Cloud Elasticity Vs Cloud Scalability

When loads are low, auto scaling allows both companies managing their own infrastructure and businesses that rely on cloud infrastructure to send some servers to sleep. This reduces electricity costs and water costs where water is used in cooling. Cloud auto scaling also means paying for total usage instead of maximum capacity. When it comes to elasticity, a cloud solution can bring more resources.

  • Scalability is an essential factor for a business whose demand for more resources is increasing slowly and predictably.
  • The group itself is an auto scaling group, with each instance in the group subject to those auto scaling policies.
  • The hospital’s services are in high demand, and to support the growth, they need to scale the patient registration and appointment scheduling modules.
  • Not just have elasticity for traffic bursts, but scale for growth.
  • For this reason, it is often the preferred approach for cloud-based businesses.
  • Vertical scale, e.g., Scale-Up - can handle an increasing workload by adding resources to the existing infrastructure.
  • Both of which are benefits of the cloud and also things you need to understand for the AZ-900 exam.

Elasticity is a defining characteristic that differentiates cloud computing from previously proposed computing paradigms, such as grid computing. The dynamic adaptation of capacity, e.g., by altering the use of computing resources, to meet a varying workload is called "elastic computing". Scalability is very similar to elasticity but it's on a more permanent, less makeshift type scale. With scalability in the cloud you can move in lots of directions, so you can scale up or scale out. Because it involves adding more power to an existing machine, there are inherent architectural challenges to vertical auto scaling.

Changes to an auto scaling group’s desired capacity might be fixed or incremental. Incremental changes decrease or increase by a specific number rather than setting an end value. Scaling up policies, also called scaling out policies, increase desired capacity. Scaling down policies, also called scaling in policies, decrease desired capacity.

As long as the capacity of this hotel is not exceeded, no problem. However, with the sheer number of services and distributed nature, debugging may be harder and there may be higher maintenance costs if services aren’t fully automated. The owners just migrated the business to the cloud and were a bit concerned knowing that Black Friday means more traffic in a short period. It will remain in an idle mode until the platform has a spike in traffic once again.

Microservices Architecture

Or cut some of the resources to meet the demands of your business. If you have more traffic on your platform, you will need to bring more servers. Storage resource demand is, for the most part, a lumpy, non-linear process with imperfect predictability -- there are always ebbs and flows. Some applications may require peak resources at the end of a quarter or during the early morning hours. Others may not require peak resources except during a specific quarter during the year, such as retail. Elasticity allows the system to respond to the "lumpiness" of the demand cost-effectively.

difference between Elasticity and scalability

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This architecture views each service as a single-purpose service, giving businesses the ability to scale each service independently and avoid consuming valuable resources unnecessarily. For database scaling, the persistence layer can be designed and set up exclusively for each service for individual scaling. Most monolithic applications use a monolithic database — one of the most expensive cloud resources. Cloud costs grow exponentially with scale, and this arrangement is expensive, especially regarding maintenance time for development and operations engineers.

Cloud Computing Mcq

There is no redundant server, and application health is tied to the machine’s single location. Vertical scaling also demands downtime for reconfigurations and upgrades. Finally, vertical auto scaling enhances performance, but not availability. Scalability is one of the driving reasons for migrating to the cloud.

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 consider. Another criterion that has been added to the list recently is cloud scalability and cloud elasticity.

In the above example, under-provisioning the website may make it seem slow or unreachable. Web users eventually give up on accessing it, thus, the service provider loses customers. On the long term, the provider's income will decrease, which also reduces their profit.

Benefits Of Cloud Scalability

In these cases, horizontal auto scaling adds more machines to the resource pool. Load balancing, distributed file systems, and clustering are all part of effective horizontal auto scaling. Core autoscaling features also allow lower cost, reliable performance by seamlessly increasing and decreasing new instances as demand spikes and drops. As such, autoscaling provides consistency despite the dynamic and, at times, unpredictable demand for applications. Auto scaling and load balancing are related because an application typically scales based on load balancing serving capacity.

difference between Elasticity and scalability

Horizontal auto scaling does not demand downtime, in that it creates independent new instances. It also increases availability as well as performance due to this independence. Stateless servers are important for applications which typically have many users. User sessions should ideally never be tied to one server, and should instead be able to move seamlessly across many servers while maintaining a single session. This kind of browser-side session storage allows for better user experience and is one of the results of good horizontal scaling. Horizontal auto scaling refers to adding more servers or machines to the auto scaling group in order to scale.

