EdgeDevice

Params

name

A human readable description of the object.

structure_carbon_footprint_fabrication

Structure fabrication carbon footprint of custom edge device in kilogram.

components

A list of EdgeRAMComponents.

lifespan

Lifespan of custom edge device in year.

Backwards links

RecurrentEdgeDeviceNeed

Calculated attributes

lifespan_validation

Example value: no value

Depends directly on:

through the following calculations:

You can also visit the link to no value’s full calculation graph.

component_needs_edge_device_validation

Example value: no value

Depends directly on:

through the following calculations:

You can also visit the link to no value’s full calculation graph.

total_nb_of_units

ExplainableQuantity in dimensionless, representing the Custom edge device has no group (default count = 1).

Example value: 1

Depends directly on:

through the following calculations:

You can also visit the link to custom edge device has no group (default count = 1)’s full calculation graph.

structure_fabrication_footprint_per_usage_pattern

Dictionary with EdgeUsagePattern as keys and Hourly custom edge device structure fabrication footprint for default edge usage pattern as values, in kilogram.

Example value: {
EdgeUsagePattern Default edge usage pattern (fa81f1): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.000949, 0.0019, 0.00285],
last 10 vals [0.00475, 0.00475, 0.00475, 0.00475, 0.00475, 0.0038, 0.00285, 0.0019, 0.000948, 0],
}

Depends directly on:

through the following calculations:

You can also visit the link to Hourly custom edge device structure fabrication footprint for Default edge usage pattern’s full calculation graph.

instances_fabrication_footprint_per_usage_pattern

Dictionary with EdgeUsagePattern as keys and Hourly custom edge device instances fabrication footprint for default edge usage pattern as values, in kilogram.

Example value: {
EdgeUsagePattern Default edge usage pattern (fa81f1): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.00326, 0.00653, 0.0098],
last 10 vals [0.0163, 0.0163, 0.0163, 0.0163, 0.0163, 0.0131, 0.00979, 0.00653, 0.00326, 0],
}

Depends directly on:

through the following calculations:

You can also visit the link to Hourly custom edge device instances fabrication footprint for Default edge usage pattern’s full calculation graph.

instances_energy_per_usage_pattern

Dictionary with EdgeUsagePattern as keys and Hourly energy consumed by custom edge device instances for default edge usage pattern as values, in concurrent * hour * watt.

Example value: {
EdgeUsagePattern Default edge usage pattern (fa81f1): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in MWh:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.0103, 0.0206, 0.0309],
last 10 vals [0.0515, 0.0515, 0.0515, 0.0515, 0.0515, 0.0412, 0.0309, 0.0206, 0.0103, 0],
}

Depends directly on:

through the following calculations:

You can also visit the link to Hourly energy consumed by custom edge device instances for Default edge usage pattern’s full calculation graph.

energy_footprint_per_usage_pattern

Dictionary with EdgeUsagePattern as keys and Custom edge device energy footprint for default edge usage pattern as values, in kilogram.

Example value: {
EdgeUsagePattern Default edge usage pattern (fa81f1): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.000874, 0.00175, 0.00262],
last 10 vals [0.00437, 0.00437, 0.00437, 0.00438, 0.00438, 0.0035, 0.00262, 0.00175, 0.000873, 0],
}

Depends directly on:

through the following calculations:

You can also visit the link to custom edge device energy footprint for Default edge usage pattern’s full calculation graph.

instances_fabrication_footprint

Custom edge device total fabrication footprint across usage patterns 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 t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.00326, 0.00653, 0.0098],
last 10 vals [0.0163, 0.0163, 0.0163, 0.0163, 0.0163, 0.0131, 0.00979, 0.00653, 0.00326, 0]

Depends directly on:

Hourly custom edge device instances fabrication footprint for Default edge usage pattern

through the following calculations:

You can also visit the link to custom edge device total fabrication footprint across usage patterns’s full calculation graph.

fabrication_footprint_breakdown_by_source

Dictionary with EdgeRAMComponent as keys and Custom edge device fabrication footprint attributed to edge ram component as values, in kilogram.

Example value: {
EdgeRAMComponent edge RAM component (b4823f): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.000696, 0.00139, 0.00209],
last 10 vals [0.00348, 0.00348, 0.00348, 0.00348, 0.00348, 0.00279, 0.00209, 0.00139, 0.000695, 0],
EdgeCPUComponent edge CPU component (cbb00a): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.000696, 0.00139, 0.00209],
last 10 vals [0.00348, 0.00348, 0.00348, 0.00348, 0.00348, 0.00279, 0.00209, 0.00139, 0.000695, 0],
EdgeStorage edge storage component (6e17ce): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.00187, 0.00374, 0.00562],
last 10 vals [0.00937, 0.00936, 0.00937, 0.00937, 0.00937, 0.00749, 0.00562, 0.00374, 0.00187, 0],
}

Depends directly on:

through the following calculations:

You can also visit the link to custom edge device fabrication footprint attributed to edge RAM component’s full calculation graph.

instances_energy

Custom edge device total energy consumed across usage patterns in concurrent * hour * watt.

