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What does cloud elasticity mean and how does cloud elasticity help us in the cloud? Here’s your in-depth guide.
One of the most significant differences between on-premise and cloud computing is that you don’t need to buy new hardware to expand your cloud-based operations as you would for an on-prem system. But there is something else.
Cloud computing is so flexible that you can allocate varying compute resources with changes in demand. For example, you can buy extra online storage for your chatbot system as you receive increasing customer inquiries over time. You’d pay for as much online storage as you use.
The additional storage would help your bots collect more data in one place. Then, if you use machine learning and big data analytics, the bots would rapidly query the data and find best-fit responses to relevant questions.
But this ability is often confusing. Is it cloud elasticity? Or is it cloud scalability?
This guide will explain what cloud elasticity is, why and how it differs from scalability, and how elasticity is used. We’ll also cover specific examples and use cases, the benefits and limitations of cloud elasticity, and how elasticity affects your cloud spend.
Table Of Contents
Cloud elasticity is the ability to gain or reduce computing resources such as CPU/processing, RAM, input/output bandwidth, and storage capacities on demand without causing system performance disruptions. This is often an automatic process in cloud computing.
An elastic cloud service will let you take more of those resources when you need them and allow you to release them when you no longer need the extra capacity.
The elasticity process often needs to happen quickly. Delaying expansion would lead to server overloads and outages. On the other hand, if you delay shrinking, some of your servers would lie idle, which is a waste of your cloud budget.
When a cloud provider matches resource allocation to dynamic workloads, such that you can take up more resources or release what you no longer need, the service is referred to as an elastic environment. The process is referred to as rapid elasticity when it happens fast or in real-time.
Public cloud providers such as Amazon Web Services (AWS) and Google Cloud support rapid elasticity. The quicker a cloud provider can allocate varying resources to dynamic customer demands, the more elastic its cloud services are.
You can take advantage of cloud elasticity in four forms; scaling out or in and scaling up or down.
Scaling up or down refers to vertical scalability. This then refers to adding/removing resources to/from an existing infrastructure to boost/reduce its performance under a changing workload. Scaling out or in refers to expanding/shrinking an existing infrastructure’s resources by adding new/removing existing components. This is also referred to as horizontal scalability.
Now, you may think “that sounds a lot like cloud scalability.” Well, cloud elasticity and cloud scalability are both fundamental elements of the cloud. But many people often mistakenly use them interchangeably. They are not the same thing. Here’s why.
Picture a restaurant in an excellent location. It can seat up to 30 customers, including outdoor seating. Customers come in and go throughout the day. So the restaurant rarely exceeds its seating capacity.
But the staff adds a table or two at lunchtime and dinner when more people stream in with an appetite. They then remove the tables and chairs to declutter the space. The restaurant scales up and down its seating capacity within the confines of the space it occupies.
Now, picture this. A nearby center hosts a bi-annual event that attracts hundreds of attendees for a week-long convention.
The restaurant often sees a traffic surge during the convention weeks. The demand is usually so high that it has to turn customers away. The restaurant has let those potential customers down for two years in a row. It often loses the business and customers to nearby competitors.
But this year the restaurant came up with an idea.
They agreed with a nearby building’s management to lease enough room to seat 33 more people for the week. The available space is on that building’s second floor. But there it offers high-speed lifts, allows for a commercial kitchen setup, and flexible leasing plans that the restaurant found feasible.
After serving the most customers ever for the entire week, the restaurant decides to keep the extra space they leased. But a month later, the management concludes the space is not profitable enough to keep open around the year save for the conventions’ duration. So they take advantage of the flexible leasing clause and vacate at the end of that month. No fines.
That is how cloud elasticity is different from cloud scalability, in a nutshell.
Cloud scalability works the same way a restaurant adds or reduces tables and chairs inside the space they have, depending on the traffic they welcome.
In cloud computing, that is like scaling compute resources up or down inside a server to suit an increase or reduction in workload at different hours, days, or seasons — without degrading customer experiences.
But unlike a restaurant where your landlord expects you to pay for the entire space, whether or not you actively use all of it, a cloud platform will only charge you for the compute resources you use.
Cloud service providers have scalability built into their platforms’ architecture, such that they can automatically allocate processing, RAM, storage, and bandwidth to match your changing workload and without deteriorating performance.
Still, there is only so much space to add chairs and tables in a confined room, just as there is a limit to the amount of hardware you can add to a server.
Remember how the restaurant in our analogy leased additional space? The new space allowed it to accommodate 33 more people and install a temporary kitchen.
Cloud elasticity is similar in that instead of sending new business away when your provisioned server is running at full capacity, you can deploy new resources such as virtual machines within one server to handle changing workloads rapidly.
Each virtual machine would have scaling capabilities just as the newly leased restaurant’s staff could add or remove chairs and tables within the leased space. You could increase or reduce computing resources as you need with zero downtime in each of those servers.
Now, you’ve probably noticed this by now. Cloud elasticity and cloud scalability go hand-in-hand.
Before a system can be elastic, it needs to be scalable. Elasticity then swoops in to ensure the scaling happens appropriately and rapidly.
Cloud elasticity helps users prevent over-provisioning or under-provisioning system resources. Over-provisioning refers to a scenario where you buy more capacity than you need.
Under-provisioning refers to allocating fewer resources than you use.
Over-provisioning leads to cloud spend wastage, while under-provisioning can lead to server outages as available servers are overworked. Server outages lead to revenue losses and customer dissatisfaction, both of which are bad for business.
Scaling with elasticity provides a middle-ground.
