Scalability and elasticity help organizations achieve greater flexibility and efficiency in their IT infrastructure. However, scalability and elasticity have distinct differences that organizations should be aware of when deciding which approach to take. This blog post will break down the differences between the two, provide examples of their use, and eventually discuss how to achieve both in cloud computing.
What is Scalability?
Scalability refers to a cloud system’s ability to adjust resources to meet changing demands. You can do this by scaling up (vertical scaling) or scaling out (horizontal scaling). Vertical scaling involves adding resources to an existing machine, like upgrading the CPU or RAM. Horizontal scaling involves adding additional machines (servers) to the pool of resources to handle the increasing load.
Microservices, a developmental approach that breaks down software into small, independent services, facilitates horizontal scaling by allowing services to be replicated across multiple servers. Each microservice can be scaled and load balanced separately, thereby handling increased traffic and workload.
What is Elasticity?
Elasticity, on the other hand, refers to a system’s ability to automatically scale up or down in response to changes in demand. It’s an auto-scaling feature that ensures resources are always available when needed and automatically released when demand decreases. In contrast to scalability, elasticity is more dynamic and requires less manual intervention.
An e-commerce store or an online marketplace during the holiday season is an example of elasticity at work. A large website should have an elastic infrastructure that automatically scales up to handle increased traffic and sales without disrupting operations. When demand decreases, the infrastructure automatically scales down to reduce costs.
Comparing Scalability vs. Elasticity in Cloud Computing
While scalability and elasticity are closely related and often used interchangeably, they refer to different concepts in cloud computing. Let’s take a look at the key differences below.
| Feature | Scalability | Elasticity |
| Definition | Ability to handle the increased workload by adding resources | Ability to automatically adjust resources based on demand |
| Approach | Planned and often manual | Automatic and dynamic |
| Time Frame | Typically long-term (weeks to months) | Short-term (minutes to hours) |
| Resource Utilization | Adds resources permanently or semi-permanently | Adds or removes resources quickly as needed, often temporarily |
| Cost Implications | Higher due to permanent resource addition | Cost-efficient due to pay-as-you-go model |
Scalability Examples
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- Online Retailers: An e-commerce site might use several servers during regular operations. As the user base grows, the platform adds more servers (horizontal scaling) or upgrades the existing servers (vertical scaling) to maintain performance.
- Data Centers: Companies often upgrade their data centers by adding more storage, processing power, and networking capabilities to handle increased data loads.
- Database Sharding: With a larger user base comes the complexity of handling large amounts of data and metadata. To address this, databases can split their table schemas, a.k.a. shards, and store data in separate physical locations. Doing this allows databases to handle more queries and transactions simultaneously.
Elasticity Examples
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- Streaming Services: Platforms like Netflix adjust their resources based on the number of active viewers. During peak times, they scale up resources; during off-peak times, they scale down to save costs.
- Online Retailers: During Black Friday or Cyber Monday sales, online retailers use cloud elasticity to handle the sudden surge in traffic and transactions, automatically provisioning more servers to meet demand.
- Online Gaming: Multiplayer games like Fortnite scale up their servers during peak gaming hours or new releases to accommodate the influx of players, then scale down during off-peak times.
How to Achieve Scalability and Elasticity in Cloud Computing
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- Cloud Providers: Services like AWS, Microsoft Azure, and Google Cloud offer built-in scalability and elasticity features, providing tools like auto-scaling groups and managed services that automatically adjust resources.
- Load Balancers: Distribute incoming traffic across multiple servers to ensure no single server is overwhelmed, aiding both scalability and elasticity.
- Containerization: With the advent of Docker, shipping and deploying applications is straightforward, making it easier to scale out and manage resources dynamically.
- Monitoring and Automation: You can use monitoring tools to help track resource usage and set up automated scripts or use cloud-native tools to adjust resources based on predefined metrics.
The Future of Scalability and Elasticity
The future of scalability and elasticity seems to be heading toward serverless resource provisioning. Platforms like AWS Lambda, Microsoft Azure, and Google Cloud offer functions that scale automatically according to the resources needed. This functionality helps developers avoid overprovisioning resources, making it a cost-effective choice for startups and enterprises alike.
Delivering value via application scalability and elasticity provides agile organizations the speed, flexibility, and adaptability they require in their quest to meet user demand. Like scalability and elasticity pertain to software, agility scales up human potential in an organization (big or small).
Key Takeaways
To successfully develop and maintain applications designed for large user bases, you should utilize both scalability and elasticity. Because organizations will encounter scenarios in which they can exclusively rely on scalability (quarterly) and scenarios in which they need to adjust resources to meet increased demand quickly (short-term needs), both approaches are necessary.
To continue learning about scalability, elasticity, serverless, and the future of cloud computing, you should read our guides to serverless architecture, serverless databases, and serverless functions.
