Difference Between Scalability And Elasticity In Cloud Computing

If your service has an outage because of insufficient resources, you’ve failed your end-users, and having elasticity working on your system is the prudent choice. This means that the ability to scale a system, which is the ability to increase or decrease resources, is required before a system can be elastic. Elasticity is the system’s ability to take advantage of that scaling ability appropriately and rapidly to demand. However, if you are running a system that is scalable but not elastic, then you are, by definition, not running a cloud. Some of the real time examples for your system to be Elasticity ready are retail services sales like Christmas, Black Friday, Cyber Monday, or Valentine’s day.

elasticity vs scalability

These cloud systems are nifty services that offer users their professional storage services. Basically, with their help, you can store your data in massive quantities without burdening your own hardware. All of this is part of the popular SaaS (Storage-as-a-Service) and IaaS (Infrastructure-as-a-Service) models used by many businesses.

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If the business raises its prices up or down, consumers’ buying habits will remain mostly unchanged. This can impact demand and total revenue for a business in a couple of different ways. With most modern public clouds, you can use a managed service, such as MongoDB Atlas, to make it easily scale applications scalability vs elasticity both horizontally and vertically. If you have relatively stable demand for your products or services online, cloud scalability alone may be sufficient. Cloud elasticity combines with cloud scalability to ensure both customers and cloud platforms meet changing computing needs as and when required.

elasticity vs scalability

As a general go-to rule, elasticity is provided through public cloud services, while scalability is provided through private cloud services. Elasticity provides the functionality to automatically increase or decrease resources to adapt dynamically based on the workload’s demands. Even though it could save some on overall infrastructure costs, elasticity isn’t useful for everyone. Services that do not exhibit sudden changes in workload demand may not fully benefit from the full functionality that elasticity provides. For example, there is a small database application supported on a server for a small business. Over time as the business grows so will the database and the resource demands of the database application. In other words, scale up performance without having to worry about not meeting SLAs in a steady pay-as-you-grow solution.

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You can use either one of them and it wouldn’t matter because they are synonymous, right? A fault-tolerant cloud system will be able to manage to quickly shift to another copy of the server in the data-center whenever any failure is identified. As work from home became a part and employees were forced to go remote, tasks were largely done on cloud infrastructure. Internal usage – application team using development and test environments. Scalability is the peak of how many resources can be dedicated and consumed by a task. High Availability or Availability usually goes hand-in-hand with Horizontal Scaling.

Use Vertical Scalability when you’re using a non-distributed system such as a database. For this purpose, AWS offers RDS, ElastiCache services that can scale vertically. Both, Scalability and Elasticity refer to the ability of a system to grow and shrink in capacity and resources and to this extent are effectively one and the same. The difference is usually in needs and conditions under which this happens. Scalability is mostly manual, predictive and planned for expected conditions. Elasticity is automatic and reactive to external stimuli and conditions. Elasticity is automatic scalability in response to external conditions and situations.

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With cloud elasticity, users avoid paying for unused capacity or idle resources while maintaining the ability to scale up and respond to peaks in demand for their systems. Scalability can either be vertical (scale-up within a system) or horizontal (scale-out multiple systems). Because of this, applications have room to scale up or scale-out to prevent anything from hindering performance. Of course, there are cases where the IT manager knows that they will no longer need resources, and scale down the infrastructure statically to maintain a smaller environment. For example, a small business has a small database application supported on a server. As the business grows, so will the database and the resource demands. If a business or IT manager is able to predict the business’s growth rate, they can purchase provisioned infrastructure so that the database application has room to grow to its maximum capacity.

elasticity vs scalability

While scalability vs elasticity needs to be considered, there are some similarities that need to be highlighted too. Both of them are adaptable solutions for organizations, but they have specific differences.

Scalability And Elasticity In Cloud Computing

By the time our very competent engineer has the additional servers online, there have been outages, and it also takes a while to scale back down. The demand for infrastructure resources – compute, storage, and network – are often not static in nature. Users sometimes access websites more often at certain times of the day. The ability to scale up and scale down is related to how your system responds to the changing requirements. Elastically in the context of cloud computing, it is required that the scaling of the system is quick, and it means the variable demands that the system exhibit. To scale vertically , you add or subtract power to an existing virtual server by upgrading memory , storage or processing power .

  • It will address issues of traditional centralized networks and lead the way for the next generation of CoT technologies.
  • Elasticity is used to describe how well your architecture can adapt to workload in real time.
  • Economists use price elasticity of demand to measure demand sensitivity as a result of price changes for a given product.

Advertising elasticity of demand measures a market’s sensitivity to increases or decreases in advertising saturation and its effect on sales. The cross elasticity of demand measures the responsiveness in the quantity demanded of one good when the price changes for another good. Normally, a price increase does, in fact, lead to a decrease in quantity demanded . So, businesses that deal with inelastic goods are generally able to increase their prices, sell a little less, and still make higher revenues.

Cloud Computing: Elasticity Vs Scalability

Depending on how much change in demand a system experiences, it is quite possible that adding or deleting application instances can provide the rapid elasticity needed. However, even when you aren’t using underlying resources, you are often still paying for them. Consider applications in the enterprise where you might want to run reports at a certain time of the week or month. Naturally, at those times, you will require more resources; but do you really want to pay for the larger machines or more machines to be running all the time? This is a major area where cloud computing can help, but we need to take into account the workload.

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Elasticity is a measure of how quick and easy it is to increase and decrease the resources dedicated to performing some task. Let’s say I work for the HBO and I know a lot of HBO customers will be opening a new account just to see Game of Thrones. Now, I know a lot of people will be buying the membership of HBO and creating accounts for the first time – this is where I will increase the size of the instance by using Vertical Scaling.

Horizontal Scaling means running the application in at least 2 availability zones . E.g., in AWS, Scale-Out / Scale In by using the Auto Scaling Groups or Load Balancers. On the other hand, High Availability means that we’re running the application/system in at least 2 data centers i.e. Horizontal Scaling means you’re increasing the number of instances.

In this case, cloud scalability is used to keep the system’s resources as consistent and efficient as possible over an extended time and growth. The notification Institution of Engineering and Technology triggers many users to get on the service and watch or upload the episodes. Resource-wise, it is an activity spike that requires swift resource allocation.

FO is pleased we’ve cut our idle infrastructure cost in half; she still sees some cost savings that should be attainable. On top of that, our head of engineering and CEO is not pleased that we are again in a state where we are having outages and the work of manually scaling up and down in response to system changes is tedious work. We have achieved cloud scaling, but are not yet at a point of true cloud elasticity. The ability to scale up is not as efficient as reacting swiftly to a downtime or service shutdown.

A new movie or a season of a famous show could mean a sudden traffic surge of people logged in to watch Netflix on the weekend. This sudden spike can be handled by a surge of compute resources provisioned for a small amount of time. 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. 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.

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