Building an Efficient Cloud System with Auto Scaling Software

As more and more operations and workloads shift to the cloud, it is vital to ensure resources are correctly optimized to be available when required and cost-efficiently.  

Auto scaling software helps in the dynamic scaling of resources based on the capacity requirement. This helps save costs that companies incur when they employ more resources than required while ensuring that the business is not impacted due to the non-availability of resources.

Cloud resource management just became easier with auto scaling software

Cloud environments employ servers to run applications and store data. The organization configures the number of these servers based on the expected load. But the amount of traffic or demand requirement is not static. It varies depending on how many are using applications and the number of applications running, among many other factors. For example, a machine learning application might need more computing power than configured initially and would not run properly without the correct number of resources. At other times, there might be very few applications running, in which case, resources remain idle. 

Managing cloud resources for optimum utilization is a challenge that companies grapple with today. Cloud infrastructure monitoring can help by indicating resource usage and availability at any point in time. But that is not enough. The resource pool still needs to be manually adjusted, which is impractical. This is where auto scaling software can help.

Load balancing software is a similar software that ensures systems are not impacted by high traffic by distributing it across all available resources. Auto scaling solutions, combined with load-balancing software, can provide even more efficient management of resources.

How do auto scaling tools help with cost management and improved performance of cloud systems?  

Optimization holds the key to cloud cost management as cloud costs increase (or cloudflation, as it has been referred to of late). Auto-scaling helps companies smartly balance the availability of cloud systems and costs by automating resource management. Let’s look at the key areas it has an impact on.

Cost management: The use of cloud computing resources is not linear. 

Consider an e-commerce platform. Traffic to the site may be high for a few hours daily, signifying the window during which people have time to shop. Traffic might go off the charts during Black Friday and other sale days, while it might be very low towards the last few days of the month. Having many servers online in anticipation of traffic spikes is very cost-inefficient. Adding resources manually when traffic increases is also not an option because the latency in adding the resource can be detrimental. 

Auto scaling software has a pool of servers at its discretion, ready to be deployed when the cloud environment demands more resources. This helps ensure that only the required resources are employed, thus reducing the cost which the over-deployment of resources would have caused.

Resource utilization: “40% of cloud-based instances are at least one size too big, with workloads running at 5% to 10% utilization,” according to John Purcell, chief product officer, DoiT International Ltd.  Auto scaling can help improve resource utilization and bring down cloud costs that stem from over provisioning. 

Performance: A spike in traffic or usage can bring down the cloud infrastructure. Auto-scaling software monitors the cloud environment and anticipates spikes in advance through pre-configured rules or AI-based analysis. When it sees a rising demand that existing resources cannot accommodate, it springs into action, adding parallel resources to support the load. This seamless scaling ensures that the system does not crash due to overburdening.

According to a review on the G2 website.

“Auto Scaling automatically scales a resource based on demands and ensures that your application has the resources to function smoothly without manual intervention. This can help reduce costs by only allocating the needed resources."

Another review says,

"One of the key features that has been particularly useful for us is its ability to provision virtual machines and containers quickly. This has helped us reduce our time to market for new products and services, which is crucial in the fast-paced fintech industry.”

But reviewers also caution that there is a learning curve in understanding the interface and functionalities. And the costs can increase if overused.

Different approaches to auto scaling

There are different approaches to auto scaling based on how resources are increased and
decreased. 

Vertical auto scaling is the approach where there is an increase in server or instances capacity when there is a heavier load. The servers are shrunk when demand is less. But this may not work for larger organizations as the capacity of servers cannot be increased beyond an extent.

In such cases, horizontal scaling is more feasible.

Horizontal auto scaling involves adding more nodes or machines to the existing environment when needed and removing them when lesser capacity is sufficient.

When is auto scaling triggered?

Companies must decide when auto scaling must be triggered depending on the business requirements. According to the need, the following approaches are undertaken:

Reactive auto scaling: In this approach, the system constantly monitors resources. When it sees demand exceeding the available resources, additional capacity is added to the existing instance pool. The disadvantage of this approach is that there might be some latency between resource demand and capacity expansion, leading to the risk of crashes.

Scheduled auto scaling: Companies can use this to schedule scaling options based on expected traffic or demand. But the problem with this approach is that unexpected spikes can upset all the flow. 

Predictive auto scaling: Predictive auto scaling involves analyzing usage and demand patterns to automatically upscale or downscale the resources in anticipation of demand variations.

The good and the bad  

Auto scaling helps companies get their money’s worth from their cloud deployments. It ensures lower costs, higher performance, and increased availability of cloud systems. However, companies must use auto-scaling with caution. Overdoing auto-scaling can result in cost overruns as dynamically added resources(as in the case of auto scaling) come at higher costs.

Edited by Shanti S Nair