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November 20, 2024
May 24, 2024
November 20, 2024
May 24, 2024
Optimize compute, storage and data
Choose copilot or autopilot execution
Continuously improve with reinforcement learning
While each organization’s approach is different let’s consider a journey through four stages of maturity from high-touch, manual processes to sophisticated, intelligent systems that enhance performance and cost-efficiency with minimal human intervention as shown below:
The four stages of the journey are:
The following table provides a detailed comparison of how each ECS optimization control — from rightsizing tasks and instances to geographical placement and autoscaling — is implemented across four key methods: Reactive Manual Optimization, Proactive Manual Optimization, Automation via rules, and Autonomous AI-driven Optimization.
Let’s compare an autonomous system to traditional automated systems. Autonomous systems can undertake the following steps on its own, guided by the goals given by the user:
Automation comes with challenges because it involves a lot of manual configuration. In the table below we contrast how key activities involved in optimizing ECS for cost, performance and availability differ between an automated and autonomous approach. For example, you have to manually set the thresholds and come up with the metrics that you need to monitor. However, an autonomous system continuously studies the behavior of the application and it adapts accordingly.
Below are the steps that autonomous systems use to optimize ECS environments. Imagine you have a fleet of multiple services. In the autonomous model, your topology is discovered and observed well with effective telemetry that’s being watched 24/7. That telemetry is used to understand your app behavior for each new release. That understanding of the application and how it responds is used to safely reconfigure the service. The effectiveness of that optimization is then checked and validated; and will be stopped if needed. You then learn from the change made and its effects (reinforcement learning) and then you keep repeating the loop for the service.
So performing these actions in the right order is the key to ensuring that we can run these services as optimized as possible from both the perspective of cost and performance.
Moving to an autonomous system allows you to shift away from managing autoscaling and service templates to focusing on goals.
If you're managing at the template level for each service, you end up with hundreds of templates that need to be modified every release. These include the individual services, service autoscalers and cluster autoscalers.
In an ideal scenario, you should avoid transferring your production runtime duties to the left. Instead, aim to elevate them to a level where an independent system can handle and optimize your runtime settings. While still requiring templates for software versions, dependencies, and other factors, the most effective approach is to shift upwards to effectively manage your runtime parameters.
Below is an ECS Optimization Maturity Model that you can use to assess where your organization sits today and to set goals for upgrading your ECS optimization capabilities, taking a wider view beyond individual engineering and purchasing tactics. This table charts a clear path from basic, manual setups to advanced, AI-enhanced operations. It covers essential areas like goal-setting, engineering adjustments, smart purchasing, and more, showing you exactly how to progress from simple to state-of-the-art in managing your ECS environments effectively.
* See separate table for a more detailed view on how individual levers such as autoscaling would operate at different stages of maturity.
May 24, 2024
November 20, 2024
While each organization’s approach is different let’s consider a journey through four stages of maturity from high-touch, manual processes to sophisticated, intelligent systems that enhance performance and cost-efficiency with minimal human intervention as shown below:
The four stages of the journey are:
The following table provides a detailed comparison of how each ECS optimization control — from rightsizing tasks and instances to geographical placement and autoscaling — is implemented across four key methods: Reactive Manual Optimization, Proactive Manual Optimization, Automation via rules, and Autonomous AI-driven Optimization.
Let’s compare an autonomous system to traditional automated systems. Autonomous systems can undertake the following steps on its own, guided by the goals given by the user:
Automation comes with challenges because it involves a lot of manual configuration. In the table below we contrast how key activities involved in optimizing ECS for cost, performance and availability differ between an automated and autonomous approach. For example, you have to manually set the thresholds and come up with the metrics that you need to monitor. However, an autonomous system continuously studies the behavior of the application and it adapts accordingly.
Below are the steps that autonomous systems use to optimize ECS environments. Imagine you have a fleet of multiple services. In the autonomous model, your topology is discovered and observed well with effective telemetry that’s being watched 24/7. That telemetry is used to understand your app behavior for each new release. That understanding of the application and how it responds is used to safely reconfigure the service. The effectiveness of that optimization is then checked and validated; and will be stopped if needed. You then learn from the change made and its effects (reinforcement learning) and then you keep repeating the loop for the service.
So performing these actions in the right order is the key to ensuring that we can run these services as optimized as possible from both the perspective of cost and performance.
Moving to an autonomous system allows you to shift away from managing autoscaling and service templates to focusing on goals.
If you're managing at the template level for each service, you end up with hundreds of templates that need to be modified every release. These include the individual services, service autoscalers and cluster autoscalers.
In an ideal scenario, you should avoid transferring your production runtime duties to the left. Instead, aim to elevate them to a level where an independent system can handle and optimize your runtime settings. While still requiring templates for software versions, dependencies, and other factors, the most effective approach is to shift upwards to effectively manage your runtime parameters.
Below is an ECS Optimization Maturity Model that you can use to assess where your organization sits today and to set goals for upgrading your ECS optimization capabilities, taking a wider view beyond individual engineering and purchasing tactics. This table charts a clear path from basic, manual setups to advanced, AI-enhanced operations. It covers essential areas like goal-setting, engineering adjustments, smart purchasing, and more, showing you exactly how to progress from simple to state-of-the-art in managing your ECS environments effectively.
* See separate table for a more detailed view on how individual levers such as autoscaling would operate at different stages of maturity.
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