What Is Elasticity? Embracing Elasticity for a Scalable Future in Cloud Technology

Who said that cloud resources have to be set in stone? The cloud demands of your business are unlikely to be the same tomorrow as they are today.

That means constantly obsessing over cloud demands and the resources you have available to you, right? Wrong! Enter cloud elasticity, which, despite how it sounds, has nothing to do with the waist of your sweatpants.

Instead, elasticity in this context refers to the ability of a cloud solution to expand or decrease things like storage, processing, and memory depending on the demands of the application.

Essentially, elastic computing allows companies to scale their operations up or down without having to worry about resources — plus it often has the added benefit of allowing businesses to only pay for the resources they use rather than what they think they might need.

Curious about how elasticity can help you? Here’s everything you need to know about it.

Cloud Elasticity can work manually or automatically. In the case of manual elasticity, resources and demands can be monitored and increased or decreased in a matter of minutes.

Automatic cloud elasticity is a little more versatile in that, instead of manually monitoring resources and demands, they’re monitored automatically, and changes are executed based on meeting those needs. Here’s a deeper look.

Automatic Resource Scaling

Automatic resource scaling is essentially what it sounds like. It refers to the process in which a cloud platform can dynamically change the resources that it allocates based on the demand of an application.

These resources can include processing, memory, or storage, and the way in which it allocates resources ensures that an application remains running smoothly and reliably under varying workloads.

Horizontal vs. Vertical Scaling

There are a few different ways in which a cloud environment can scale based on demands. These ways are referred to as horizontal and vertical scaling, and they have different implications for how a platform can handle a workload.

• Horizontal Scaling (Scaling Out/In): Horizontal scaling essentially adds or removes instances of resources depending on demand. This can include adding or removing servers or virtual machines, with the load distributed across those resources and expanded or contracted as needed. When scaling out, more machines will be added to handle more traffic, while when scaling in, they’ll be removed to decrease costs.
• Vertical Scaling (Scaling Up/Down): Vertical scaling involves increasing the resources of an instance already in use to handle higher demand. It can be used when a system might need a short-term boost in performance. While it is cheaper, it can only expand to the limits of the actual hardware.

Systems that need quicker, smaller bursts of added resources may benefit from the lower cost of vertical scaling. However, applications that might see more dynamic increases in demand are likely to be better served by horizontal scaling.

Elasticity vs. Scalability

Elasticity and scalability are very closely related, but there are a few differences. Scalability refers more to the ability to increase or decrease capacity based on long-term workloads and often involves manual intervention, or at least a defined automation that was initially set up manually.

Elasticity, on the other hand, is based on on-demand resource changes, enabling a system to react to unexpected spikes of demand without the need for manual intervention.

Elastic Resource Management

Elastic resource management is the mechanism by which cloud service providers allocate resources dynamically to meet demands and usage patterns.

Usually, elastic resource management is conducted through APIs that monitor the demands of an application and take action to increase or decrease resources based on that. Sometimes management consoles are used too, allowing users to manually monitor demand and allocate resources quickly and easily.

Not all cloud elasticity is the same, and depending on its implementation, there can be different outcomes for your business and the resources it both needs and uses.

Here’s a look at the main kinds:

• Reactive Elasticity: Resources are automatically scaled based on changes in demand that have already occurred.
• Proactive Elasticity: Resources are scaled in advance of changes in demand based on predictions or scheduled events.
• Manual Elasticity: Resource changes are overseen by a human operator who monitors changes in demand and applies resource increases or decreases as they see fit.

Anticipating changes in demand can be tricky, and there’s no way to tell the future. Reactive elasticity helps ensure that you’re not paying for resources you don’t need, but the flip side of that coin is that there can be a slight delay between increased demand and increased resources.

Proactive elasticity can cost more because it’s based on models that predict changes that may not happen as predicted or at all. Manual elasticity takes some of the advantages of elasticity out of the picture by requiring human intervention.

There are plenty of features to elasticity in cloud computing. Here is a look at some of the most important ones.

There are real-world benefits to cloud elasticity, meaning that if your business isn’t already using elastic computing, it might be worth considering.

Here’s a look at some of the major benefits:

• Cost Efficiency: Unneeded resources aren’t being paid for.
• Improved Performance and Availability: Downtime is limited, and applications remain running smoothly without overwhelming a server.
• Operational Agility: Decisions can be made quickly, and changes can be implemented without needing to first take cloud resources into consideration.
• Reduced Administrative Overhead: Managers aren’t needed to oversee the performance of cloud applications and dictate how and when resources should be allocated.

In other words, cloud elasticity ensures that an application is always running at peak efficiency without costing you more than it needs to.

There are some challenges associated with cloud elasticity, and it’s not necessarily the be-all and end-all of high-performance cloud applications. Here are some of the challenges associated with it.

Cost Management

Only paying for the resources you need can be a double-edged sword.

While you might pay less by cutting out resources you don’t need, you could also end up with a higher bill if unexpected or unmanaged scaling occurs, leading to a rapid increase in resource allocation.

The best way to mitigate this is to set effective thresholds and limits on resource usage.

