What is cloud elasticity?
Cloud elasticity is the ability of a cloud system to automatically change its computing resources to match a changing workload. Also referred to as elastic computing, this concept is like a rubber band; it can stretch to handle more work and then snap back when the work is done, meaning you don't have to guess how much capacity you'll need ahead of time. Instead, your cloud service automatically gives you more processing power, memory, or storage when you have a spike in traffic and then scales it back down when things are slow. This can help businesses save money by only paying for the resources they use.
Understanding elastic computing
Cloud elasticity is a key benefit of cloud computing that lets a company's cloud infrastructure grow or shrink its resources automatically based on demand. The core cloud elasticity meaning is about adapting to unpredictable changes in workload without human help.
For example, an ecommerce website may see a huge surge in traffic on a holiday like Black Friday. With elastic computing, the website automatically gets more servers to handle the traffic spike, ensuring customers have a smooth shopping experience. Once the holiday is over, the system reduces the resources to normal levels.
Types of cloud elasticity
There are two main ways that a system can use elasticity: horizontal and vertical.
Horizontal elasticity
This is also called "scaling out" or "scaling in." It involves adding or removing more machines, or instances, to your system. When you need more capacity, you add more servers. When you need less, you get rid of them. This is often used for applications that can be split up and run on multiple servers at once.
Vertical elasticity
This is also called "scaling up" or "scaling down." This involves increasing or decreasing the resources of a single machine. For example, if you need more power for a specific server, you'd give it more CPU, memory, or storage. When you're done, you can reduce those resources.
Cloud elasticity versus scalability
While the terms elasticity and scalability are often used together, they describe different things.
- Cloud scalability is a system's ability to handle a larger workload over time by adding more resources. It's a planned, long-term strategy for growth. For example, a company might know it will have more users each year, so it plans to add more servers to its data center.
- Cloud elasticity is about a system's ability to adapt to sudden, unpredictable changes in demand in real time. It's an automatic and reactive process that focuses on efficiency and cost savings.
Think of it this way: Scalability is like preparing for a big marathon by training for months. Elasticity is like a runner who can instantly speed up or slow down their pace during the race to match the other runners.
Benefits of using cloud elasticity
Elastic computing offers many potential advantages for businesses and developers.
Cost efficiency
Cost efficiency
Elastic computing can help you save money by only paying for the resources you use. When demand is low, you don't have to keep extra servers running, which cuts down on unnecessary spending. You can also avoid buying expensive hardware to handle peak traffic that you only need a few times a year.
High availability and reliability
High availability and reliability
By automatically adjusting resources, elastic computing can help ensure that your application remains available and responsive even during unexpected traffic spikes. This can prevent slowdowns or crashes that could frustrate users or hurt your business's reputation.
Improved performance
Improved performance
The system automatically adds resources when needed, which can help maintain fast response times and a smooth user experience. This is especially important for applications with unpredictable workloads, like online gaming, streaming services, or ecommerce.
Simplified management
Simplified management
Because the scaling process is automated, developers and IT teams don't have to manually monitor and adjust resources. This can free up time and effort that can be focused on other important tasks, like developing new features or improving the user experience.
Elastic computing for enterprise developers
For developers in an enterprise environment, elastic computing isn't just a feature—it's an important strategy that can help build resilient, cost-effective, and performant applications. It's about designing systems that can intelligently adapt to unpredictable workloads, avoiding both over-provisioning and under-provisioning. This is especially important for mission-critical applications that must remain available 24/7, such as ecommerce platforms, financial services, or data processing pipelines.
Building elastic systems with Compute Engine
Cloud elasticity is the core principle behind how Compute Engine's managed instance groups (MIGs) operate. Instead of manually provisioning VMs to handle traffic, you can design a system that automatically adapts to changes in demand. A MIG is a group of identical VMs that you can manage as a single entity, and it’s the primary tool for building an elastic system on Compute Engine. The MIG uses an autoscaler to automatically add or remove VMs from the group based on predefined metrics, which is how it achieves elasticity.
Here's how an enterprise developer can practically apply these concepts to build a system that scales:
Create a VM blueprint: an instance template | Before you can create an elastic system, you need an instance template. This template serves as a single source of truth for your application's VM configuration, including the machine type, boot disk, and any necessary startup scripts. This ensures every new VM is an exact replica of the last, which promotes consistency and simplifies rollouts. |
Configure the managed instance group | Go to the "Instance groups" page in the Google Cloud console and create a new managed instance group. Select the instance template you created, set a minimum and maximum number of instances, and choose the zones for your group to ensure redundancy and high availability. |
Implement autoscaling rules | This is the heart of cloud elasticity. Instead of simple CPU-based scaling, enterprise developers can implement advanced autoscaling rules based on Cloud Monitoring metrics (like a queue length for a backend worker service) or use predictive autoscaling, which uses historical data to spin up new VMs before a traffic spike is expected. |
Add a load balancer | For any public-facing application, a load balancer is essential for distributing incoming user traffic across all the VMs in your managed instance group, which ensures that no single VM is overloaded and that your application remains highly available. |
Create a VM blueprint: an instance template
Before you can create an elastic system, you need an instance template. This template serves as a single source of truth for your application's VM configuration, including the machine type, boot disk, and any necessary startup scripts. This ensures every new VM is an exact replica of the last, which promotes consistency and simplifies rollouts.
Configure the managed instance group
Go to the "Instance groups" page in the Google Cloud console and create a new managed instance group. Select the instance template you created, set a minimum and maximum number of instances, and choose the zones for your group to ensure redundancy and high availability.
Implement autoscaling rules
This is the heart of cloud elasticity. Instead of simple CPU-based scaling, enterprise developers can implement advanced autoscaling rules based on Cloud Monitoring metrics (like a queue length for a backend worker service) or use predictive autoscaling, which uses historical data to spin up new VMs before a traffic spike is expected.
Add a load balancer
For any public-facing application, a load balancer is essential for distributing incoming user traffic across all the VMs in your managed instance group, which ensures that no single VM is overloaded and that your application remains highly available.
Related products
Compute EngineThis service provides custom virtual machines (VMs) that can be automatically scaled using managed instance groups, which are a foundational tool for building an elastic infrastructure.
Cloud MonitoringThis service collects and visualizes metrics from your applications and infrastructure, such as CPU utilization and HTTP load. This data is what the autoscaler uses to make intelligent decisions about when to add or remove virtual machines.- Cloud Load BalancingThis service automatically distributes incoming user traffic across all the virtual machines in your managed instance group, which ensures your application remains highly available and performs optimally during traffic spikes.
Additional resources
- Autoscaling in managed instance groups: This documentation provides details on how to use autoscaling with Compute Engine's managed instance groups.
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