Ruminating on AWS Fargate Autoscaling

Amazon Fargate is a serverless compute engine that allows you to run containers without having to provision or manage servers. One of its powerful features is automatic scaling, which enables your application to adjust its capacity based on demand. This ensures optimal performance and cost efficiency.

Target Tracking and Step Scaling: A Dynamic Duo

Fargate automatic scaling primarily relies on two strategies: target tracking and step scaling. Let's delve into how these mechanisms work together to maintain desired application performance.

Target Tracking:

• Defining a Metric: You specify a metric that represents your application's performance or resource utilization. This could be CPU utilization, memory usage, or a custom metric.
• Setting a Target Value: You establish the desired target value for the metric. For instance, you might set a target CPU utilization of 70%.
• Continuous Monitoring: Fargate continuously monitors the actual metric value and compares it to the target.
• Scaling Actions: If the actual value deviates significantly from the target, Fargate triggers scaling actions to adjust the number of tasks.

Step Scaling: 

• Step Adjustments: Step scaling involves increasing or decreasing the number of tasks by a predefined step size.
• Scaling Policies: You define scaling policies that specify:
• Step size: The number of tasks to add or remove in each scaling action.
• Cooldown period:The minimum time between scaling actions to prevent excessive fluctuations.
• Thresholds:The deviation from the target metric that triggers scaling.

How They Work Together:

• Target Tracking: Fargate monitors the specified metric and determines if it's deviating from the target.
• Step Scaling: If the deviation exceeds the defined thresholds, Fargate applies the corresponding scaling policy.
• Adjustment: The number of tasks is increased or decreased by the step size.
• Evaluation: Fargate continues to monitor the metric and adjusts the number of tasks as needed to maintain the target value.

Imagine a web application/API that experiences sudden traffic spikes during peak hours. By using target tracking and step scaling, you can configure Fargate to automatically increase the number of tasks when demand surges, ensuring optimal performance for your users.

Ruminating on dedicated instance vs. dedicated host

 Many folks get confused between the AWS terminology of 'Dedicated Instance' vs 'Dedicated Host'.

A simple way to understand the difference is to remember that a "host" is a physical machine that can host many virtual machine instances. 

Hence a "dedicated host" is a physical machine that is dedicated to your organization. On this physical machine (host), you can install many VMs/containers. So you control what VMs (instances) are going to run on that host. 

So what is a dedicated instance then? A dedicated instance is a virtual machine that runs on hardware that is not shared with other accounts. Dedicated instances are physically isolated at the host hardware level from instances that belong to other AWS accounts. Hence you can only be certain that the underlying hardware that is hosting your VM is not shared with someone else. But you have no fine-grained control over which VM would be launched on which host, etc. 

Cloud Native Banking Platform - Temenos

Temenos is the world's number one core banking platform. It is built entirely on the AWS cloud and uses all managed services. 

I was stuck with the simplicity of the overall architecture on AWS and how it enabled elastic scalability to scale-out for peak loads and also scale-back for reducing operational costs. 

Another interesting aspect was the simple implementation of the CQRS pattern to offload queries (read-only API requests) to DynamoDB. The pipeline was built using Kinesis and Lambda. 

An excellent short video on the AWS architecture of the Temenos platform is here: https://youtu.be/mtZvA7ARepM