Complexity in Configuration

Elasticity in cloud computing can get complicated quickly. Often it requires attempting to predict the needs of an application and set rules for how resources are allocated based on those predictions.

Not only that, but it’s possible to misconfigure a platform, which can result in inefficient resource allocation.

Latency and Delays

While cloud elasticity usually results in less downtime, sudden spikes in demand can lead to performance issues, as elastic systems take more time to respond to those spikes.

The best way to get around this is to build a buffer of resources into a configuration, ensuring there’s always a little extra that can be utilized.

Security Concerns

As with any kind of rapid scaling, new security challenges can arise with cloud elasticity. Notably, you’ll have to ensure that connections across distributed resources remain secure. Not only that, but it can be tricky to ensure consistent access controls across scaling resources.

You can mitigate security concerns by using a Virtual Private Cloud or Virtual Private Network.

There are, thankfully, ways to mitigate security concerns, though. For example, you can employ network isolation, a technology that separates scaling cloud resources from the broader internet, through techniques like a Virtual Private Cloud (VPC) or Network (VPN).

Other security techniques can help too, such as ensuring all data is encrypted, both in transit and at rest. Lastly, it can be helpful to perform regular vulnerability assessments to identify any security risks and implement any necessary patches.

You might be wondering exactly where and how elasticity is used today — and there are actually plenty of examples. Here’s a look at some of the most common.

eCommerce Websites

You probably shop online all the time, but do you ever think about what’s going on behind the curtain? Online retailers like Amazon and Walmart handle an absurd amount of traffic without a hitch, and it’s all thanks to the elasticity of the cloud.

eCommerce websites make use of elasticity to ensure that large shopping events don’t cause any downtime on a site, which could result in significant lost income.

For example, an eCommerce website could use AWS Auto Scaling to ensure that the platform can handle any amount of traffic that might come from sales on Black Friday without any interruptions to service.

The last thing you want is to lose out on those sweet holiday sales, so a bit of autoscaling can be a real lifesaver.

SaaS (Software as a Service) Applications

SaaS applications based in the cloud need to scale as subscriptions grow, and demand can spike at certain times, like during the premier of a popular new show or the launch of a new game. A SaaS provider could make use of the Google Cloud Autoscaler to ensure that as new customers come to the platform, it remains reliable and online.

Streaming and Media Services

I like Netflix and Hulu just as much as the next person. Part of the appeal of these services is that you can get access to buffer-free TV shows and movies from anywhere in the world.

Streaming platforms and other media services can use elasticity to manage video streaming demand in different regions.

For example, Netflix uses Amazon EC2 Auto Scaling to ensure that content is reliably delivered no matter where it’s being streamed.

Now everyone can binge the latest season of Bridgerton being forced to try and figure out what splotch of pixels make up Luke Thompson’s face.

Scientific Research and Big Data Analytics

Complex datasets, like those used in healthcare for genome sequencing workloads, require high levels of computing power at certain times, such as when specific research is being conducted.

This and other use cases of big data analytics can make use of elasticity to ensure that higher levels of computing are always available when needed but not always being paid for when they are not in use.

Things aren’t necessarily as simple as just ticking a box marked “elasticity.” It’s worth thinking about how you’ll implement cloud elasticity before you do so. Here are some considerations to keep in mind.

Right-Sizing Resources

Elasticity is largely used to avoid downtime. To ensure you do it effectively, it’s worth selecting the right resource limits to avoid over- or under-provisioning when resources are scaling.

Essentially, you’ll want to strictly define thresholds that help prevent overscaling or underscaling, which can lead to performance bottlenecks on one end or high costs on the other. This will minimize downtime while maximizing efficiency.

Optimizing Auto-Scaling Policies

This will reduce the cost of the server’s performance. It’s a good idea to create auto-scaling policies that can clearly define performance metrics when it comes to thresholds and triggers used in the auto-scaling process.

For example, make sure that you’re leveraging historical data to analyze trends and better predict resource thresholds.

Do this regularly to ensure that as a business grows or as demands change, the thresholds remain valid for your needs.

Additionally, clearly define specific performance metrics around things like CPU utilization, memory, and request rate.

Monitoring and Alerts

Auto-scaling may minimize the need for manual monitoring, but it’s still a good idea to set up alerts around resource usage and have some monitoring tools in place that can help you optimize elastic scaling.

The good news is that all of the major cloud providers offer tools to help keep an eye on resource usage — like AWS CloudWatch and Azure Monitor.

Cost Optimization and Budget Control

Along with avoiding downtime, elasticity is built to optimize costs, but it can be a double-edged sword if your business experiences rapid growth in a short amount of time or unexpected resource needs.

To avoid being slapped with a high bill after such an event, set scaling limits that kick in at certain amounts.

You can also consider combining elastic scaling with spot instances that minimize the impact additional resources might have on infrastructure costs.

If you have a plan in place, you can stay comfortably within your budget while still enjoying the benefits of elasticity.

Cloud elasticity can be hugely beneficial for businesses that experience rapidly growing or rapidly shrinking resource needs over time. It can help take the guesswork out of allocating resources to cloud services.

While poorly implemented, it can result in a big bill at the end of the month. When properly used, it should both minimize downtime and reduce costs.