Cloud computing provides significant benefits over on-premises computing, including the ability to expand operations without purchasing new hardware. But there’s more.
With cloud computing, you can adjust compute resources to meet changing demands. 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.
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.
What Is Elasticity In Cloud Computing?
Elasticity in the cloud refers to the ability of a system to automatically increase or decrease computing resources on demand to meet workload requirements for a given time. These resources include vCPU, RAM, and network bandwidth.
Cloud elasticity enables you to access more resources when necessary and release them when they are no longer needed.
The elasticity process often needs to happen quickly.
- A delay in increasing capacity could overload your system, potentially causing service outages.
- In contrast, if you delay shrinking, some of your servers will have little to do. But you’d still have to pay for the idle capacity, which is a waste of your cloud budget.
Elastic environments match resource allocation to dynamic workloads, allowing you to take up more resources or release those you no longer need. If the process occurs quickly or in real time, it is called rapid elasticity.
Public cloud providers such as Amazon Web Services (AWS) and Google Cloud Platform support rapid elasticity. The quicker a cloud service provider can allocate resources to dynamic customer demands, the more elastic its cloud services are.
What Is Scalability In Cloud Computing?
Scalability refers to a system’s ability to grow or contract at the infrastructure level instead of at the resources level (elasticity).
Here, the system uses virtualization technology to automatically increase or decrease its capacity to handle more or less workload.
Additionally, scaling this way enables systems to be easily expanded or contracted as needed, without needing to replace existing hardware or applications. The scaling happens at the software level, not necessarily at the hardware level.
Scalability is essential for applications that require high availability and performance because it enables them to adapt to changing user demands.
Most major cloud providers have built automatic scaling (auto-scaling) into their infrastructure to help their customers meet their cloud price-performance goals.
Types Of Elasticity In Cloud Computing
Elasticity can be divided into two categories; vertical and horizontal scalability.
Vertical scaling refers to increasing or decreasing a system’s power up or down. This involves increasing or decreasing resources, such as vCPU, memory, and network capacity in real-time to match the desired performance level under changing loads.
On the other hand, horizontal scaling involves expanding or shrinking existing infrastructure capacity by adding or removing existing components. Consider the following illustration.
We’ve also gone into more detail about the differences between horizontal and vertical scaling here.
Now, you may think “elasticity sounds a lot like cloud scalability.”
But they are not the same thing.
Scalability Vs. Elasticity In Cloud Computing
Some quick differences between scalability and elasticity include:
|A system’s ability to automatically increase or decrease infrastructure capacity over time to handle increased or decreased loads
|A system’s ability to automatically increase or decrease cloud resources in real-time, often within each virtual server, such as vCPU, memory, and network bandwidth, to meet changing workload requirements
|It involves provisioning or de-provisioning infrastructure capacity, such as increasing the number of nodes in a cluster to maintain a certain level of performance under increased load (e.g. spinning up virtual machines from 2 to 4)
|It involves increasing or decreasing computing power within a server to maintain a desired performance level under increased or decreased load (e.g. increasing memory from 2GiB to 6GiB)
|Reconfigures infrastructure components without replacing hardware to expand or release infrastructure capacity
|Involves adjusting the amount of computing resources within the provisioned server(s) in accordance with real-time user or application demand
|Often associated with a system’s ability to increase or decrease capacity over a longer period
|Often about adapting to on-demand or real-time demand, such as a sudden spike in website visits
|Suitable for more stable and pre-planned use cases
|Suitable for applications and business use cases with unpredictable or variable workloads or fluctuating performance requirements
Differences between elasticity and scalability in cloud computing
Restaurant analogy for comparing elasticity and scalability in the cloud
Picture a restaurant in an excellent location. It can seat up to 30 customers. Customers come in and go throughout the day.
As more people stream in for lunch and dinner, the staff adds a table or two inside. Afterward, they declutter the space by removing the tables and chairs.
Similar to how a restaurant will add or reduce seating capacity inside based on the traffic they get in real-time, cloud elasticity is about expanding or shrinking computing power within a server’s limitations.
Occasionally, the restaurant has so much traffic that it adds tables and chairs on its patio, a place that is otherwise kept open for ventilation. The concept of cloud scalability is similar. The restaurant seats more people without leasing more space; it just reconfigures the patio to seat the additional guests.
That is how cloud elasticity is different from cloud scalability, in a nutshell.
You’ve probably noticed this by now that cloud elasticity and cloud scalability go hand-in-hand. Before a system can be elastic, it needs to be scalable.
What Is The Purpose Of Cloud Elasticity?
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?
Rapid Elasticity Used Cases And Examples
Three excellent examples of cloud elasticity at work include e-commerce, insurance, and streaming services.
Use case one: Insurance
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.
Use case two: E-commerce
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 potential buyers’ interest you can expect to peak.
New shoppers would register new accounts. Existing customers would also revisit old wishlists, and 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.
Use case three: Streaming services
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 will allocate you unlimited resources at any time. That means you would be able to handle both sudden and expected workload spikes at any time.
Benefits And Limitations Of Cloud Elasticity
There are several powerful benefits of elasticity in the cloud.
Elasticity balances performance with cost-effectiveness
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.
It helps provide smooth services
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, you never know when your company may be able to capitalize on a sudden wave of interest. So, what do you do when you need to be ready for that opportunity but do not want to spend your cloud budget on speculation?
With cloud cost optimization, you can balance system performance with cost objectives. Cloud cost optimization focuses on dynamically balancing price-performance, rather than simply reducing cloud costs regardless of the impact on system performance (hence user experience).
How To Take Advantage Of Cloud Cost Optimization With CloudZero
CloudZero empowers you in several key ways:
- The engineering team can drill down and examine the specific costs and services that drive their product, features, and more. Using metrics such as cost per deployment, cost per service, or cost per environment, they can see how their technical activities impact your bottom line. This can help them build more cost-effective solutions from the start
- You can view costs per unit; Cost per Project, per Team, per Feature, per Product, per Service, etc. This can help you pinpoint where to reduce usage to cut costs or where to invest more to maximize returns
- You can view Cost per Customer. By knowing who your most profitable and most expensive customers are, you can adjust your contract terms with them accordingly to protect your margins.
- CloudZero also enables you to allocate 100% of your cloud spend in minutes to a few hours — no matter how complex your environment is.
- You’ll also get timely and context-rich cost anomaly alerts to help you fix issues that could lead to overspending.
But, don’t just take our word for it. Demandbase used CloudZero to reduce their annual cloud spend by 36%, justifying $175 million in financing. MalwareBytes now saves 6-10 hours weekly managing their costs with CloudZero. You can, too.