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Server

Params

name

A human readable description of the object.

server_type

Server type of server.

For example, autoscaling.

carbon_footprint_fabrication

Carbon footprint fabrication of server in kilogram.

power

Power of server in watt.

lifespan

Lifespan of server in year.

idle_power

Idle power of server in watt.

ram

Ram of server in gigabyte_ram.

compute

Nb cpu cores of server in cpu_core.

power_usage_effectiveness

Pue of server in dimensionless.

average_carbon_intensity

Average carbon intensity of server electricity in gram / kilowatt_hour.

utilization_rate

Server utilization rate in dimensionless.

base_ram_consumption

Base ram consumption of server in megabyte_ram.

base_compute_consumption

Base cpu core consumption of server in cpu_core.

storage

An instance of Storage.

fixed_nb_of_instances

User defined number of server instances. Can be an EmptyExplainableObject in which case the optimum number of instances will be computed, or an ExplainableQuantity with a dimensionless value, in which case e-footprint will raise an error if the object needs more instances than available.

Calculated attributes

hour_by_hour_ram_need

Server hour by hour ram need in gigabyte_ram.

Example value: 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in GB ram:
first 10 vals [0.1, 0.0668, 0.15, 0.0167, 0.0167, 0.0668, 0.0501, 0.131, 0.0945, 0.142],
last 10 vals [0.0694, 0.0694, 0.0694, 0.0694, 0.0694, 0.0555, 0.0416, 0.0277, 0.0138, 0]

Depends directly on:

through the following calculations:

You can also visit the link to server hour by hour ram need’s full calculation graph.

hour_by_hour_compute_need

Server hour by hour compute need in cpu_core.

Example value: 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in cpu core:
first 10 vals [0.00655, 0.00437, 0.00983, 0.00109, 0.00109, 0.00437, 0.00328, 0.0354, 0.0599, 0.0898],
last 10 vals [0.139, 0.139, 0.139, 0.139, 0.139, 0.111, 0.0832, 0.0555, 0.0277, 0]

Depends directly on:

through the following calculations:

You can also visit the link to server hour by hour compute need’s full calculation graph.

occupied_ram_per_instance

ExplainableQuantity in gigabyte_ram, representing the Occupied ram per server instance including services.

Example value: 2.3 GB ram

Depends directly on:

through the following calculations:

You can also visit the link to Occupied RAM per server instance including services’s full calculation graph.

occupied_compute_per_instance

ExplainableQuantity in cpu_core, representing the Occupied cpu per server instance including services.

Example value: 2 cpu core

Depends directly on:

through the following calculations:

You can also visit the link to Occupied CPU per server instance including services’s full calculation graph.

available_ram_per_instance

ExplainableQuantity in gigabyte_ram, representing the Available ram per server instance.

Example value: 113 GB ram

Depends directly on:

through the following calculations:

You can also visit the link to Available RAM per server instance’s full calculation graph.

available_compute_per_instance

ExplainableQuantity in cpu_core, representing the Available cpu per server instance.

Example value: 19.6 cpu core

Depends directly on:

through the following calculations:

You can also visit the link to Available CPU per server instance’s full calculation graph.

raw_nb_of_instances

Hourly raw number of server instances in concurrent.

Example value: 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in :
first 10 vals [0.000887, 0.000591, 0.00133, 0.000148, 0.000148, 0.000591, 0.000444, 0.00181, 0.00305, 0.00458],
last 10 vals [0.00708, 0.00708, 0.00708, 0.00708, 0.00708, 0.00566, 0.00425, 0.00283, 0.00141, 0]

Depends directly on:

through the following calculations:

You can also visit the link to Hourly raw number of server instances’s full calculation graph.

nb_of_instances

Hourly number of server instances in concurrent.

Example value: 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in :
first 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
last 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 0]

Depends directly on:

through the following calculations:

You can also visit the link to Hourly number of server instances’s full calculation graph.

instances_fabrication_footprint

Hourly server instances fabrication footprint in kilogram.

Example value: 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in g:
first 10 vals [11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4],
last 10 vals [11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 0]

Depends directly on:

through the following calculations:

You can also visit the link to Hourly server instances fabrication footprint’s full calculation graph.

instances_energy

Hourly energy consumed by server instances in kilowatt_hour.

Example value: 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in kWh:
first 10 vals [0.0603, 0.0602, 0.0604, 0.06, 0.06, 0.0602, 0.0601, 0.0605, 0.0609, 0.0614],
last 10 vals [0.0621, 0.0621, 0.0621, 0.0621, 0.0621, 0.0617, 0.0613, 0.0608, 0.0604, 0]

Depends directly on:

through the following calculations:

You can also visit the link to Hourly energy consumed by server instances’s full calculation graph.

energy_footprint

Hourly server energy footprint in kilogram.

