Game Server Energy Management Logs
This dataset provides granular logs of energy consumption, operational metrics, and sustainability indicators for distributed game servers. It supports anomaly detection, resource optimization, and ESG reporting, empowering gaming companies to enhance efficiency and meet environmental goals. The schema enables robust analysis of usage patterns, carbon emissions, and renewable energy adoption across server infrastructures.
Sample rows
preview · 8 of 120 rows · all 17 columns| log_idstring | anomaly_typestring | cpu_usage_percentfloat | anomaly_detectedboolean | location_countrystring | server_idstring | server_namestring | location_citystring | location_data_centerstring | timestampdatetime | energy_consumed_kwhfloat | memory_usage_percentfloat | network_usage_mbpsfloat | player_sessions_activeinteger | renewable_energy_percentfloat | carbon_emissions_kgfloat | notesstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| c5e3d9f1-2a4b-49e0 | blank | 41.5 | false | United States | srv-1001 | AlphaNode-01 | San Francisco | DC-West-1 | 2024-06-01T08:15:00Z | 45.8 | 38.2 | 220.1 | 910 | 60.2 | 34.5 | Normal usage, peak morning traffic. |
| f2ec8d88-1ee7-4b23 | blank | 36.7 | false | Canada | srv-1002 | BetaNode-02 | Toronto | DC-North-2 | 2024-06-01T09:00:00Z | 38.4 | 44.1 | 198.3 | 805 | 72.6 | 22.1 | Higher renewables due to wind supply. |
| a85c2b3f-8ee2-4cda | spike | 58.6 | true | Germany | srv-1003 | GammaNode-03 | Frankfurt | DC-EU-3 | 2024-06-01T10:30:00Z | 67.2 | 53.4 | 302.5 | 1202 | 49.8 | 58.9 | CPU spike detected after game patch. |
| e1b3d7a9-7fb0-45d3 | blank | 44.9 | false | United Kingdom | srv-1004 | DeltaNode-04 | London | DC-UK-4 | 2024-06-01T11:15:00Z | 52.7 | 47.3 | 253.9 | 990 | 39.2 | 47.6 | Steady traffic, no anomalies. |
| b67f0c2a-8cdb-4a12 | blank | 29.2 | false | France | srv-1005 | EpsilonNode-05 | Paris | DC-FR-5 | 2024-06-01T12:00:00Z | 33.5 | 35.8 | 130.7 | 645 | 63.7 | 19.4 | Low session count, off-peak. |
| d2ec4f9b-7c3e-4263 | blank | 34.1 | false | Sweden | srv-1006 | ZetaNode-06 | Stockholm | DC-SE-6 | 2024-06-01T12:45:00Z | 40.9 | 39.7 | 172.4 | 701 | 81.4 | 11.2 | Hydro energy predominant. |
| f9a1d6b5-23c5-4c8f | blank | 53.9 | false | Japan | srv-1007 | EtaNode-07 | Osaka | DC-JP-7 | 2024-06-01T13:30:00Z | 57.4 | 50.3 | 289 | 1110 | 29.6 | 61.7 | Usage increase before evening. |
| c7b2a4d6-9f4e-4bcd | blank | 45.2 | false | Australia | srv-1008 | ThetaNode-08 | Sydney | DC-AU-8 | 2024-06-01T14:15:00Z | 49.1 | 46.8 | 210.3 | 925 | 35.7 | 44.3 | Cloud cover reduced solar contribution. |
| e2fa5b7c-2e16-44c1 | drop | 61.3 | true | Brazil | srv-1009 | IotaNode-09 | Sao Paulo | DC-BR-9 | 2024-06-01T15:00:00Z | 63.8 | 59.9 | 347.5 | 1351 | 22.4 | 79.8 | Sharp drop in sessions; network issue suspected. |
| a3d6b8a5-6e1c-4c5f | blank | 37.8 | false | India | srv-1010 | KappaNode-10 | Mumbai | DC-IN-10 | 2024-06-01T15:45:00Z | 42.6 | 41.6 | 185.2 | 820 | 14.2 | 70.1 | Monsoon affecting solar input. |
| d6e1c3b7-7c4d-4e8f | blank | 27.1 | false | Singapore | srv-1011 | LambdaNode-11 | Singapore | DC-SG-11 | 2024-06-01T16:30:00Z | 31.7 | 33.2 | 110.8 | 612 | 18.6 | 24.9 | Stable, routine maintenance window. |
| b8f1d6a3-5c2e-4d4d | blank | 43.5 | false | South Africa | srv-1012 | MuNode-12 | Johannesburg | DC-ZA-12 | 2024-06-01T17:15:00Z | 48.9 | 45.7 | 202.1 | 880 | 21.9 | 54.6 | Usage up after local promotion. |
| c6b3a2f8-8d1c-4a7f | blank | 31.5 | false | Italy | srv-1013 | NuNode-13 | Rome | DC-IT-13 | 2024-06-01T18:00:00Z | 35.6 | 36.3 | 148.7 | 670 | 59.1 | 15.2 | Solar uptake high midday. |
| e9f2a7d5-2a7e-4c3b | blank | 49.2 | false | Spain | srv-1014 | XiNode-14 | Madrid | DC-ES-14 | 2024-06-01T18:45:00Z | 54.1 | 52.1 | 231.6 | 1050 | 65.8 | 19.6 | Wind energy higher than forecast. |
| a6b2e3c7-4d9f-4f1d | blank | 25.2 | false | Netherlands | srv-1015 | OmicronNode-15 | Amsterdam | DC-NL-15 | 2024-06-01T19:30:00Z | 29.8 | 31.1 | 98.5 | 580 | 74.3 | 8.7 | Off-peak, mostly green energy. |
| d4c7e8b1-7f2a-4c3f | blank | 42.3 | false | Russia | srv-1016 | PiNode-16 | Moscow | DC-RU-16 | 2024-06-01T20:15:00Z | 47.2 | 43.8 | 195.4 | 865 | 20.5 | 55.8 | Coal usage increased during interval. |
| b3a7f9d2-5e1c-42a3 | hardware_failure | 57.5 | true | China | srv-1017 | RhoNode-17 | Shanghai | DC-CN-17 | 2024-06-01T21:00:00Z | 60.3 | 56.2 | 329.7 | 1288 | 12.1 | 90.2 | Hardware failure flagged by monitoring. |
