External Input Data

Green-DCC uses external input data to provide a realistic simulation environment for each of the individual data centers:

Workload

The Workload external data in Green-DCC represents the computational demand placed on the DC. By default, Green-DCC includes a collection of open-source workload traces from Alibaba and Google DCs.

Users can customize this component by adding new workload traces to the data/Workload folder or specifying a path to existing traces in the hierarchical_env.py file under the workload_file configuration. Comparison between two workload traces of Alibaba trace (2017) and Google (2011) is given in the figure below.

workload comparison

Weather

The Weather external data in Green-DCC captures the ambient environmental conditions impacting the DC’s cooling requirements. By default, Green-DCC includes weather data files in the .epw format from various locations where DCs are commonly situated. These locations include Arizona, California, Georgia, Illinois, New York, Texas, Virginia, and Washington.

Users can customize this component by adding new weather files to the data/Weather folder or specifying a path to existing weather files in the hierarchical_env.py file under the weather_file configuration.

Each .epw file contains hourly data for various weather parameters, but for our purposes, we focus on the ambient temperature. Comparison between external temperature of the different selected locations is given below.

workload comparison

Carbon Intensity

The Carbon Intensity (CI) external data in Green-DCC represents the carbon emissions associated with electricity consumption. By default, Green-DCC includes CI data files for various locations: Arizona, California, Georgia, Illinois, New York, Texas, Virginia, and Washington. These files are located in the data/CarbonIntensity folder and are extracted from this zip file.

Users can customize this component by adding new CI files to the data/CarbonIntensity folder or specifying a path to existing files in the hierarchical_env.py file under the cintensity_file configuration. Comparison of carbon intensity across the different selected locations is given in the figure below

CI comparison by location

Furthermore, in the figure below, we show the average daily carbon intensity against the average daily coefficient of variation (CV) for various locations. This figure highlights an important perspective on the variability and magnitude of carbon intensity values across different regions. Locations with a high CV indicate greater fluctuation in carbon intensity, offering more “room to play” for DRL agents to effectively reduce carbon emissions through dynamic actions. Additionally, locations with a high average carbon intensity value present greater opportunities for achieving significant carbon emission reductions. The selected locations are highlighted, while other U.S. locations are also plotted for comparison. Regions with both high CV and high average carbon intensity are identified as prime targets for DRL agents to maximize their impact on reducing carbon emissions.

CI vs aavearage CV

Below is a summary of selected locations, typical weather values, and carbon emissions characteristics:

Location

Typical Weather

Carbon Emissions

Arizona

Hot, dry summers; mild winters

High avg CI, High variation

California

Mild, Mediterranean climate

Medium avg CI, Medium variation

Georgia

Hot, humid summers; mild winters

High avg CI, Medium variation

Illinois

Cold winters; hot, humid summers

High avg CI, Medium variation

New York

Cold winters; hot, humid summers

Medium avg CI, Medium variation

Texas

Hot summers; mild winters

Medium avg CI, High variation

Virginia

Mild climate, seasonal variations

Medium avg CI, Medium variation

Washington

Mild, temperate climate; wet winters

Low avg CI, Low variation