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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 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in B:
first 10 vals [0.267, 1.07, 1.2, 1.07, 0.401, 0.134, 0.935, 0.801, 1.31, 0.489],
last 10 vals [0.555, 0.555, 0.555, 0.555, 0.555, 0.555, 0.444, 0.333, 0.222, 0.111]

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 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in cpu_core:
first 10 vals [0.00218, 0.00874, 0.00983, 0.00874, 0.00328, 0.00109, 0.00765, 0.00655, 0.0376, 0.0577],
last 10 vals [0.139, 0.139, 0.139, 0.139, 0.139, 0.139, 0.111, 0.0832, 0.0555, 0.0277]

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: 18.4 B

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: 903 B

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 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in :
first 10 vals [0.000296, 0.00118, 0.00133, 0.00118, 0.000444, 0.000148, 0.00104, 0.000887, 0.00192, 0.00294],
last 10 vals [0.00708, 0.00708, 0.00708, 0.00708, 0.00708, 0.00708, 0.00566, 0.00425, 0.00283, 0.00141]

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 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+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, 1]

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 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+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, 11.4]

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 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in kWh:
first 10 vals [0.0601, 0.0604, 0.0604, 0.0604, 0.0601, 0.06, 0.0603, 0.0603, 0.0606, 0.0609],
last 10 vals [0.0621, 0.0621, 0.0621, 0.0621, 0.0621, 0.0621, 0.0617, 0.0613, 0.0608, 0.0604]

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 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in g:
first 10 vals [6.01, 6.04, 6.04, 6.04, 6.01, 6, 6.03, 6.03, 6.06, 6.09],
last 10 vals [6.21, 6.21, 6.21, 6.21, 6.21, 6.21, 6.17, 6.13, 6.08, 6.04]

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 (6c76c4): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in :
first 10 vals [0.667, 2.67, 3, 2.67, 1, 0.333, 2.33, 2, 3, 0.667],
last 10 vals [1, 2.67, 3, 0.667, 2.67, 2, 1.67, 0.667, 2, 1],
}

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 (f473c8): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in :
first 10 vals [0.00000506, 0.0000203, 0.0000228, 0.0000203, 0.0000076, 0.00000253, 0.0000177, 0.0000152, 0.00129, 0.00253],
last 10 vals [0.00633, 0.00633, 0.00632, 0.00633, 0.00633, 0.00633, 0.00506, 0.00379, 0.00253, 0.00126],
VideoStreamingJob Video streaming job (3a2d35): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in :
first 10 vals [0.016, 0.017, 0.0172, 0.017, 0.0161, 0.0158, 0.0168, 0.0167, 0.0172, 0.016],
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 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in :
first 10 vals [0.016, 0.017, 0.0172, 0.017, 0.0162, 0.0158, 0.0169, 0.0167, 0.0185, 0.0185],
last 10 vals [0.00633, 0.00633, 0.00632, 0.00633, 0.00633, 0.00633, 0.00506, 0.00379, 0.00253, 0.00126]

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 (f473c8): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in :
first 10 vals [0.000317, 0.00119, 0.00132, 0.00119, 0.00047, 0.00016, 0.00105, 0.000911, 0.0697, 0.137],
last 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
VideoStreamingJob Video streaming job (3a2d35): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in :
first 10 vals [1, 0.999, 0.999, 0.999, 1, 1, 0.999, 0.999, 0.93, 0.863],
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 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 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in :
first 10 vals [0.016, 0.017, 0.0172, 0.017, 0.0162, 0.0158, 0.0169, 0.0167, 0.0185, 0.0185],
last 10 vals [0.00633, 0.00633, 0.00632, 0.00633, 0.00633, 0.00633, 0.00506, 0.00379, 0.00253, 0.00126]

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 (f473c8): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in :
first 10 vals [0.000317, 0.00119, 0.00132, 0.00119, 0.00047, 0.00016, 0.00105, 0.000911, 0.0697, 0.137],
last 10 vals [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
VideoStreamingJob Video streaming job (3a2d35): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in :
first 10 vals [1, 0.999, 0.999, 0.999, 1, 1, 0.999, 0.999, 0.93, 0.863],
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 server usage impact attribution to Manually defined job’s full calculation graph.