Cost-optimization techniques with cloud autoscaling

Cost optimization in cloud computing

Cost optimization in cloud computing refers to reducing overall cloud spending by identifying mismanaged resources, eliminating waste, reserving capacity for higher discounts, and selecting the right size and type of cloud services to fit the needs. This practice is essential in managing operational costs and ensuring that the resources are efficiently used to maximize value and performance.

Autoscaling is a key technique in cloud computing that dynamically adjusts processing resources based on the workload requirements at any given time. Organizations can balance performance and cost efficiency by integrating cost optimization strategies with autoscaling.

Cost optimization autoscaling strategies and techniques

The following are some cost optimization autoscaling strategies and techniques:

• Demand forecasting: Use predictive analytics to project future demand. Consequently, the system can scale resources appropriately, avoiding charges for unused capacity.

• Planned scaling: Scheduling capacity expansions or decreases may result in cost savings, especially if workload patterns are predictable. It’s beneficial for scenarios like daily traffic peaks or seasonal demand changes.

• Dynamic sizing: Instant modifications of computing resources are based on current demand, with continual monitoring and adjustments.

• Vertical scaling: Sometimes known as “scaling up,” this involves increasing the CPU and RAM of an existing machine, which can be more cost-effective than horizontal scaling.

• Horizontal scaling: It is also known as “scaling out”. This involves adding more machines to the existing pool. It can sustain significant workload increases but is often more expensive.

• Rightsizing: Adjusting instance types and sizes to the application’s needs, utilizing the variety of instance types cloud service providers offer.

• Spot instances: These unused resources can be purchased at a discount. They are cost-effective for expandable resources, even though the provider can terminate them.

• Use of containers: Containerization can reduce the required infrastructure and improve resource utilization, enabling sophisticated autoscaling strategies with systems like Kubernetes.

Example: E-commerce application scaling

Let us say we manage an online store with a spike in holiday visitors. Scheduled scaling allows us to boost resources shortly before the season begins and decrease them once it is through. Dynamic scaling would assist in controlling unanticipated increases over the Christmas season. Spot instances might be utilized for background processing activities that are less crucial. Here is the higher-level architecture diagram of the e-commerce application using AWS services:

The above diagram represents that an application is hosted on an EC2 instance, and the EC2 instances are handled by the Auto Scaling group and load-balancer. Whenever there is peak time, autoscaling will increase the resources and automatically decrease the resources when the load becomes normal.

Advantages and disadvantages of using cost-optimization

Here are some of the advantages and disadvantages of using cost-optimization with cloud autoscaling: