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Cloud Scalability vs Cloud Elasticity Comparison

With cloud computing, customers only pay for the resources they use at any given time. Cloud elasticity proves cost-effective for any business with dynamic workloads such as digital streaming services or e-commerce platforms. Defining the businesses in terms of resource predictability or workload variations may allow cloud computing services to provide a scalable or elastic solution to their client’s business needs. This will help in identifying which cloud computing services would belong in a public cloud environment and which businesses can be handled by the organization itself. Cloud Elasticity refers to the system’s ability to decrease or increase its resource allocation in real-time in response to a sudden drop or spike in demand. Cloud Scalability on the other hand refers to the increased workload with respect to its already existing infrastructure to meet its long-term growth demands without any service interruption.

Most of the time, they are confused amongst each other once the concept is taken into consideration. Scalable systems and elastic systems both use a pay-as-you-go pricing model that helps companies achieve efficiencies in price and performance of their systems. For elastic scaling, there’s also a pay-as-you-grow aspect that denotes the added resource expansion for spikes, which, when they have passed, returns to the pay-as-you-go for use model. In the case of needing more processing power, a company moves from a smaller resource to a larger one that is more performant, such as moving from a virtual server with two cores to one that has three. While cloud scaling is automated and fast, often on the order of seconds for new containersand up to minutes for VMs, to bring up new hardware can take some time.

difference between scalability and elasticity in cloud computing

As another example, you can configure your system to increase the total disk space of your backend cluster by an order of 2 if more than 80% of the total storage currently available to it is used. If for whatever reason, at a later point, data is deleted from the storage and, say, the total used storage goes below 20%, you can decrease the total available disk space to its original value. For example, if you have an application hosted on a VM or any other compute service, we want that it should always remain up and running even if underlying server hardware fails.

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If the provider is untrustworthy or neglectful of security, you might have a major data loss on your hands. As a result, this well-connected infrastructure (if the infrastructure you paid for is well-connected) doesn’t even need supervision most of the time. Moreover, it’s a perfect place to teach algorithmic machines what they have to do. If one of your servers does all the work, and the others are barely busy, you won’t achieve much.

Under the elastic model, companies can add all the resources they need to meet peak demand — for example, for black Friday retail situations — without experiencing any downtime or significant delays. Companies can add all the necessary resources, such as RAM, CPU processing power, and bandwidth. In elastic systems, resources are neither idle nor missing; instead, they are available. Elasticity goes hand-in-hand with rapid response to dynamic environments. It helps to deliver computing services dynamically to the customers with ease without having to add extra cost to the business in terms of capacity building.

difference between scalability and elasticity in cloud computing

The applications within the infrastructure have room to expand or shrink according to the demand. In these scenarios, the manager is well aware that once the resources’ request difference between scalability and elasticity in cloud computing lowers down, they will scale out and get back to normal. When the resources are much more than required, they are made to scale out until the demand arises again.

Where elasticity and scalability cross paths

With the DataMyte Digital Clipboard, companies can quickly create, edit, and delete scalability & elasticity rules. This scalability and elasticity system allows companies to set up multiple scalability strategies and create scalability & elasticity rules that can be applied in real-time. If you’re going to implement scalability and elasticity into your cloud infrastructure, one of the best ways to do it is by using a low-code platform like DATAMYTE. Our Digital Clipboard, in particular, is a low-code software capable of creating workflows that help implement, monitor, and maintain scalability and elasticity.

There are many aspects of cloud computing that CIOs, cloud engineers and IT managers should consider when deciding to add cloud services to their infrastructure. Cost, security, performance, availability and reliability are some common key areas to evaluate. Two additional criteria that have become increasingly important are cloud scalability and cloud elasticity.

difference between scalability and elasticity in cloud computing

Ideally, when the workload is up one work unit the cloud will provide the system with another “computing unit”, when workload goes back down the cloud will gracefully stop providing that computing unit. Scalability is the ability of the system to accommodate larger loads just by adding resources either making hardware stronger or adding additional nodes . Cloud elasticity and scalability optimize the infrastructure and ensure that the organizations keep up to the compliance levels. Right-sized infrastructure is also something that these two bring along. This is used by companies that need high availability and little or no downtime with applications.