Cloud scalability is an effective solution for businesses whose needs and workload requirements are increasing slowly and predictably. Сloud elasticity is a system's ability to manage available resources according to the current workload requirements dynamically. It is totally different from what you have read above in Cloud Elasticity. Scalability is used to fulfill the static needs while elasticity is used to fulfill the dynamic need of the organization. Scalability is a similar kind of service provided by the cloud where the customers have to pay-per-use.


If you need your infrastructure to handle sustainable growth year over year, you want scalability. When you’re first starting out as a business, your needs will be very different than a business which has been around for 10 years. You expect a certain level of traffic, a certain number of member accounts in your database when you start. At some point, your business will grow, and your servers need to grow with it. Not only to keep your database intact as those numbers increase, but so that search queries return at the same rate of speed as they always have, no matter how many records there are to search. The advantages of elasticity and scalability mostly lie in what your goals are.

difference between Elasticity and scalability

With database scaling, there is a background data writer that reads and updates the database. All insert, update or delete operations are sent to the data writer by the corresponding service and queued to be picked up. For application scaling, adding more instances of the application with load-balancing ends up scaling out the other two portals as well as the patient portal, even though the business doesn’t need that.


When the platform has more traffic, the cloud uses more virtual machines. But it returns to a single machine when the traffic returns to normal. Storage scalability is commonly measured in terms of capacity and performance.

This means they only need to scale the patient portal, not the physician or office portals. Let’s break down how this application can be built on each architecture. Unlike elasticity, which is more of makeshift resource allocation - cloud scalability is a part of infrastructure design. System scalability is the system's infrastructure to scale for handling growing workload requirements while retaining a consistent performance adequately.

Both scalability and elasticity are related to the number of requests that can be made concurrently in a cloud system — they are not mutually exclusive; both may have to be supported separately. Scalability handles the scaling of resources according to the system's workload demands. In the past, a system's scalability relied on the company's hardware, and thus, was severely limited in resources. With the adoption of cloud computing, scalability has become much more available and more effective.

That could look like shopping on an ecommerce site during a busy period, ordering an item, but then receiving an email saying it is out of stock. Asynchronous messaging and queues provide back-pressure when the front end is scaled without scaling the back end by queuing requests. Cloud elasticity is a cost-effective solution for organizations with dynamic and unpredictable resource demands.

While the owners were prepared and spent a lot of money on the servers, the traffic increased rapidly to 7000 users. Physical servers have a maximal storage space, and they can handle a certain amount of traffic on a platform. If the traffic reaches a certain point, the server can break down and stop users from purchasing items from the store. Although you might believe that cloud services are far more expensive than a regular server, you are going to see that it’s not true. Figure 1 describes under and over provisioning plus the ideal of elasticity.

Now once I have architected my app to embrace scalability – I deploy it on Windows Azure. And here, Windows Azure gives my app the “elasticity” it may need. Event-driven architecture is better suited than monolithic architecture for scaling and elasticity. For example, it publishes an event when something noticeable happens.

Scale In The Cloud

If you need a server to handle individual spikes in traffic, or bursting, then you want elasticity. If a traffic spike happens an elastic system can spin up another scalability vs elasticity server to help handle the increased traffic and temporarily assist with the spike. When the spike dies down, everything automatically goes back to the way it was.

Consistent Performance

You can deploy an auto scaling group load balancer to improve availability and performance, and decrease application latency. Vertical scaling involves scaling up or down and is used for applications that are monolithic, often built prior to 2017, and may be difficult to refactor. It involves adding more resources such as RAM or processing power to your existing server when you have an increased workload, but this means scaling has a limit based on the capacity of the server. It requires no application architecture changes as you are moving the same application, files and database to a larger machine.

The first step is moving from large monolithic systems to distributed architecture to gain a competitive edge — this is what Netflix, Lyft, Uber and Google have done. However, the choice of which architecture is subjective, and decisions must be taken based on the capability of developers, mean load, peak load, budgetary constraints and business-growth goals. When it comes to scalability, businesses must watch out for over-provisioning or under-provisioning. This happens when tech teams don’t provide quantitative metrics around the resource requirements for applications or the back-end idea of scaling is not aligned with business goals. To determine a right-sized solution, ongoing performance testing is essential. While many businesses have a set daily, weekly, or yearly cycle to govern server use, auto scaling is different in that it reduces the chance of having too many or too few servers for the actual traffic load.

Migrating legacy applications that are not designed for distributed computing must be refactored carefully. Horizontal scaling is especially important for businesses with high availability services requiring minimal downtime and high performance, storage and memory. Elastic load balancing and application autoscaling are closely related. In fact, it is common to see solutions that include a load balancer with autoscaling features.

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