Memory Capacity Analysis of AWS EC2 Instances: Discrepancies and Solutions Needed

Question

Hi everyone,  
Apologies if this isn't the right forum to post about my issue. Please feel free to direct me to a more appropriate one if needed.

Over the past few days, I’ve been analyzing the AWS EC2 nodes currently running across multiple Kubernetes clusters in different regions, with various instance types. Here are the memory capacity differences I’ve found:

| Instance Type | Largest Memory Capacity (Ki) | Difference in Memory Capacity (Ki) | % Difference with AWS Documentation |
|---------------|------------------------------|-------------------------------------|--------------------------------------|
| t3a.xlarge    | 16226752                     | 550508                             | 3.28%                               |
| t3a.2xlarge   | 32587216                     | 967248                             | 2.88%                               |
| c5a.xlarge    | 8022472                      | 366136                             | 4.36%                               |
| c6a.large     | 3900312                      | 293992                             | 7.01%                               |
| t3a.medium    | 3955608                      | 238704                             | 5.69%                               |
| t3a.small     | 1976632                      | 120552                             | 5.75%                               |
| t3a.large     | 8045460                      | 343148                             | 4.09%                               |
| c5a.large     | 3943324                      | 250980                             | 5.98%                               |

In summary, the differences range from 2% to 7%, which seems quite significant and inconsistent. I retrieved the memory capacity from the `node describe` command and cross-checked it with `/proc/meminfo` to confirm that `MemTotal` matches the memory capacity shown in the `node describe` output.

For additional context, we are using Karpenter with the Nitro hypervisor.

Does anyone have any insights on why these discrepancies occur, and what percentage difference should be considered acceptable when calculating general memory capacity? I need to establish a baseline for memory capacity calculations.

Thank you!

re:Post Agent · Answer

The discrepancies you're observing in memory capacity between what you're seeing in your Kubernetes clusters and the AWS documentation are not uncommon. There are several factors that can contribute to these differences:

1. Operating System Overhead: The memory reported by the operating system is typically less than the total hardware memory because some memory is reserved for the kernel, boot processes, and other system requirements.

2. Hypervisor Overhead: AWS uses virtualization technology, specifically the Nitro hypervisor for the instance types you mentioned. The hypervisor itself requires some memory to operate, which reduces the amount available to the guest operating system.

3. EC2 Instance Memory Management: AWS may reserve a small portion of memory for management purposes, such as monitoring and maintenance tasks.

4. Rounding and Reporting Differences: The way memory is reported can vary between tools and systems. Some may report in binary units (where 1 GB = 1,073,741,824 bytes) while others use decimal units (where 1 GB = 1,000,000,000 bytes).

5. Instance Generation and Specific Configuration: Different generations or configurations of the same instance type might have slight variations in available memory.

Regarding what percentage difference should be considered acceptable, there's no official AWS guideline. However, based on common practices and the data you've provided:

- Differences of 2-5% are generally considered normal and acceptable.
- Differences of 5-10% might warrant further investigation but are not necessarily problematic.
- Differences exceeding 10% would be unusual and should be investigated more thoroughly.

For establishing a baseline for memory capacity calculations, consider the following approach:

1. Use the actual observed memory from your instances rather than the advertised amounts.
2. Apply a conservative buffer (e.g., 5-10%) to account for variations and ensure you don't overcommit resources.
3. Regularly monitor and adjust your calculations based on real-world performance and utilization.

If you need more precise memory allocation for specific workloads, you might consider using dedicated instances or bare metal instances where available, as these can provide more predictable resource allocation. Additionally, reaching out to AWS support for clarification on specific instance types might provide more detailed insights into memory allocation for your use case.  
**Sources**  
[New – Amazon EC2 R7a Instances Powered By 4th Gen AMD EPYC Processors for Memory Optimized Workloads | AWS News Blog](https://aws.amazon.com/blogs/aws/new-amazon-ec2-r7a-instances-powered-by-4th-gen-amd-epyc-processors-for-memory-optimized-workloads/)  
[Specifications for Amazon EC2 memory optimized instances - Amazon EC2](https://docs.aws.amazon.com/ec2/latest/instancetypes/mo.html)

Instance Type	Largest Memory Capacity (Ki)	Difference in Memory Capacity (Ki)	% Difference with AWS Documentation
t3a.xlarge	16226752	550508	3.28%
t3a.2xlarge	32587216	967248	2.88%
c5a.xlarge	8022472	366136	4.36%
c6a.large	3900312	293992	7.01%
t3a.medium	3955608	238704	5.69%
t3a.small	1976632	120552	5.75%
t3a.large	8045460	343148	4.09%
c5a.large	3943324	250980	5.98%

Memory Capacity Analysis of AWS EC2 Instances: Discrepancies and Solutions Needed

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