Example value: 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in MWh:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.0103, 0.0206, 0.0309],
last 10 vals [0.0515, 0.0515, 0.0515, 0.0515, 0.0515, 0.0412, 0.0309, 0.0206, 0.0103, 0]

Depends directly on:

Hourly energy consumed by custom edge device instances for Default edge usage pattern

through the following calculations:

You can also visit the link to custom edge device total energy consumed across usage patterns’s full calculation graph.

energy_footprint

Custom edge device total energy footprint across usage patterns 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 t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.000874, 0.00175, 0.00262],
last 10 vals [0.00437, 0.00437, 0.00437, 0.00438, 0.00438, 0.0035, 0.00262, 0.00175, 0.000873, 0]

Depends directly on:

custom edge device energy footprint for Default edge usage pattern

through the following calculations:

You can also visit the link to custom edge device total energy footprint across usage patterns’s full calculation graph.

fabrication_impact_repartition_weights

Dictionary with RecurrentEdgeComponentNeed as keys and Ram need fabrication weight in custom edge device impact repartition as values, in kilogram.

Example value: {
RecurrentEdgeComponentNeed RAM need (8a16ab): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.000696, 0.00139, 0.00209],
last 10 vals [0.00348, 0.00348, 0.00348, 0.00348, 0.00348, 0.00279, 0.00209, 0.00139, 0.000695, 0],
RecurrentEdgeComponentNeed CPU need (9b9328): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.000696, 0.00139, 0.00209],
last 10 vals [0.00348, 0.00348, 0.00348, 0.00348, 0.00348, 0.00279, 0.00209, 0.00139, 0.000695, 0],
RecurrentEdgeStorageNeed Storage need (122cb4): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
last 10 vals [0.00937, 0.00936, 0.00937, 0.00937, 0.00937, 0.00749, 0.00562, 0.00374, 0.00187, 0],
}

Depends directly on:

through the following calculations:

You can also visit the link to RAM need fabrication weight in custom edge device impact repartition’s full calculation graph.

fabrication_impact_repartition_weight_sum

Sum of custom edge device fabrication impact repartition weights 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 t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.00139, 0.00279, 0.00418],
last 10 vals [0.0163, 0.0163, 0.0163, 0.0163, 0.0163, 0.0131, 0.00979, 0.00653, 0.00326, 0]

Depends directly on:

through the following calculations:

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

fabrication_impact_repartition

Dictionary with RecurrentEdgeComponentNeed as keys and Custom edge device fabrication impact attribution to ram need as values, in concurrent.

Example value: {
RecurrentEdgeComponentNeed RAM need (8a16ab): 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, 0.5, 0.5, 0.5],
last 10 vals [0.213, 0.213, 0.213, 0.213, 0.213, 0.213, 0.213, 0.213, 0.213, 1],
RecurrentEdgeComponentNeed CPU need (9b9328): 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, 0.5, 0.5, 0.5],
last 10 vals [0.213, 0.213, 0.213, 0.213, 0.213, 0.213, 0.213, 0.213, 0.213, 1],
RecurrentEdgeStorageNeed Storage need (122cb4): 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, 0, 0, 0],
last 10 vals [0.573, 0.573, 0.573, 0.573, 0.573, 0.573, 0.573, 0.573, 0.573, 1],
}

Depends directly on:

through the following calculations:

You can also visit the link to custom edge device fabrication impact attribution to RAM need’s full calculation graph.

usage_impact_repartition_weights

Dictionary with RecurrentEdgeComponentNeed as keys and Ram need usage weight in custom edge device impact repartition as values, in kilogram.

Example value: {
RecurrentEdgeComponentNeed RAM need (8a16ab): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.00034, 0.000679, 0.00102],
last 10 vals [0.0017, 0.0017, 0.0017, 0.0017, 0.0017, 0.00136, 0.00102, 0.000679, 0.000339, 0],
RecurrentEdgeComponentNeed CPU need (9b9328): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.000535, 0.00107, 0.00161],
last 10 vals [0.00268, 0.00267, 0.00268, 0.00268, 0.00268, 0.00214, 0.0016, 0.00107, 0.000534, 0],
RecurrentEdgeStorageNeed Storage need (122cb4): no value,
}

Depends directly on:

through the following calculations:

You can also visit the link to RAM need usage weight in custom edge device impact repartition’s full calculation graph.

usage_impact_repartition_weight_sum

Sum of custom edge device usage impact repartition weights 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 t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.000874, 0.00175, 0.00262],
last 10 vals [0.00437, 0.00437, 0.00437, 0.00438, 0.00438, 0.0035, 0.00262, 0.00175, 0.000873, 0]

Depends directly on:

through the following calculations:

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

usage_impact_repartition

Dictionary with RecurrentEdgeComponentNeed as keys and Custom edge device usage impact attribution to ram need as values, in concurrent.

Example value: {
RecurrentEdgeComponentNeed RAM need (8a16ab): 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, 0.388, 0.388, 0.388],
last 10 vals [0.388, 0.388, 0.388, 0.388, 0.388, 0.388, 0.388, 0.388, 0.388, 1],
RecurrentEdgeComponentNeed CPU need (9b9328): 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, 0.612, 0.612, 0.612],
last 10 vals [0.612, 0.612, 0.612, 0.612, 0.612, 0.612, 0.612, 0.612, 0.612, 1],
RecurrentEdgeStorageNeed Storage need (122cb4): no value,
}

Depends directly on:

through the following calculations:

You can also visit the link to custom edge device usage impact attribution to RAM need’s full calculation graph.