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Use AWS's ECS Retail Demo Application to Test Sedai's Autonomous Optimization

Published on
Last updated on

March 28, 2024

Max 3 min
Use AWS's ECS Retail Demo Application to Test Sedai's Autonomous Optimization

Introduction & Setting up the Retail Demo App

Sedai, an AI-powered cloud optimization software, is a powerful tool any developer can easily use. Assuming one has an AWS account (if not, see tutorial here),

For this example, we are optimizing a sample e-commerce store that will be deployed with and without Sedai for comparison using ECS.

The retail demo store can simply be deployed by following the steps in the of the GitHub repository linked above. 

Step 1: Setting up Sedai

The first step is to go to the Sedai website at and begin by clicking the Start Free button as displayed below.

Once you click the button, you are prompted to a simple account creation page to input your basic information for a one-month free trial. After signing up, you will begin an introduction to the autonomous tools used. Here, you can easily specify your goals and resource type for your application (ECS, EKS, Lambda, and Kubernetes compatible).

The next step involves connecting to your cloud account through the AWS Identity and Access Management pins on your account. If you are slightly unfamiliar with this, simply click the “Launch CloudFormation” button to create a stack in your AWS account, which will prompt you to create a stack – press create.

 After that, simply go to your stack and copy and paste the ARN in the outputs tab as shown below:

Congratulations – once you complete this step, you are ready to launch Sedai! Once you launch the UI, you will be prompted to a homepage with a directory bar on the left to specify the different tools and modes you can access, while the main page will simply display your activity on the application. Once you are ready to begin your optimization process, click on the Topology tab and specify the cloud name. 

Step 2: Generating Traffic 

To effectively simulate the software for a variety of different-sized e-commerce stores, we simulated four different loads of traffic/engagement for the e-commerce site: new startup (low), growth company (medium), established (high), and market leader (very high). This was done through the Locust framework, where each traffic bot engaged with the website’s product and payment functionality. The GitHub repository for the traffic simulation is linked here.

Step 3: Optimizing with Sedai

When you have selected your desired cloud, cluster, application, etc., you will be able to see the optimization/availability actions. Once you are ready to optimize your resource, simply click on the resource name and specify the nature of optimization as displayed below.

After specifying your optimizations, you should be all set! Now, deploy your application to see the changes. Below, we will focus on a side-by-side comparison of Sedai optimization for an Elastic Container Service (ECS) deployment of the retail demo store. 

Step 4: Reviewing Results 

Sedai typically has a two-week learning period deployment, after which results can be obtained. Given that this is a demo store, and for the sake of a fair comparison, we did not modify the base configurations of the store. Consequently, the minimum fargate CPU and Memory requirements prevented Sedai from optimizing the cost. Nonetheless, we did see significant results across all 4 traffic levels where latency and error rates dropped significantly. 

As seen above, prior to  optimization, the site showed the following statistics: a single GET request with an average response time of 171 ms, while three PUT requests to the "/carts/5" endpoint averaged 466 ms. However, after implementing Sedai, the site witnessed significant improvements. The GET request now performs at an average of 220 ms, while the PUT requests have been optimized to an average of 149 ms. These improvements are reflected in the response time statistics, where the site achieved a remarkable 200 ms as the 99th percentile response time for PUT requests. Sedai's optimization software has undoubtedly transformed the e-commerce site into a more efficient and seamless experience for users.

Medium Traffic

For medium traffic, results, again, display an improvement in statistics. Before undergoing optimization, the site's request statistics revealed a series of 9 GET requests, averaging at 129 ms, and 14 PUT requests to the "/carts/5" endpoint, with an average response time of 125 ms. However, the implementation of Sedai aided the site's performance, leading to advancements. Following the optimization, the GET requests now boast an impressive average response time of 118 ms, demonstrating Sedai's prowess in streamlining server-side processes. Meanwhile, the PUT requests experienced a improvement, achieving an average response time of 129 ms, thanks to the software's intelligent optimizations. These improvements are further emphasized in the response time statistics. The 50th percentile response time for GET requests significantly dropped to 96 ms, showcasing the enhanced efficiency and responsiveness of the site. 


For higher traffic sites receiving upwards of 100 orders per minute, Sedai begins to have more than a profound impact on the website’s performance. Prior to optimization, the site faced challenges with an average response time of 720 ms for GET requests and 925 ms for PUT requests to the "/carts/5" endpoint. However, Sedai's intervention significantly enhanced the site's performance. Following optimization, the GET requests now enjoy an average response time of 114 ms, while the PUT requests have notably improved to 155 ms. These enhancements are reflected in the response time statistics, with the 50th percentile response time for GET requests reduced to 120 ms, and the 99th percentile response time for PUT requests reaching 540 ms. These notable results exemplify Sedai's ability to expedite data retrieval and optimize complex operations. 

Finally, the site’s true capabilities can be best exemplified for high traffic sites as errors and response time statistics improved significantly. Initially, the site faced performance challenges with an average response time of 341 ms for GET requests and a concerning 1,999 ms for PUT requests to the "/carts/5" endpoint. However, after implementing Sedai, significant improvements were observed. The average response time for GET requests dropped to 452 ms, while PUT requests saw a notable reduction to 534 ms. These enhancements are evident in the response time statistics, with the 50th percentile response time for GET requests reaching 160 ms, and PUT requests achieving a 50th percentile response time of 190 ms. Furthermore, Sedai's optimization efforts led to a considerable decrease in failures. The occurrence of PUT request failures decreased from 680 instances to just 116.


User behavior can have a significant impact on the performance and reliability of cloud applications. Even small changes in user behavior can lead to decreased failures and improved response time. For example, a study by Google found that a 1% decrease in page load time can lead to a 2% increase in conversions. Similarly, a study by Amazon found that a 100ms decrease in response time can lead to a 1% increase in customer satisfaction. A study by Microsoft found that a 10% decrease in latency can lead to a 5% increase in productivity. Sedai, an advanced optimization software, has proven highly effective in improving e-commerce website performance. By reducing response times and minimizing failures, Sedai enhances user experience, boosts customer satisfaction, and increases revenue potential. With optimized server-side processes, GET and PUT requests become faster and more efficient, enabling swift access to resources. The software's importance lies in its ability to provide a seamless and reliable online experience, enhancing customer trust and loyalty. Implementing Sedai is crucial for businesses aiming to gain a competitive edge and thrive in the fast-paced world of e-commerce.

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