Teads AWS Estimation Model

Goal: Estimate the carbon footprint of using AWS EC2 resources.

AWS EC2 Carbon Footprint Dataset: Google Sheets spreadsheet

Methodology

Tool turbostress was used to perform several stress tests to simulate different workloads and to report power consumption measures using RAPL.

Notes:

Available Intel-based bare-metal instances on AWS were tested: c5, m5, r5, m5zn, z1d, i3, c5n
Other platforms (AMD, ARM) weren't measured, but estimated based on known TDP values.
PUE is included in the carbon estimates (AWS: 1.2).

Assumptions:

billing reports reflect the actual usage of resources
a bare-metal instance is cut into virtual instances linearly and all instances are used by other tenants (consumption of idle is split to all)
generalization to all platforms can be made by using the results of the machine with the closest hardware and to estimate the consumption based on the TDP

Limitations:

only CPU and RAM of EC2 instances are measured, other components like storage and network cards are missing
consumption of resources that are used by AWS on top of EC2 like monitoring and logging is not included
EC2 instance types use a processor of the same family, but sometimes different models, e.g. T2 (Xeon E5-2676 V3 & Xeon E5-2686 V4)
Some EC2 instance types have burstable performance (e.g. using Intel Turbo Boost) → leads to inconsistencies
CPU frequency is not always the same due to DVFS
Platforms that are not Intel-based were not measure → Available TDP information was used to guess the energy consumption