an application to effectively auto-scale, every component within that application must be configured to auto-scale accordingly. This includes not only the frontend and backend components but also the database layer and infrastructure elements like load balancers. Each of these components plays a crucial role in ensuring that the application can seamlessly adapt to changing demand levels while maintaining optimal performance and reliability. Therefore, thorough planning and configuration are essential to ensure that auto-scaling functions as intended across all aspects of the application architecture.
  • For successful horizontal scaling, the underlying application must be designed with this scalability in mind. Engineers need to develop the application as a collection of microservices rather than a monolithic structure. It’s essential to enforce statelessness wherever feasible, ensuring that a user request doesn’t rely on a specific node “remembering” it. Moreover, NoSQL databases and read-only databases are more conducive to horizontal scaling compared to read/write relational databases. Read-only databases can be efficiently scaled horizontally through replicas, further enhancing the scalability and performance of the application architecture.
  • During sudden demand peaks, the auto-scaling response may lag behind, leading to potential performance issues for users. In the best-case scenario, it may take minutes for nodes to come online, causing customers to experience poor performance or slowness during that time.
  • The ability of engineers to precisely identify the performance criteria that should initiate scaling is essential for effective auto-scaling. Accurately measuring these measures isn’t always simple, though. Unintentionally basing auto-scaling decisions on the wrong performance indicators can happen to engineers, resulting in less than ideal user experiences.