AWS autoscaling

Before AWS autoscaling came along, bolstering conventional data centers with more resources to run mobile and web apps was once an absurd, fruitless prospect. This approach enables your data center to take on peak loads. But in the end, you spend money on unneeded resources. Alternatively, you could set your data center to handle typical day-to-day utilization. However, you’re running the risk of dissatisfying your end-users due to usage spikes slowing down your apps.

That was then. Today, cloud resources can be scaled up or down automatically to meet the shifting load requirements of your applications. Such solutions, like AWS Autoscaling, revolutionize cloud resource management and allocation. AWS Autoscaling automatically right-sizes your resources and distributes them well across your data center based on traffic.

Your apps perform at an optimum level even during a sudden surge in traffic. This results in  significantly lower costs and zero resource wastage when usage slows down. You only spend money on resources you actually consume.  Furthermore, you are able to both increase and eliminate instances based on your workload.

Outlining how to properly structure your app

People trying to execute auto scaling are faced with two main issues:

  • Determining the correct launch configuration for either the size of the group of instances, and;
  • Deciding the size and type of the EC2 instance. 

The correct parameters rely on two things:

  • How the app functions, and;
  • How the app utilizes the resources when it operates.

Surprisingly, many enterprises don’t have knowledge of the quantity of resources their infrastructure requires and consumes. 

This is due to the fact that apps frequently make it too challenging to comprehend which infrastructure resources are being consumed. This prevents enterprises from making well-informed adjustments of sizes, scaling parameters, and node types are needed within an auto scaling group.

This is a complicated process as you can run out of resources if your app levels are programmed too low. In addition, costs will increase and the app will constantly be running if the minimal setting is too high. Basically, if auto scaling is not done correctly, there is almost no point to do it at all.

If it can’t be done manually, then how is it done?

The answer is deep reinforcement learning.  This is a branch of machine learning powered by neural networks. The latter refers to digital systems that mirror the human brain’s instinct for pattern recognition.

DRL converts observed patterns and learned responses into ever more refined algorithmic behavior. Furthermore, this technology allows us to examine loads in auto scaling groups and in nodes. This information is applied for optimization of the singular load type and size. Also, the data helps coordinate the whole scale group with the application needs. 

This is done by harnessing both ML and AI. The ML technology works by the engine,deciding the right size and instance type. OpsaniAI uses deep reinforcement learning to optimize those settings that are too complex for humans to touch. These include middleware configuration variables like:

  • JVM GC type and pool sizes
  • Kernel parameters (page sizes and jumbo packet sizes)
  • Application parameters (thread pools, cache timeouts and write delays)

The Result

Following this process, the engine constantly analyzes cloud catalogs. This helps the engine remain updated on the most current instance offerings available from every cloud provider.

Lastly, the engine performs a permutation analysis in order to determine the most excellent match between inventory and requirements. This process helps pinpoint what resources need adjusting to improve both cost and performance.

When the process of ML correctly optimizes the EC2 instance type and size, you need to set the entire group parameters to scale based on the app’s requirements. The engine will then refer to metrics to figure out the maximum and minimum group size. After that, it will make recommendations based on whether the app needs to be scaled up or down.

ML is essential when using AWS Autoscaling because you are able to get optimal performance and cost by right-sizing your cloud resources. This is crucial for your app to handle traffic influx while maintaining performance at the optimal level and keeping costs low.

For more information on achieving AWS Cloud Cost Optimization, read our blog.