What is cloud scalability?
Cloud scalability is the ability of a cloud computing system to increase or decrease its resources, such as computing power, storage, and network bandwidth, to meet changing demands. It can be a key benefit of the cloud because it allows businesses to handle increases or decreases in workload without overhauling their entire infrastructure. This means you can easily adjust your resources as your business grows or as traffic to your application changes, paying only for what you use.
Patterns for scalable and resilient apps
Scalability versus elasticity in cloud computing
While often used interchangeably, scalability and elasticity are different concepts. Both relate to a system's ability to handle changing demands, but they do so in different ways.
Scalability
Scalability is about a system's ability to handle a growing amount of work by adding resources over time. It's often a planned, long-term approach to meeting increased demand. For example, a company might plan to scale up its database resources in anticipation of a big marketing campaign that will bring in more users. It's a system's ability to expand to meet future growth.
Elasticity
Elasticity is the ability of a system to automatically and quickly adjust resources in real time, responding to immediate changes in demand. Think of it like a rubber band stretching and shrinking. An e-commerce website, for example, might be designed to automatically add more servers to handle a sudden surge of traffic on a holiday like Black Friday and then reduce the number of servers when the traffic dies down. Elasticity is about dynamic, on-demand adjustments to match current demand, which can help optimize costs and performance.
Types of scalability in cloud computing
Cloud scalability primarily comes in two forms: vertical scaling and horizontal scaling. The right approach depends on the application's architecture and the nature of the workload.
Vertical scaling (scale up/down)
Vertical scaling, also known as "scaling up" or "scaling down," is the process of adding or removing resources from a single server or machine. It's like upgrading a single computer with a faster processor or more RAM. The goal is to make one machine more powerful.
How vertical scaling works
You can vertically scale by increasing the capacity of an existing virtual machine (VM). For instance, if your application is running on a VM with a certain number of CPU cores and a certain amount of memory, you can scale up by simply choosing a larger VM with more resources. Similarly, you can scale down by moving to a smaller VM.
Horizontal scaling (scale out/in)
Horizontal scaling, also referred to as "scaling out" or "scaling in," involves increasing the number of machines or nodes in a system to distribute the workload more effectively. It's like adding more computers to a network to share the work. The goal is to increase capacity by using multiple machines instead of making one machine more powerful.
How horizontal scaling works
Horizontal scaling involves running your application on multiple VMs, and a load balancer then distributes incoming traffic across these machines. As demand increases, you can automatically or manually add more VMs to the group. When demand decreases, you can remove them.
Vertical versus horizontal scaling: choosing the right approach
Deciding between vertical and horizontal scaling depends on your application's architecture and your business needs.
Feature | Vertical scaling | Horizontal scaling |
Complexity | Simpler, as it involves a single machine. | More complex, as it requires managing multiple nodes and a load balancer. |
Downtime | May cause a brief service interruption during an upgrade. | Typically has no downtime as new machines are added without disrupting existing ones. |
Scalability limit | Limited by the physical capacity of a single machine. | Nearly unlimited, as you can add as many machines as you need. |
Fault tolerance | Has a single point of failure; if the machine goes down, the application is unavailable. | High fault tolerance; if one machine fails, others can continue to operate. |
Feature
Vertical scaling
Horizontal scaling
Complexity
Simpler, as it involves a single machine.
More complex, as it requires managing multiple nodes and a load balancer.
Downtime
May cause a brief service interruption during an upgrade.
Typically has no downtime as new machines are added without disrupting existing ones.
Scalability limit
Limited by the physical capacity of a single machine.
Nearly unlimited, as you can add as many machines as you need.
Fault tolerance
Has a single point of failure; if the machine goes down, the application is unavailable.
High fault tolerance; if one machine fails, others can continue to operate.
For an initial performance enhancement, vertical scaling can be the most effective method. As your application expands and requires greater availability, transitioning to horizontal scaling may become more appropriate. A hybrid approach, sometimes called "diagonal scaling," also combines both methods.
Cloud scalability with Google Cloud
While many providers offer standard scaling capabilities, Google Cloud provides a set of differentiated, flexible tools designed to meet the evolving needs of modern applications. From machine learning-driven predictions to unique hybrid capabilities, these offerings can help you optimize performance and costs more effectively.
Predictive autoscaling
Traditional autoscaling is reactive—it adds resources only after demand has already spiked, which can sometimes lead to brief performance lags. Compute Engine predictive autoscaling solves this by using machine learning to forecast future load based on your historical data. It can scale out your infrastructure in advance of predicted traffic, helping ensure your applications are ready to serve users the moment demand arrives.
Custom machine types
Most cloud providers force you to choose from pre-defined "t-shirt sizes" for your virtual machines, which often leads to paying for resources you don't need. Google Cloud offers custom machine types, allowing you to tailor the exact number of vCPUs and amount of memory for your workloads. This granular control helps you vertically scale your infrastructure efficiently, fitting the resource to the task rather than the other way around.
Cloud bursting and hybrid scalability
For organizations with on-premises infrastructure, handling sudden traffic spikes can be a challenge. Cloud bursting allows you to seamlessly "burst" excess traffic to the public cloud when your private data center reaches capacity. Google Cloud’s hybrid and multi-cloud solutions, such as GKE Enterprise, provide the consistency and flexibility needed to manage workloads across environments, ensuring you can scale indefinitely without abandoning your existing investments.
Serverless scalability with Cloud Run
Cloud Run allows you to run containerized applications without managing the underlying infrastructure. A key differentiator is its ability to scale to zero—meaning if there is no traffic, you use no resources and pay nothing. When traffic returns, Cloud Run can almost instantly scale up from zero to thousands of instances, making it ideal for applications with highly variable or unpredictable traffic patterns.
Global load balancing
Scalability is not just about adding servers; it is about getting traffic to them efficiently. Google Cloud Load Balancing uses a single global anycast IP address to distribute traffic across your resources worldwide. This allows you to scale your application globally without complex DNS setups or regional fragmentation, ensuring users are always routed to the closest healthy instance with available capacity.
Key benefits of cloud scalability
Cloud scalability can offer several important benefits that are difficult to achieve with traditional on-premises infrastructure.
Cost efficiency
Cost efficiency
You can potentially avoid over-provisioning resources and the high costs of maintaining idle hardware. With cloud scalability, you only pay for the resources you use, which turns fixed costs into variable ones.
Improved performance and availability
Improved performance and availability
By dynamically adjusting resources, your applications can more easily maintain high performance even during peak demand, preventing slowdowns and service interruptions that can frustrate users.
Handling unpredictable demand
Handling unpredictable demand
Scalability helps you handle unexpected traffic spikes, like those from a viral social media post, without having to manually add new hardware.
Business agility
Business agility
The ability to scale quickly helps you innovate and respond to market changes faster. You could deploy new features or applications without waiting for infrastructure to be set up.
Cloud scalability with Google Cloud
Google Cloud offers a range of services designed that can help you build highly scalable applications. Many of these services have built-in autoscaling features, so you don't have to manage the underlying infrastructure.
Google Kubernetes Engine (GKE) autoscalingGKE is a powerful platform for running containerized applications. It can automatically scale the number of pods (which are the smallest deployable units in Kubernetes) and the number of nodes (VMs) in your cluster based on your workload. This makes it a great choice for managing dynamic and unpredictable demand.
Compute Engine autoscalerThe Compute Engine autoscaler manages virtual machine instances within a managed instance group by automatically adding or removing them as needed. You can set up policies based on metrics like CPU utilization, HTTP load balancer usage, or custom metrics, and the autoscaler will handle the rest.
Cloud Run autoscalingCloud Run is a fully managed application platform that lets you run containers that are invocable via requests or events. By default, each Cloud Run instantiation or “revision” is automatically scaled to the number of instances needed to handle all incoming requests, events, or CPU utilization.- Compute Engine CPU overcommitCPU overcommit on sole-tenant nodes lets you schedule instances that can share their spare CPU cycles with each other, enabling you to schedule more VM CPUs than are normally available. CPU overcommit is especially valuable for workloads that are underutilized but might experience relatively uncorrelated bursts.
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