Cloud Elasticity Vs Cloud Scalability

This can also be a big cost savings to retail companies looking to optimize their IT spend if packaged well by the service provider. Private Cloud is defined as computing services offered either over the Internet or a private internal network and only to select users instead of the general public. Elasticity follows on from scalability and defines the characteristics of the workload.

  • This can give IT managers the security of unlimited headroom when needed.
  • New employees need more resources to handle an increasing number of customer requests gradually, and new features are introduced to the system (like sentiment analysis, embedded analytics, etc.).
  • The big difference between static scaling and elastic scaling, is that with static scaling, we are provisioning resources to account for the “peak” even though the underlying workload is constantly changing.
  • High Availability is the ability of the system to be up and running with very little downtime.
  • As workload resource demands decrease; again, we could have rules that start to scale in those instances when it is safe to do so without giving the user a performance impact.
  • In this model, the customer is responsible for maintaining the servers provisioned by the cloud provider, including software updates and ensuring that patches are applied accordingly.

Elasticity is the ability to automatically or dynamically increase or decrease the resources as needed. Elastic resources match the current needs and resources are added or removed automatically to meet future demands when it is needed. Scalability handles the increase and decrease of resources according to the system’s workload demands.

A PaaS provider hosts the hardware and software on its own infrastructure. As a result, PaaS frees users from having to install in-house hardware and software to develop or run a new application. In the PaaS model, the customer does not need to worry about maintaining the underlying server infrastructure; this is maintained by the cloud provider. The customer is responsible for the application that runs on the PaaS service. 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.

Cloud Computing Concepts

Consider an online shopping site whose transaction workload increases during festive season like Christmas. So for this specific period of time, the resources need a spike up. In order to handle this kind of situation, we can go for Cloud-Elasticity service rather than Cloud Scalability.

scalability and elasticity agility

Elasticity is automatic and reactive to external stimuli and conditions. Elasticity is automatic scalability in response to external conditions and situations. Elasticity is the ability of a system to increase its compute, storage, netowrking, etc. capacity based on specified criteria such as the total difference between scalability and elasticity load on the system. It is a mixture of both Horizontal and Vertical scalability where the resources are added both vertically and horizontally. For example, your application hosted on an App Service, VM or any other compute service, should remain up and running even if underlying server hardware fails.

Organizations gain the flexibility and computing power of the public cloud for basic and non-sensitive computing tasks, while keeping business-critical applications and data on-premises, safely behind a company firewall. Cloud environments (AWS, Azure, Google Cloud, etc.) offer elasticity and some of their core services are also scalable out of the box. Furthermore, if you build a scalable software, you can deploy it to these cloud environments and benefit from the elastic infrastructure they provide you to automatically increase/decrease the compute resources available to you on-demand.

Scalability And Elasticity In Cloud Computing

This is because vertical scaling typically requires a redeployment of an instance or powering down of the instance to make the change, depending on the underlying operating system. Either way, the benefit of doing this in Azure is that we don’t have to purchase the hardware up front, rack it, configure it etc. Rather via clicking in the Azure portal or using code, we can adjust for it. Microsoft already has pre-provisioned resources we can allocate; we begin paying for those resources as we use them. For example, there is a small database application supported on a server for a small business.

scalability and elasticity agility

ELASTICITY – ability of the hardware layer below to increase or shrink the amount of the physical resources offered by that hardware layer to the software layer above. The increase / decrease is triggered by business rules defined in advance (usually related to application’s demands). The increase / decrease happens on the fly without physical service interruption. Once again, Cloud computing, with its perceived infinite scale to the consumer, allows us to take advantage of these patterns and keep costs down. If we can properly account for vertical and horizontal scaling techniques, we can create a system that automatically responds to user demand, allocating and deallocating resources as appropriate. New employees need more resources to handle an increasing number of customer requests gradually, and new features are introduced to the system (like sentiment analysis, embedded analytics, etc.).

In short, on Azure you pay for what resources you use, and stop paying for them when the resources are removed. Traditionally, IT departments could replace their existing servers with newer servers that had more CPUs, RAM, and storage and port the system to the new hardware to employ the extra compute capacity available to it. Usually, when someone says a platform or architectural scales, they mean that hardware costs increase linearly with demand.

A use case that could easily have the need for cloud elasticity would be in retail with increased seasonal activity. For example, during the holiday season for black Friday spikes and special sales during this season 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. This can give IT managers the security of unlimited headroom when needed.

Diagonal scale is a more flexible solution that combines adding and removing resources according to the current workload requirements. Vertical scale, e.g., Scale-Up – can handle an increasing workload by adding resources to the existing infrastructure. Turbonomic allows you to effectively manage and optimize both cloud scalability and elasticity.

