Elastic Computing

Definition of Elastic Computing

Elastic computing, also known as cloud elasticity, is an approach that enables the dynamic allocation and reallocation of computing resources based on demand. It allows IT systems to automatically scale up or down to efficiently handle workload variations. This results in improved resource utilization, cost savings, and the ability to deliver a consistent user experience irrespective of fluctuations in demand.


The phonetic pronunciation of the keyword “Elastic Computing” is:ih-last-ik kuhm-pyoot-ing

Key Takeaways

  1. Elastic computing enables the dynamic allocation of resources to match demand, ensuring efficient utilization of infrastructure, and the ability to easily scale applications up or down.
  2. Cost savings are a significant benefit with elastic computing, as users only pay for the resources they consume, avoiding the need to maintain expensive, underutilized resources during periods of low demand.
  3. Elastic computing improves the reliability and availability of applications by supporting features like auto-scaling and load balancing, which ensures applications can handle sudden traffic spikes and maintain performance.

Importance of Elastic Computing

Elastic Computing is an essential technology term because it represents a dynamic and flexible approach to resource management in cloud computing environments.

This concept allows businesses to quickly adapt to changes in demand, scaling computing resources upwards or downwards as needed.

Elastic Computing enhances cost efficiency by enabling organizations to only pay for the resources they use and avoid wasting funds on unnecessary capacity.

Additionally, it improves overall performance, as computing workloads can be distributed efficiently across multiple virtual and physical servers.

Elastic Computing’s seamless, automated provisioning and deprovisioning of resources empower businesses to maintain optimal productivity and agility in an ever-evolving technological landscape.


Elastic computing, also referred to as “cloud elasticity,” is a core concept in modern cloud computing that revolves around the ability of a cloud-based infrastructure to dynamically adapt to workload fluctuations. The purpose of elastic computing is to enable organizations to seamlessly scale computing resources according to their changing demands, which in turn ensures optimal performance and avoids potential disruptions. By harnessing the power of cloud computing, businesses can scale their IT infrastructure up or down automatically, based on predefined conditions, without making commitments to long-term fixed resources.

Elastic computing enables organizations to improve productivity and reduce costs, making them more agile and responsive to the evolving business environment. In practical terms, elastic computing is often used to support a wide range of applications, such as e-commerce, gaming, and data analytics, that witness varying degrees of user demands. For instance, a retailer may experience a sudden surge in its online traffic during promotional events or holidays.

With elastic computing, the retailer’s IT infrastructure can be scaled up to ensure smooth customer experience, and later scaled down when the demand subsides. Similarly, a data analytics platform might require large computing resources periodically to process vast amounts of data and generate insights. Elastic computing enables such platforms to allocate additional resources during peak usage and release them afterward, thus reducing costs and ensuring stellar performance.

Overall, elastic computing has become an essential component of the modern IT ecosystem, empowering businesses to stay ahead in a rapidly changing digital landscape.

Examples of Elastic Computing

Elastic computing, also known as cloud elasticity, refers to the scalable computing resources and services provided by cloud computing platforms that can automatically adapt to changes in demand or workload, offering flexibility, cost efficiencies, and increased resource availability.Here are three real-world examples of elastic computing:

Netflix: As one of the largest streaming services with millions of subscribers, Netflix leverages elastic computing to adapt their resources based on customer demand. By using Amazon Web Services (AWS) for cloud computing, Netflix can easily and quickly scale up their infrastructure during peak streaming times and scale down during times of lower demand – all without incurring the costs associated with maintaining their own hardware and data centers.

Airbnb: The global home-sharing platform Airbnb also uses elastic computing to cater to fluctuations in demand from users. By using AWS, Airbnb can quickly scale their infrastructure to accommodate the spikes in traffic that occur daily or during peak seasons, while also seamlessly scaling back when demand is not as high. This allows Airbnb to allocate resources efficiently and ensure seamless user experiences for their customers.

Pokémon Go: The incredibly popular mobile game Pokémon Go, developed by Niantic, faced extreme spikes in demand with millions of simultaneous players during its launch. To handle this influx of users, Niantic relied on Google Cloud, which enabled them to scale their infrastructure elastically to manage this massive load. Elastic computing allowed Pokémon Go to avoid downtime and provide an uninterrupted gaming experience for its players, even during periods of rapid and unpredictable growth.

Elastic Computing FAQ

1. What is Elastic Computing?

Elastic Computing is a cloud computing model that allows users to scale computing resources up or down dynamically in response to workload changes. It provides automatic provisioning and deprovisioning of resources as needed, ensuring that applications have the right amount of resources at any given time.

2. How does Elastic Computing work?

Elastic Computing works by monitoring the performance of your application and adjusting the number of computing resources based on the current demand. It utilizes advanced algorithms and cloud infrastructure to add or remove resources in real-time, ensuring optimal performance and cost-efficiency.

3. What are the benefits of Elastic Computing?

There are several benefits of using Elastic Computing, including improved performance, cost optimization, and flexibility. Businesses can handle traffic spikes and fluctuations without any manual intervention, reducing IT management overhead and allowing them to focus on core business processes. Elastic Computing also allows for more efficient utilization of resources, reducing operational costs.

4. When should I consider using Elastic Computing?

Elastic Computing is ideal for applications and businesses that experience variable workloads, traffic spikes, or seasonal fluctuations. It’s also well-suited for startups that anticipate growth but are unsure about future resource requirements. If your application demands unpredictable resources or has frequently changing workloads, you should consider adopting Elastic Computing.

5. Are there any drawbacks to Elastic Computing?

While Elastic Computing offers many advantages, it may not be suitable for every scenario. For example, some applications with consistent workloads and predictable resource requirements might not benefit from the dynamic scaling capabilities. Additionally, there may be increased complexity with managing applications that rely on Elastic Computing and potential security concerns with a shared cloud infrastructure.

6. How can I get started with Elastic Computing?

To get started with Elastic Computing, you can sign up for a cloud service provider like Amazon Web Services, Microsoft Azure, or Google Cloud Platform, which offer various Elastic Computing options that cater to different user needs. You will need to configure your application to take advantage of the dynamic scaling capabilities, and you may need to consult with an expert or use existing documentation to optimize the setup for your specific use case.

Related Technology Terms

  • Scalability
  • Load Balancing
  • Cloud Computing
  • Auto-Scaling
  • Virtualization

Sources for More Information


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