Automation & Zero-touch IT: The Keys to Optimizing SaaS

As mentioned earlier, cloud elasticity refers to scaling up the computing capacity as needed. It basically helps you understand how well your architecture can adapt to the workload in real time. In the grand scheme of things, cloud elasticity and cloud scalability are two parts of the whole. Both of them are related to handling the system’s workload and resources.

These volatile ebbs and flows of workload require flexible resource management to handle the operation consistently. The purpose of elasticity is to match the resources allocated with the actual amount of resources needed at any given point in time. Using the example of our Pizzeria again, you notice that several large subdivisions are being developed within a five-mile radius of your store and city.

  • This allows for the system to be flexible and responsive and to minimize waste by only using the resources that are needed.
  • Your application may use systems that aren’t in the cloud or that are hosted by a different cloud provider.
  • Cloud service providers typically offer a service level agreement that guarantees HA or uptime of resources and services as a percentage.
  • Still, they love to drop those terms in conversation to sound timely and relevant.
  • Both features of cloud computing are important and very well related to each other in enhancing system efficiency.
  • Scalability also encompasses the ability to expand with additional infrastructure resources—in some cases, without a hard limit.

This can include CPU processing power, memory, and storage and is often limited to the resources available in existing hardware. Scalability handles the scaling of resources according to the system’s workload demands. Where IT managers are willing to pay only for the duration to which they consumed the resources. Nowadays, cloud computing services may offer both scalable and elastic services to their customers.

What is reliability in cloud computing?

While you could add a database server to double the load potential, a simpler approach would be to provision a more robust server on a more persistent basis, a process known as scaling up. A use case where cloud elasticity is necessary would be in retail during increased seasonal activity. For example, during the holiday season (e.g., Black Friday spikes and special sales) there can be a sudden increased demand on the system. Instead of spending budget on additional permanent infrastructure capacity to handle a couple months of high load out of the year, this is a good opportunity to use an elastic solution. The additional infrastructure to handle the increased volume is only used in a pay-as-you-grow model and then “shrinks” back to a lower capacity for the rest of the year. This also allows for additional sudden and unanticipated sales activities throughout the year, if needed, without impacting performance or availability.

difference between scalability and elasticity in cloud computing

Brad has spent 20 years in the IT field as a network engineer, IT manager, instructor and technical writer. His portfolio includes a long assortment of white papers, articles and learning curriculum. He is an accomplished pianist and composer as well as the author of two inspirational books.

Achieving perfect Elasticity & Scalability

Companies can plan to meet their usage demands without worrying about downtime. With scalability and elasticity, companies can quickly scale up or down resources to keep their services running smoothly during times of need. In addition, scalability and elasticity can help companies avoid costly over-provisioning of resources by scaling up or down when needed. When a company decides to take up cloud services for its infrastructure, many things should be considered. The cost, availability, reliability, and performance are among a few of them. Apart from these significant areas of concern, scalability vs elasticity need proper consideration too.

When elasticity and scalability collide

This is done by adding or deleting the resources to ensure that resources are neither lacking nor available in excess. In horizontal scaling, companies add more of an equivalent function, to apportion the workload across multiple servers, keeping performance high and increasing available storage. Scalability is simply the ability of a system to add or remove resources to meet workloads within the system’s existing resources.

Of course, there will be far more orders placed on the day of the big game than on an average Sunday. To ensure that you can sufficiently meet customer demand, you double the number of delivery drivers that period and add two internal staff members to take orders and make the pizzas. The chances are that the increase in business for that once-a-year event will come at the expense of demand the following Monday.

Scalability is pretty simple to define, which is why some of the aspects of elasticity are often attributed to it. If you are just visiting the site, just wait a bit and it should be back soon. If you own the web site, please verify with your hosting company if your server is up and running and if they have our firewall IPs whitelisted. If the problem persists, open a ticket on our support page and we will assist with troubleshooting. Scalability is largely manual, planned, and predictive, while elasticity is automatic, prompt, and reactive to expected conditions and preconfigured rules.

To provide scalability the framework’s capacity is designed with some extra room to handle any surges in demand that might occur. Reliability in cloud computing is important for businesses of any size. Buggy software can cause lost productivity, lost revenue, and lost trust in your brand. Before you deploy your applications to the cloud, make sure they are thoroughly tested against a variety of real-world scenarios. This helps to ensure that they are reliable and will meet customer expectations. On the one hand, where elasticity works well in public cloud environments, stability has been known to work the best in private cloud environments.

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