Cloud Scalability Vs Cloud Elasticity

This also allows for additional sudden and unanticipated sales activities throughout the year if needed without impacting performance or availability. This can give IT managers the security of unlimited headroom when needed. This can also be a big cost savings to retail companies looking to optimize their IT spend if packaged well by the service provider. To scale horizontally , you add more resources like servers to your system to spread out the workload across machines, which in turn increases performance and storage capacity. Horizontal scaling is especially important for businesses with high availability services requiring minimal downtime. Elasticity follows on from scalability and defines the characteristics of the workload.

Companies that seek elastic computing are often smaller and use public clouds to achieve the elastic solutions they need. 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. As more and more organizations look to hybrid cloud environments, scalability and elasticity needs can delineate which services belong in a public cloud environment and which can be handled by the enterprise.

Scalability vs Elasticity

Scalability is pretty simple to define, which is why some of the aspects of elasticity are often attributed to it. Many of the services in AWS are scalable by default, which is one of the reasons that AWS is so successful. While these two processes may sound similar, they differ in approach and style. Make all posts by lazypro less visible lazypro consistently posts content that violates DEV Community 👩‍💻👨‍💻’s code of conduct because it is harassing, offensive or spammy. Resource usage fluctuates dramatically over time, with spikes occurring at certain times. Take an e-commerce site for example, when there is a Black Friday sale or Christmas coming up, the system usage will be much higher than usual.

Conclusion Of Cloud Elasticity In Cloud Scalability

If every 1,000 users you get, you need 2x the amount of servers, then it can be said your design does not scale, as you would quickly run out of money as your user count grew. Simply put, elasticity adapts to both the increase and decrease in workload by provisioning and de-provisioning resources in an autonomous capacity. But autoscaling is not a long-term solution, and additional analysis, design and implementation costs are required to fundamentally solve the scalability issue. It is totally different from what you have read above in Cloud Elasticity.

But the definition of scalability and elasticity in cloud computing is not complete without understanding the clear connection between both these terms. Vertical scale, e.g., Scale-Up – can handle an increasing workload by adding resources to the existing infrastructure. Before you learn the difference, it’s important to know why you should care about them. If you’re considering adding cloud computing services to your existing architecture, you need to assess your scalability and elasticity needs.

  • Elasticity follows on from scalability and defines the characteristics of the workload.
  • For example, if a virtual machine in Azure fails due to a hardware failure on the physical host, the Fabric Controller moves that VM to another physical node based on the same hard disk stored in Azure storage.
  • Because the highest point of the blue line is the limit of what the system can record, and more rejected user requests cannot enter the system and are not recorded.
  • This also allows for additional sudden and unanticipated sales activities throughout the year if needed without impacting performance or availability.
  • Now due to flash crowds, a single machine is no longer sufficient to serve all users.

In other words, how to make this line as straight and as close to the horizontal as possible is the issue to be addressed in solving the scalability. For a cloud to be a real cloud, rapid Elasticity is required instead Scalability vs Elasticity of just Elasticity. As long as it can be flexible, it’s always an accurate cloud system. In other words, it is the ability of a system to remain responsive during significantly high instantaneous spikes in user load.

What Is The Difference Between Elasticity And Scalability?

The real difference between scalability and elasticity lies in how dynamic the adaptation. Scalability responds to longer business cycles, such as projected growth. Elasticity can handle the up-and-down nature of website hits, sales demand, and similar business needs in a rapid and often automated manner. Organizations with sudden or cyclical changes will most often need elastic capabilities in at least some areas. Scalability handles the increase and decrease of resources according to the system’s workload demands.

Use Horizontal Scalability when you’re using distributed systems such as web applications. E.g., in AWS Scale Up / Scale Down by increasing the size of the instance from t2.nano instance to u-12tb1.metal. Generally, the website is unpopular and a single machine is sufficient to serve all web users. Scaling TypesManual scaling – specify only the changes in maximum, minimum, or desired capacity of auto scaling groups. Allows you to match the supply of resources—which cost money—to demand. Say we have a system of 5 computers that does 5 work units, if we need one more work unit to be done we we’ll have to use one more computer.

It is not practical to use where persistence resource infrastructure is required to handle the heavy workload. The balance can shift further toward on-premises for the right use cases when IT also controls data center costs, including IT hardware maintenance. Many ERP systems, for example, need to be scalable but not exceptionally elastic. Running them on owned, not pay-for-use, equipment—even in a virtualized, self-provisioning, and other “cloudy” environment—is often the best answer. Cloud elasticity is a cost-effective solution for organizations with dynamic and unpredictable resource demands. Something can have limited scalability and be elastic but generally speaking elastic means taking advantage of scalability and dynamically adding removing resources.

Scalability vs Elasticity

Also, if a new computer is purchased and the extra work unit is not needed any more, the system get stuck with a redundant resource. Not all AWS services support elasticity, and even those that do often need to be configured in a certain way. Scalability is required for elasticity, but not the other way around.