Elasticity is ideal for short-term needs, such as handling website traffic spikes and doing database backups.
But elasticity also helps smooth out service delivery when combined with cloud scalability. For example, by spinning up additional VMs in a single server, you create more capacity in that server to handle dynamic workload surges.
So, how does cloud elasticity work in business environments?
Three excellent examples of cloud elasticity at work include e-commerce, insurance, and streaming services.
Say you are in the auto insurance business. Perhaps your customers renew auto policies at around the same time annually. You can expect a surge in traffic when that time comes around. Policyholders would be rushing to beat the renewal deadline.
If you relied on scalability alone, the traffic spike could quickly overwhelm your provisioned virtual machine, causing service outages. That would cause a loss of revenue and customers.
But if you “leased” a few more virtual machines, you could handle the traffic for the entire policy renewal duration. Thus, you would have several scalable virtual machines to manage demand in real-time.
Policyholders wouldn’t notice any changes in performance whether you served more customers this year than the previous year. You could then release some of those virtual machines when you no longer need them, such as during off-peak months, to reduce cloud spend.
An elastic cloud platform would let you do that. It would also charge you on a pay-per-use basis for only the resources you used and not the number of virtual machines you deployed.
Say you run a limited-time offer on notebooks to mark your anniversary, Black Friday, or a tech festival. You can expect more traffic and server requests during that time. The more effectively you run your awareness campaign, the more the potential buyers’ interest you can expect to peak.
New shoppers would register new accounts. Existing customers would also revisit old wishlists, abandoned carts, or try to redeem accumulated points. This would put a lot more load on your servers during the campaign’s duration than at most times of the year.
With an elastic platform, you could provision more resources to absorb the higher festive season demand. After that, you could return the extra capacity to your cloud provider and keep what’s workable in everyday operations.
Netflix is perhaps the best example to use here. When the streaming service released all 13 episodes of the second season of House of Cards, viewership soared to a startling 16% of Netflix subscribers compared to just 2% for the first season’s premiere weekend.
Those subscribers streamed one of those episodes within a seven to ten-hour period that Friday. Now, Netflix had over 50 million subscribers around that time (February, 2014). So a 16% bump in viewership meant over 8 million subscribers streamed a portion of the show within a workday in a single day.
Netflix engineers have repeatedly said they take advantage of elastic cloud services by AWS to serve such numerous server requests within a short time and with zero downtime.
Bottom line: If your cloud provider offers cloud elasticity by default, and you’ve activated the feature in your account, the platform would allocate you unlimited resources at any time. That means you would be able to handle both sudden and expected workload spikes at any time.
There are several powerful benefits of elasticity in the cloud.
An elastic cloud provider provides system monitoring tools that track resource utilization. They then automatically analyze utilization vs resource allocation. The goal is always to ensure these two metrics match up to ensure the system performs at its peak and cost-effectively.
Cloud providers also price it on a pay-per-use model, allowing you to pay for what you use and no more. The pay-as-you-expand model would also let you add new infrastructure components to prepare for growth.
Cloud elasticity combines with cloud scalability to ensure both customers and cloud platforms meet changing computing needs as and when required.
While scalability helps handle long-term growth, elasticity ensures flawless service availability at present. It also helps prevent system overloading or runaway cloud costs due to over-provisioning. For cloud platforms, elasticity helps keep customers happy.
But, what are the limitations or disadvantages of cloud elasticity?
Cloud elasticity may not be for everyone. If you have relatively stable demand for your products or services online, cloud scalability alone may be sufficient.
For example, if you run a business that doesn’t experience seasonal or occasional spikes in server requests, you may not mind using scalability without elasticity. Keep in mind elasticity requires scalability, but not the reverse.
Yet, nobody can predict when you may need to take advantage of a sudden wave of interest in your company. So, what do you do when you need to be ready for that opportunity but do not want to waste your cloud budget speculating? Enter cloud cost optimization.
Elasticity uses dynamic variations to align computing resources to workload demands as closely as possible to prevent overprovision wastage and boost cost-efficiency. Another goal is usually to ensure your systems can continue to serve customers satisfactorily, even when bombarded by massive, sudden workloads.
But not all cloud platform services support the scaling in and out involved in cloud elasticity.
Take Amazon Web Services (AWS), for example. Some AWS services include elasticity as a part of their offering, such as Amazon Simple Storage Service (S3), Amazon Simple Queue Service (SQS), and Amazon Aurora. Amazon Aurora Serverless qualifies as elastic, while others such as Amazon Elastic Compute Cloud (EC2) integrate with Amazon Auto Scaling and support elasticity.
Whether or not you use an elastic service to cut cloud costs dynamically, you’ll want to increase your cloud costs visibility in a way Amazon CloudWatch does not provide.
CloudZero allows engineering teams to drill down and inspect the specific costs and services driving their product, features, and more. You can group costs by feature, product, service, or account to uncover unique insights about your cloud costs that will help you answer what’s changing, why, and what you can do about it.
Additionally, you can also measure and monitor your unit costs like cost per customer. Here’s a look at CloudZero’s Cost Per Customer report, where you can uncover vital cost information about your customers, which can help guide your engineering and pricing decisions:
While you grow, and bring on more and more customers, it’s natural that your cloud spend will increase. What’s important to know is how your unit economics are affected by this growth (whether they remain flat, increase, or decrease) so you can ensure profitability for your company.
Whether it’s uncovering your unit cost, mapping costs to products, features, or teams, or even automatically detecting cost anomalies that might cost you thousands of dollars if left unchecked, CloudZero gives your engineering team the tools they need to make cost-informed decisions.