If you’re doing cloud operations right, your cloud needs are going to change over time. Cloud scaling can help you add cloud resources when you need them and retire or recycle them when you don’t. Cloud scaling is great for meeting traffic demands, accommodating demanding workloads, and controlling the chaos of cloud ops.
But for all the flexibility that cloud scaling offers, cloud computing scalability can also introduce operational liabilities. Read on to learn more about the benefits of cloud scaling, a few tools to handle different aspects of scaling cloud infrastructure, and the Achilles’ heel of cloud computing scalability.
Table of Contents
- What is Cloud Scaling? Understanding Cloud Computing Scalability
- Cloud Scalability vs. Cloud Elasticity
- What is the Point of Cloud Scaling?
- Cloud Scaling Tools
- The Hidden Risks of Cloud Scaling
- How Infrastructure as Code Enables Better Cloud Scaling
- Puppet Infrastructure as Code Gives Cloud Ops Teams the Confidence to Scale Away
What is Cloud Scaling? Understanding Cloud Computing Scalability
In cloud computing, cloud scaling is the act of increasing or decreasing IT resources like virtual machines, servers, and storage as needed. Cloud computing scalability is an essential aspect of efficient cloud operations because it helps reduce cloud spend and wasted resources.
Horizontal Cloud Scaling vs. Vertical Cloud Scaling
In cloud computing, vertical cloud scaling or “scaling up” increases the capacity of resources within existing servers, like CPU, RAM, and storage. In contrast, horizontal scaling or “scaling out” is the act of adding more servers and VMs to distribute cloud workloads across more machines.
Cloud ops teams usually scale up to meet increasing app resource requirements. Scaling out helps them handle growing workloads and ensure redundancy (availability and resiliency of cloud resources).
Back to topCloud Scalability vs. Cloud Elasticity
The main difference between cloud scalability and elasticity is that cloud computing scalability is focused on expanding capacity to meet long-term goals. On the other hand, cloud elasticity automatically responds to changing capacity needs in real time.
Yes, there’s a difference between cloud scalability and cloud elasticity! To go a little deeper on the differences and why you’d do one or the other…
Cloud scalability is the ability to increase and decrease resources and workloads over time as your IT needs change.
Scalability is great for planning around and measuring strategic, long-term goals. If you want to grow your IT estate over a period of years, you should invest in tools and techniques that support cloud scalability.
- Cloud computing scalability is achieved with infrastructure as code (IaC) tools, content delivery networks (CDNs) like Akamai and Cloudflare, performance monitoring tools, and more.
Cloud elasticity is the ability of a system’s cloud resources to grow and shrink automatically in real time as a direct response to your immediate needs.
Elasticity helps IT and infrastructure teams support unpredictable workloads. If you need to control resource usage now or if you don’t have long-term IT goals, you might want to invest in tools for an elastic cloud.
- Cloud elasticity is achieved with autoscalers, IaC tools, orchestration tools, load balancers like AWS Elastic Load Balancing (ELB), and more.
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What is the Point of Cloud Scaling?
Scaling cloud infrastructure helps cloud ops teams control cloud costs and optimize performance by provisioning and recycling resources according to current demand. Scaling ensures resources are available when needed and deactivates or recycles them when they’re not needed.
In the early years of the COVID-19 pandemic, businesses flocked to the cloud en masse. Now that many organizations have at least some of their IT hosted in the cloud, controlling cloud costs has become an industry unto itself. Cloud scaling is one method of doing that. By ensuring that no resources are left unused, and utilizing resources more effectively, organizations can minimize their wasted cloud spend and improve performance.
Cloud waste and out-of-control costs have also led to a phenomenon where organizations move some or all of their cloud resources back to private clouds — or even back to the data center. Learn more about cloud repatriation >>
Back to topCloud Scaling Tools
While not an exhaustive list, this table of common cloud scaling tools should give you an idea of the playing field.
Cloud Scaling Tool | Purpose/ | Examples |
| Autoscalers | Automatically adjusting resources in response to changes in demand (based on predefined policies) | AWS Auto Scaling, Azure Autoscale, Google Cloud Autoscaler |
| Load Balancers | Distributing traffic across instances to optimize resource utilization | AWS ELB, Azure Load Balancer, F5, HAProxy, NGINX |
| Infrastructure as Code Tools | Defining and managing IT configurations as code to ensure consistency as resources scale | Puppet, Ansible, Terraform, Chef |
| Container Orchestration Tools | Automating deployment and scaling of containerized applications in the cloud | Kubernetes, Docker |
The Hidden Risks of Cloud Scaling
Scaling cloud resources can lead to inconsistent configurations in cloud infrastructure. Configuration drift can introduce security risks, missed dependencies, and performance issues across hybrid cloud resources.
Cloud scaling is a great way to appropriately size your cloud and conserve resources you don’t use. But it can also introduce liabilities that can actually decrease performance and open up security vulnerabilities across the resources you’re scaling.
Without strong configuration enforcement, cloud scaling can lead to problems like:
- Configuration drift (inconsistencies between instances or components multiplying as they’re added and removed)
- Dependencies (changes in one area impacting others as resources scale up)
- Security risks (improperly configured resources present security vulnerabilities at scale)
- Low monitoring and auditability (adding resources makes it easy to miss drift and other anomalies)
How Infrastructure as Code Enables Better Cloud Scaling
Infrastructure as code (IaC) supports cloud scalability by providing a standardized, automated approach to managing infrastructure. Configurations are written as code, so they can be defined clearly, edited when needed, and repeated over and over to enforce a desired state across any kind of infrastructure.
IaC is great for cloud scaling because:
- IaC ensures that new servers and VMs are provisioned in a consistent, secure, repeatable manner. That reduces the risk of human error and inconsistency in cloud scaling.
- IaC tools can define dependencies between components explicitly, so cloud ops teams can account for dependencies as their infrastructure scales.
- IaC defines security configurations as code, so your desired security and compliance policies are enforced continuously — even as cloud resources scale up and out.
- IaC describes configurations as readable code, making it simpler to document a desired state and easier to detect anomalies.
Puppet Infrastructure as Code Gives Cloud Ops Teams the Confidence to Scale Away
Puppet infrastructure as code, written in the Ruby-based Puppet DSL, even works across multiple clouds and across hybrid cloud infrastructure. In short, using Puppet for hybrid cloud IaC makes it easier and safer to scale your cloud with confidence.
“[With Puppet,] we’re able to manage more servers with the same number of people (and even fewer). We can deliver servers with the same level of security every time with minimal effort.”
DevOps Engineer, Federal Government
For tips and insight on improving efficiency and scalability in your cloud infrastructure, download our free Guide to Unlocking Cloud Scalability and Efficiency — or if you’re ready to see how Puppet can enhance your cloud ops, request a demo at the link below.