Elasticity Vs Scalability Vs Agility Vs High Availability Vs Fault Tolerance Vs Disaster Recovery

Microsoft already has pre-provisioned resources we can allocate; we begin paying for those resources as we use them. Opposite to this, if your business is selling software or a small company with predefined growth throughout the year, you should not worry about elastic cloud computing. Having a predictable workload where capacity planning and performance are stable and have the ability to predict the constant workload or a growth cloud scalability may be the better cost saving choice.

Scalability vs Elasticity

Cloud environments (AWS, Azure, Google Cloud, etc.) offer elasticity and some of their core services are also scalable out of the box. Executed properly, capitalizing on elasticity can result in savings in infrastructure costs overall. Environments that do not experience sudden or cyclical changes in demand may not benefit from the cost savings elastic services offer. Use of “Elastic Services” generally implies all resources in the infrastructure be elastic. This includes but not limited to hardware, software, QoS and other policies, connectivity, and other resources that are used in elastic applications.


In short, the amount of resources allocated are there to handle the heaviest predicted load without a degradation in performance. To scale vertically , you add or subtract power to an existing virtual server by upgrading memory , storage or processing power . This means that the scaling has an upper limit based on the capacity of the server or machine being scaled; scaling beyond that often requires downtime. 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. Advanced chatbots with Natural language processing that leverage model training and optimization, which demand increasing capacity.

Scalability includes the ability to increase workload size within existing infrastructure (hardware, software, etc.) without impacting performance. These resources required to support this are usually pre-planned capacity with a certain amount of headroom built in to handle peak demand. Scalability also encompasses the ability to expand with additional infrastructure resources, in some cases without a hard limit. Scalability can either be vertical (scale-up with in a system) or horizontal (scale-out multiple systems in most cases but not always linearly). Therefore, applications have the room to scale up or scale out to prevent a lack of resources from hindering performance.

What Is The Difference Between Scalability And Elasticity?

In resume, Scalability gives you the ability to increase or decrease your resources, and elasticity lets those operations happen automatically according to configured rules. Scaling your resources is the first big step toward improving your system’s or application’s performance, and it’s important to understand the difference between the two main scaling types. Learn more about vertical vs. horizontal scaling and which should be used when. Scalability and elasticity represent a system that can grow in both capacity and resources, making them somewhat similar. The real difference lies in the requirements and conditions under which they function. Usually, this means that hardware costs increase linearly with demand.

Types Of Scalability: An Overview

Adding and upgrading resources according to the varying system load and demand provides better throughput and optimizes resources for even better performance. There are an expected number of desktops based on employee population. To ensure the ability to support the maximum number of users and meet SLAs, the amount of services purchased must be enough to handle all users logged in at once as a maximum use case.

Cloud scalability is not hampered by a company’s physical hardware resources. Whereas the physical nature of hardware made scaling a slower process, in the cloud, scalability is much more efficient and effective. The ten machines that are currently allocated to the website are mostly idle and a single machine would be sufficient to serve the few users who are accessing the website. An elastic system should immediately detect this condition and remove nine machines and release them to the cloud. AWS, Microsoft Azure, Google Cloud, or other providers can easily ramp up servers to stream the exciting conclusion to your expensive Superbowl ad.

Thanks to elasticity, Netflix can spin up multiple clusters dynamically to address different kinds of workloads. But some systems (e.g. legacy software) are not distributed and maybe they can only use 1 CPU core. So even though you can increase the compute capacity available to you on demand, the system cannot use this extra capacity in any shape or form. But a scalable system can use increased compute capacity and handle more load without impacting the overall performance of the system. Usually, when someone says a platform or architectural scales, they mean that hardware costs increase linearly with demand. For example, if one server can handle 50 users, 2 servers can handle 100 users and 10 servers can handle 500 users.

With scale, you add resources and keep them whether you use them or not; with elasticity, you have a base state and then use more of what you need, when you need it, and return to a ‘normal’ state otherwise. High Availability or Availability usually goes hand-in-hand with Horizontal Scaling. Horizontal Scaling means running the application in at least 2 availability zones . 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. Cloud service providers typically offer a service level agreement that guarantees HA or uptime of resources and services as a percentage.

Scalability and elasticity are the most misunderstood concepts in cloud computing. Know what exactly they are and the main differences between them. When resource usage does not rise linearly, but exponentially, something is wrong. It could be a bottleneck in usage that results in additional overhead, or a dramatic drop in performance when the amount of data exceeds a certain amount. It is a mixture of both Horizontal and Vertical scalability where the resources are added both vertically and horizontally. It goes without saying that Automation Tools play a big part in enabling elasticity and fault tolerance.

Common use cases where cloud elasticity works well include e-commerce and retail, SaaS, mobile, DevOps, and other environments that have ever changing demands on infrastructure services. The purpose of Elasticity is to match the resources allocated with actual amount of resources needed at any given point in time. Scalability handles the changing needs of an application within the confines of the infrastructure via statically adding or removing resources to meet applications demands if needed. In most cases, this is handled by adding resources to existing instances—called scaling up or vertical scaling—and/or adding more copies of existing instances—called scaling out or horizontal scaling. In addition, scalability can be more granular and targeted in nature than elasticity when it comes to sizing.