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Cloud Scaling Patterns
Govardhana M K
February 07, 2025
TechOps Examples
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🧠 Use Case
Cloud Scaling Patterns
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This guide gives you the tools, insights, and strategies you need to go beyond basic Kubernetes scaling and achieve peak infrastructure efficiency.
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🧠 USE CASE
Cloud Scaling Patterns
Cloud engineers have to deal with scaling challenges every day.
Applications need to handle traffic spikes without crashing, but at the same time, running too many resources all the time is expensive.
Scaling efficiently keeps systems reliable and cost effective.
Before getting into cloud scaling patterns, it is important to understand the two main ways to scale.
Vertical scaling means making a machine more powerful by adding more CPU, RAM, or storage.
Horizontal scaling means adding more machines instead of making a single one bigger.
Let’s look at the most common cloud scaling patterns:
Download a high resolution image of this diagram here for future reference.
1. Target Tracking Scaling
This pattern automatically adjusts capacity based on a metric like CPU utilization.
A common example is setting a target CPU utilization of 50 percent. If usage goes above this, the system adds more instances. If it drops below, instances are removed.
When to use:
For applications with unpredictable workloads.
When response time is tightly linked to resource availability.
Caution: Be mindful of metric selection. If the metric fluctuates too often, it may cause unnecessary scaling actions, leading to instability.
Use a cool down period to prevent rapid scaling changes.
2. Step Scaling
Step scaling adds or removes resources in predefined steps.
For example, if CPU usage exceeds 60 percent, two instances are added. If it falls below 40 percent, one instance is removed.
Instead of reacting to every small change, it scales in larger steps.
When to use:
When workloads have sudden spikes rather than gradual increases.
When precise scaling control is needed.
Caution: The scaling steps must be carefully planned. If too aggressive, you may over provision and waste resources. If too slow, your system might struggle under load.
Use monitoring tools to fine tune scaling thresholds.
3. Predictive Scaling
Predictive scaling uses historical data and trends to forecast future load and adjust capacity ahead of time.
It schedules scaling actions before the demand actually increases.
When to use:
For applications with recurring traffic patterns like daily, weekly, or seasonal trends.
When response time is critical and cannot wait for auto-scaling to react.
Caution: Forecasting is not always perfect. Unexpected traffic spikes can still happen.
Combine predictive scaling with reactive scaling like target tracking to handle anomalies.
4. Scheduled Scaling
Scheduled scaling adds or removes resources at fixed times based on known usage patterns.
If traffic increases every Friday evening, more instances can be added beforehand. If traffic is low every Monday, instances can be reduced.
When to use:
For applications with predictable usage, such as business hours or weekend surges.
For batch processing workloads that run at specific times.
Caution: Relying only on scheduled scaling can lead to inefficiency. Unexpected traffic outside scheduled windows can degrade performance.
Pair it with a dynamic scaling method to handle unpredictable spikes.
Remember,
No single scaling pattern works for every application.
The best approach is a combination of multiple patterns based on your workload.
Monitoring and fine tuning are critical to ensure efficient scaling.
In this AWS re:Invent 2023 session, Skye Hart and Chris Munns provided valuable insights on scaling applications for the first 10 million users on AWS. They discussed best practices for building scalable architectures.
As an extension of this topic, I recommend this fantastic watch.
‘Terraform Basics to Advanced in One Guide’ FREE giveaway, Final Call 👇
(Usually, you need 10 referrals to grab this visually intuitive 71-page PDF)
Topics covered:
Terraform Fundamentals (Declarative vs. Imperative Approach, High Level Architecture)
Setting Up Your Terraform Environment (Providers, Terraform State [local, remote], State Locking, Backends)
Core Terraform Workflow (initialization, Provider Plugins, Organizing Terraform configs, resources and Variables, Planning and Applying Changes)
Managing Infra (Reusable Modules, Workspaces)
Variables, Data Types, and Outputs
Advanced Terraform Techniques (Handling Dependencies, Sensitive Data, Dealing with Large Infra)
Provisioners and Lifecycle Management
Debugging and Troubleshooting (TF_LOG Levels, common tf issues, best practices)
Must Know Terraform Commands
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