Example value: 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in g:
first 10 vals [6.03, 6.02, 6.04, 6, 6, 6.02, 6.01, 6.05, 6.09, 6.14],
last 10 vals [6.21, 6.21, 6.21, 6.21, 6.21, 6.17, 6.13, 6.08, 6.04, 0]

Depends directly on:

through the following calculations:

You can also visit the link to Hourly server energy footprint’s full calculation graph.

service_total_job_volumes

Dictionary with VideoStreaming as keys and Total job volume for video streaming service as values, in concurrent.

Example value: {
VideoStreaming Video streaming service (79c681): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [2, 1.33, 3, 0.333, 0.333, 1.33, 1, 2.33, 1.33, 2],
last 10 vals [3, 3, 2.67, 1, 2, 2, 1, 1.33, 2.33, 0.667],
}

Depends directly on:

through the following calculations:

You can also visit the link to Total job volume for Video streaming service’s full calculation graph.

job_repartition_weights

Dictionary with Job as keys and Manually defined job weight in server impact repartition as values, in concurrent.

Example value: {
Job Manually defined job (940432): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in :
first 10 vals [0.0000152, 0.0000101, 0.0000228, 0.00000253, 0.00000253, 0.0000101, 0.0000076, 0.00128, 0.00254, 0.00381],
last 10 vals [0.00633, 0.00632, 0.00633, 0.00633, 0.00633, 0.00506, 0.00379, 0.00253, 0.00126, 0],
VideoStreamingJob Video streaming job (32541b): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in :
first 10 vals [0.0167, 0.0163, 0.0172, 0.0158, 0.0158, 0.0163, 0.0161, 0.0168, 0.0163, 0.0167],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
}

Depends directly on:

through the following calculations:

You can also visit the link to Manually defined job weight in server impact repartition’s full calculation graph.

fabrication_impact_repartition_weight_sum

Sum of server fabrication impact repartition weights in concurrent.

Example value: 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in :
first 10 vals [0.0167, 0.0163, 0.0172, 0.0158, 0.0158, 0.0163, 0.0162, 0.0181, 0.0189, 0.0205],
last 10 vals [0.00633, 0.00632, 0.00633, 0.00633, 0.00633, 0.00506, 0.00379, 0.00253, 0.00126, 0]

Depends directly on:

through the following calculations:

You can also visit the link to Sum of server fabrication impact repartition weights’s full calculation graph.

fabrication_impact_repartition

Dictionary with Job as keys and Server fabrication impact attribution to manually defined job as values, in concurrent.

Example value: {
Job Manually defined job (940432): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in :
first 10 vals [0.000911, 0.00062, 0.00132, 0.00016, 0.00016, 0.00062, 0.00047, 0.0707, 0.135, 0.186],
last 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
VideoStreamingJob Video streaming job (32541b): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in :
first 10 vals [0.999, 0.999, 0.999, 1, 1, 0.999, 1, 0.929, 0.865, 0.814],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
}

Depends directly on:

through the following calculations:

You can also visit the link to server fabrication impact attribution to Manually defined job’s full calculation graph.

usage_impact_repartition_weight_sum

Sum of server usage impact repartition weights in concurrent.

Example value: 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in :
first 10 vals [0.0167, 0.0163, 0.0172, 0.0158, 0.0158, 0.0163, 0.0162, 0.0181, 0.0189, 0.0205],
last 10 vals [0.00633, 0.00632, 0.00633, 0.00633, 0.00633, 0.00506, 0.00379, 0.00253, 0.00126, 0]

Depends directly on:

through the following calculations:

You can also visit the link to Sum of server usage impact repartition weights’s full calculation graph.

usage_impact_repartition

Dictionary with Job as keys and Server usage impact attribution to manually defined job as values, in concurrent.

Example value: {
Job Manually defined job (940432): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in :
first 10 vals [0.000911, 0.00062, 0.00132, 0.00016, 0.00016, 0.00062, 0.00047, 0.0707, 0.135, 0.186],
last 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
VideoStreamingJob Video streaming job (32541b): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in :
first 10 vals [0.999, 0.999, 0.999, 1, 1, 0.999, 1, 0.929, 0.865, 0.814],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
}

Depends directly on:

through the following calculations:

You can also visit the link to server usage impact attribution to Manually defined job’s full calculation graph.