How to prevent ECS tasks from being terminated because too much memory is used.

Based on the information you have provided, there could be several factors that might be contributing to the difference in behavior between your ECS and EKS setups. Let's analyze the situation and suggest some potential solutions:

Memory Allocation:

In ECS, Container 1 has a memory limit of 10240 MB (10 GB). In EKS, Container 1 has a memory limit of 10 Gi (approximately 10.74 GB).

The EKS setup actually allows for slightly more memory usage. This small difference could be significant if your application is operating near the limit.

Reserved Memory vs Requests:

In ECS, you're using reserved memory (5120 MB for Container 1). In EKS, you're using requests (4 Gi, or about 4.3 GB for Container 1).

The reserved memory in ECS is higher, which should theoretically be beneficial. However, ECS and EKS handle these differently:

In EKS, the "requests" amount is guaranteed to the container. In ECS, the reserved memory is more of a soft limit used for initial placement decisions.

Instance Resources:

ECS: m6a.4xlarge (16 CPUs, 64 GB RAM) EKS: t3a.2xlarge (8 CPUs, 32 GB RAM)

Your ECS instance has more resources, but it's also running 3 tasks, which could lead to resource contention.

ECS vs EKS Resource Management: ECS and EKS have different mechanisms for managing resources, which could lead to different behaviors under memory pressure.

Recommendations:

Increase Memory Limit in ECS: Try increasing the memory limit for Container 1 in ECS to match or exceed the EKS setting:

MemoryLimit: 11264 # This is equivalent to 11 Gi Adjust Reserved Memory: Increase the reserved memory to be closer to the actual usage:

reserved memory: 8192 # 8 GB Monitor Actual Memory Usage: Use CloudWatch to monitor the actual memory usage of your containers. This will help you understand if the container is actually approaching its limit before being terminated.

Check ECS Task Placement: Ensure that your ECS task placement strategy is optimized. You might be facing issues if multiple memory-intensive tasks are placed on the same EC2 instance.

Review ECS Agent Logs: Check the ECS agent logs for any warnings or errors related to memory management.

Consider Using Fargate: If possible, consider using AWS Fargate for ECS. Fargate provides a more Kubernetes-like experience in terms of resource allocation and management.

Application-Level Monitoring: Implement application-level memory monitoring and management. This could help you identify and address memory leaks or unexpected spikes in usage.

Gradual Scaling: Instead of running 3 tasks immediately, try scaling up gradually to see at which point the memory issues occur.

Use ECS Service Auto Scaling: Implement ECS Service Auto Scaling based on memory utilization. This can help manage the load more dynamically.

Review Application Code: Ensure that your application is handling memory efficiently and releasing resources properly. The difference in environment might be exposing an underlying issue in the application.

By implementing these suggestions and closely monitoring the results, you should be able to identify the root cause of the memory termination in ECS and bring its behavior more in line with what you're experiencing in EKS.

The issue you're experiencing with ECS tasks being terminated due to memory issues, while the same containers run smoothly on EKS, likely stems from differences in how ECS and EKS handle resource allocation and limits. Here are some key points to consider:

1. Memory Reservation vs. Limits: In ECS, you've specified both reserved memory and memory limits. The reserved memory is what ECS guarantees for the container, while the limit is the maximum it can use. In your ECS configuration, you're reserving half of the limit for each container. This might be too restrictive, especially for Container 1.

2. Soft vs. Hard Limits: EKS (Kubernetes) typically uses soft limits, allowing containers to exceed their resource requests as long as the node has available resources. ECS, on the other hand, enforces hard limits more strictly.

3. Memory Overhead: ECS might be accounting for some additional memory overhead that EKS doesn't, which could lead to earlier termination of tasks.

To address this issue in ECS:

1. Increase the overall task memory limit: Consider raising it closer to the instance's total memory (64 GB), leaving some buffer for the ECS agent and other system processes.

2. Adjust individual container memory settings:

   • Increase the memory limit for Container 1 to match its EKS counterpart (10Gi ≈ 10240 MB).
   • Set the reserved memory to a lower value, perhaps 30-50% of the limit, to allow more flexibility.

3. Monitor and adjust: Use CloudWatch metrics to monitor the actual memory usage of your containers and adjust the limits accordingly.

4. Consider using task-level autoscaling: This can help manage resource allocation more dynamically.

5. Use ECS task scale-in protection: If your tasks are processing critical work that shouldn't be interrupted, you can enable task scale-in protection to prevent ECS from terminating them during scale-in events.

Remember, ECS and EKS have different resource management models, so a direct translation of settings may not always work. You may need to experiment with different configurations to find the optimal setup for your ECS tasks.
Sources
Protect your Amazon ECS tasks from being terminated by scale-in events - Amazon Elastic Container Service
Task and container security - Amazon Elastic Container Service