| c9d7b2e1-8a5c-4f7d | blank | 32.7 | false | South Korea | srv-1018 | SigmaNode-18 | Seoul | DC-KR-18 | 2024-06-01T21:45:00Z | 36.8 | 37.4 | 155.2 | 692 | 23.8 | 42.8 | Normal operation, maintenance scheduled. |
| e3f6a9b8-4d7c-4b2e | blank | 20.9 | false | Norway | srv-1019 | TauNode-19 | Oslo | DC-NO-19 | 2024-06-02T02:00:00Z | 24.5 | 26.2 | 72.6 | 310 | 99.2 | 1.2 | Night shift, nearly all hydro. |
| a2c7e9b1-7d8f-4c9e | blank | 35.2 | false | Argentina | srv-1020 | UpsilonNode-20 | Buenos Aires | DC-AR-20 | 2024-06-02T02:45:00Z | 39.3 | 41.8 | 162.9 | 705 | 27.5 | 41.3 | Solar low, wind moderate. |
What the 120 rows show
from the 120-row sample- 16%anomaly_
detected = true - 39.0median cpu_
usage_ percent - 39.6median energy_
consumed_ kwh - 43.8median memory_
usage_ percent - 163.9median network_
usage_ mbps - 726median player_
sessions_ active
Median 39.0, from 20.9 to 86.2.
- string 8
- integer 1
- float 6
- datetime 1
- boolean 1
Columns
17 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 8 columns | |||
log_id | string | Unique identifier for each energy management log entryunique | c5e3d9f1-2a4b-49e0 |
server_id | string | Unique identifier for the game server | srv-1001 |
server_name | string | Human-readable name for the game serveroptional | AlphaNode-01 |
location_country | string | Country where the server is physically located | United States |
location_city | string | City where the server is physically locatedoptional | San Francisco |
location_data_center | string | Name or code of the data center hosting the serveroptional | DC-West-1 |
anomaly_type | string | Type of anomaly detected (e.g., spike, drop, hardware failure); null if no anomalyspike · drop · hardware_failure · other · · optional | spike |
notes | string | Additional notes or comments from operations managers regarding this log entryoptional | Morning ramp up. |
| Numbers 7 columns | |||
energy_consumed_kwh | float | Amount of energy consumed by the server during the logging interval, in kilowatt-hours (kWh)0 or more | 45.8 |
cpu_usage_percent | float | Average CPU utilization during the logging interval, as a percentage0 to 100 · optional | 41.5 |
memory_usage_percent | float | Average memory utilization during the logging interval, as a percentage0 to 100 · optional | 38.2 |
network_usage_mbps | float | Average network throughput during the logging interval, in megabits per second (Mbps)0 or more · optional | 220.1 |
player_sessions_active | integer | Number of active player sessions during the logging interval0 or more · optional | 910 |
renewable_energy_percent | float | Percentage of energy sourced from renewables during the logging interval0 to 100 · optional | 60.2 |
carbon_emissions_kg | float | Estimated carbon emissions generated by the server during the logging interval, in kilograms0 or more · optional | 34.5 |
| Dates and times 1 column | |||
timestamp | datetime | Date and time when the energy usage was recorded | 2024-06-01T08:15:00Z |
| True or false 1 column | |||
anomaly_detected | boolean | Indicates whether an anomaly in energy usage was detected during this intervaloptional | false |
Use it for
A gaming dashboard
The anomaly_
detected rate, cpu_ usage_ percent by anomaly_ type and a breakdown of location_ country. Excel, Power BI or Tableau. Why do 19 of 120 rows have anomaly_
detected = true? A class exercise
Hand out the rows and one question. Everyone works from the same 120 rows.
- Logs120c5e3d9f1-2a4b-49e041.5—a85c2b3f-8ee2-4cda58.6spikee2fa5b7c-2e16-44c161.3drop
A software demo
Believable logs with server_
id, server_ name and location_ country to fill a screen in front of a buyer.
This dataset120 rows17 columns
Yours1,000 rows17 columnslocation_country: UK only
blueprint · game-server-energy-management-logs
Behind this dataset
Same schema. As many rows as you need.
These 120 rows came out of a blueprint — 17 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- Each log entry must include server ID, timestamp, and energy consumed (kWh).
- Flag peak usage hours and classify by server type (e.g., matchmaking, leaderboard, community).
- Annotate logs with maintenance events and hardware changes.
- Identify and flag outliers where energy consumption exceeds 2x typical baseline.
- Record renewable energy utilization percentage for each server site.
- Server location and hosting provider must be specified for compliance tracking.
Open the blueprint in Data Factory
1 credit per row. Preview 20 rows free. Buy rows for the full run.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- game-server-energy-management-logs