Agility Vs Elasticity

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. 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. So, in conclusion, we can say that Scalability is useful where the workload remains high and increases statically.

scalability and elasticity agility

OpEx is spending money on services or products now and being billed for them now. Scalability is meeting predictable traffic demand while elasticity is meeting sudden traffic demand. 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. Scalability refers to the ability for your resources to increase or decrease in size or quantity. While both elasticity and agility refer to being able to allocate and deallocate resources as needed, there is a slight difference.

Disaster Recovery involves a set of policies, tools and procedures to enable the recovery or continuation of vital technology infrastructure and systems following a natural or human-induced disaster. Disaster recovery can therefore be considered as a subset of business continuity. Not all AWS services support elasticity, and even those that do often need to be configured in a certain way. This is what happens when a load balancer adds instances whenever a web application gets a lot of traffic. Elastic is stretching out but at the same time holding on to the past, e.g. legacy systems. Just like stretching out an elastic band, the given context limits how far you can stretch out.

In resume, Scalability gives you the ability to increase or decrease your resources, and elasticity lets those operations happen automatically according to configured rules. Something can have limited scalability and be elastic but generally speaking elastic means taking advantage of scalability and dynamically adding removing resources. Scalability is the ability of the system to accommodate larger loads just by adding resources either making hardware stronger or adding additional nodes . The big difference between static scaling and elastic scaling, is that with static scaling, we are provisioning resources to account for the “peak” even though the underlying workload is constantly changing.

Elasticity Vs Scalability In Cloud Computing: The Final Word

Understanding these differences is very important to ensuring the needs of the business are properly met. Elasticity is related to short-term requirements of a service or an application and its variation but scalability supports long-term needs. Again, scalability is a characteristic of a software architecture related to serving higher amount if workload, where elasticity is a characteristic of the physical layer below, entirely related to hardware budget optimizations. So when the question states the company needs to react, this seems like a manual process.


With the adoption of cloud computing, scalability has become much more available and more effective. It comes in handy when the system is expected to experience sudden spikes of user activity and, as a result, a drastic increase in workload demand. Many have used these terms interchangeably but there are distinct differences between scalability and elasticity.

Scalability Vs Elasticity

Scalability is the ability of a system to scale to support the desired workload. Delivery of services like compute, storage and networking over the internet is known as Cloud Computing, and the provider of such services is known as Cloud Provider. 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. 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. Similarly, you can configure your system to remove servers from the backend cluster if the load on the system decreases and the average per-minute CPU utilization goes below a threshold defined by you (e.g. 30%).

The benefits here are that we don’t need to make changes to the virtual hardware on each machine, but rather add and remove capacity from the load balancer itself. Scaling up, or vertical scaling, is the concept of adding more resources to an instance that already has resources allocated. This could simply mean adding additional CPU or memory resources to a VM. More specifically, perhaps in response to a bunch of users hitting a website, we can simply add more CPU for that day, and then scale down the CPUs the following day. How dynamically this can happen depends on how easy it is for us to add and remove those additional CPUs while the machine is running, or the application team’s ability to take an outage.

The following is a list of useful links which cover the concepts required for the exam objectives. Making statements based on opinion; back them up with references or personal experience. Trending sort is based off of the default sorting method — by highest score — but it boosts votes that have happened recently, helping to surface more up-to-date answers. Agility for many organizations means reacting swiftly to a changing environment.

Scalability is required for elasticity, but not the other way around. Elasticity is the ability for your resources to scale in response to stated criteria, often CloudWatch rules. Many of the services in AWS are scalable by default, which is one of the reasons that AWS is so successful. Disaster Recovery is the ability of a system to recover from a disaster such as floods, thunder-storm, earth-quake, or any other natural or human-induced disaster that has destroyed the primary data center. Scalability and Elasticity both refer to meeting traffic demand but in two different situations.

Scalability is pretty simple to define, which is why some of the aspects of elasticity are often attributed to it. Fault Tolerance is the ability of a system to remain up and running during component and service failure. In the cloud, it takes a minute or two to create a Virtual Machine that is up and running. On the other hand, it takes days or weeks when we submit a request to purchase a physical server and by when it gets delivered. The other topics covered in the exam will be in Part 2 of my blog.

Elastic workloads are a major pattern which benefits from cloud computing. If our workload does benefit from seasonality and variable demand, then let’s build it out in a way that it can benefit from cloud computing. As the workload resource demands increase, we can go a step further and add rules that automatically add instances. As workload resource demands decrease; again, we could have rules that start to scale in those instances when it is safe to do so without giving the user a performance impact. 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.

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. There are cases where the IT manager knows he/she will no longer need resources and will scale down the infrastructure statically to support a new smaller environment. Either increasing or decreasing services and resources this is a planned event and static for the worse case workload scenario.

Describe the benefits and considerations of using cloud services High Availability is a characteristic of a system, which aims to ensure an agreed level of operational performance, usually uptime, for a higher than normal period. This agreed level of operational performance or uptime on Azure is governed by SLAs. As an example, the SLA for a Single Instance Virtual Machines with Premium Storage is 99.9%. Technical IT experience is not required however some general IT knowledge or experience would be beneficial.

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