EdgeAppliance

Params

name

A human readable description of the object.

carbon_footprint_fabrication

Carbon footprint fabrication of edge appliance in kilogram.

power

Power of edge appliance in watt.

lifespan

Lifespan of edge appliance in year.

idle_power

Idle power of edge appliance in watt.

Backwards links

RecurrentEdgeWorkload

Calculated attributes

structure_carbon_footprint_fabrication

ExplainableQuantity in kilogram, representing the Structure fabrication carbon footprint of edge appliance.

Example value: 60 kg

Depends directly on:

Carbon footprint fabrication of edge appliance

through the following calculations:

You can also visit the link to Structure fabrication carbon footprint of edge appliance’s full calculation graph.

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.

structure_fabrication_footprint_per_usage_pattern

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

Example value: {
EdgeUsagePattern Default edge usage pattern (eef937): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.000855, 0.00171],
last 10 vals [0.00428, 0.00428, 0.00427, 0.00427, 0.00428, 0.00428, 0.00342, 0.00256, 0.00171, 0.000853],
}

Depends directly on:

through the following calculations:

You can also visit the link to Hourly edge appliance 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 edge appliance instances fabrication footprint for default edge usage pattern as values, in kilogram.

Example value: {
EdgeUsagePattern Default edge usage pattern (eef937): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.000855, 0.00171],
last 10 vals [0.00428, 0.00428, 0.00427, 0.00427, 0.00428, 0.00428, 0.00342, 0.00256, 0.00171, 0.000853],
}

Depends directly on:

through the following calculations:

You can also visit the link to Hourly edge appliance 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 edge appliance instances for default edge usage pattern as values, in concurrent ** 2 * hour * watt.

Example value: {
EdgeUsagePattern Default edge usage pattern (eef937): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in MWh:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.0175, 0.035],
last 10 vals [0.0875, 0.0874, 0.0874, 0.0874, 0.0875, 0.0875, 0.0699, 0.0524, 0.035, 0.0174],
}

Depends directly on:

Hourly energy consumed by edge appliance appliance instances for Default edge usage pattern

through the following calculations:

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

energy_footprint_per_usage_pattern

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

Example value: {
EdgeUsagePattern Default edge usage pattern (eef937): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.00149, 0.00297],
last 10 vals [0.00744, 0.00743, 0.00743, 0.00743, 0.00744, 0.00743, 0.00594, 0.00446, 0.00297, 0.00148],
}

Depends directly on:

edge appliance appliance energy footprint for Default edge usage pattern

through the following calculations:

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

instances_fabrication_footprint

Edge appliance total fabrication footprint across usage patterns 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 t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.000855, 0.00171],
last 10 vals [0.00428, 0.00428, 0.00427, 0.00427, 0.00428, 0.00428, 0.00342, 0.00256, 0.00171, 0.000853]

Depends directly on:

Hourly edge appliance instances fabrication footprint for Default edge usage pattern

through the following calculations:

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

fabrication_footprint_breakdown_by_source

Dictionary with EdgeApplianceComponent as keys and Edge appliance fabrication footprint attributed to edge appliance appliance as values, in kilogram.

Example value: {
EdgeApplianceComponent edge appliance appliance (5a6b2e): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.000855, 0.00171],
last 10 vals [0.00428, 0.00428, 0.00427, 0.00427, 0.00428, 0.00428, 0.00342, 0.00256, 0.00171, 0.000853],
}

Depends directly on:

through the following calculations:

You can also visit the link to edge appliance fabrication footprint attributed to edge appliance appliance’s full calculation graph.

instances_energy

Edge appliance total energy consumed across usage patterns in concurrent ** 2 * hour * watt.

Example value: 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in MWh:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.0175, 0.035],
last 10 vals [0.0875, 0.0874, 0.0874, 0.0874, 0.0875, 0.0875, 0.0699, 0.0524, 0.035, 0.0174]

Depends directly on:

Hourly energy consumed by edge appliance instances for Default edge usage pattern

through the following calculations:

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

energy_footprint

Edge appliance total energy footprint across usage patterns 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 t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.00149, 0.00297],
last 10 vals [0.00744, 0.00743, 0.00743, 0.00743, 0.00744, 0.00743, 0.00594, 0.00446, 0.00297, 0.00148]

Depends directly on:

edge appliance energy footprint for Default edge usage pattern

through the following calculations:

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

fabrication_impact_repartition_weights

Dictionary with RecurrentEdgeWorkloadNeed as keys and Edge workload workload need fabrication weight in edge appliance impact repartition as values, in kilogram.

Example value: {
RecurrentEdgeWorkloadNeed edge workload workload need (d162cc): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.000855, 0.00171],
last 10 vals [0.00428, 0.00428, 0.00427, 0.00427, 0.00428, 0.00428, 0.00342, 0.00256, 0.00171, 0.000853],
}

Depends directly on:

through the following calculations:

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

fabrication_impact_repartition_weight_sum

Sum of edge appliance fabrication impact repartition weights 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 t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.000855, 0.00171],
last 10 vals [0.00428, 0.00428, 0.00427, 0.00427, 0.00428, 0.00428, 0.00342, 0.00256, 0.00171, 0.000853]

Depends directly on:

edge workload workload need fabrication weight in edge appliance impact repartition

through the following calculations:

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

fabrication_impact_repartition

Dictionary with RecurrentEdgeWorkloadNeed as keys and Edge appliance fabrication impact attribution to edge workload workload need as values, in concurrent.

Example value: {
RecurrentEdgeWorkloadNeed edge workload workload need (d162cc): 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 edge appliance fabrication impact attribution to edge workload workload need’s full calculation graph.

usage_impact_repartition_weights

Dictionary with RecurrentEdgeWorkloadNeed as keys and Edge workload workload need usage weight in edge appliance impact repartition as values, in kilogram.

Example value: {
RecurrentEdgeWorkloadNeed edge workload workload need (d162cc): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.00149, 0.00297],
last 10 vals [0.00744, 0.00743, 0.00743, 0.00743, 0.00744, 0.00743, 0.00594, 0.00446, 0.00297, 0.00148],
}

Depends directly on:

through the following calculations:

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

usage_impact_repartition_weight_sum

Sum of edge appliance usage impact repartition weights 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 t:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.00149, 0.00297],
last 10 vals [0.00744, 0.00743, 0.00743, 0.00743, 0.00744, 0.00743, 0.00594, 0.00446, 0.00297, 0.00148]

Depends directly on:

edge workload workload need usage weight in edge appliance impact repartition

through the following calculations:

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

usage_impact_repartition

Dictionary with RecurrentEdgeWorkloadNeed as keys and Edge appliance usage impact attribution to edge workload workload need as values, in concurrent.

Example value: {
RecurrentEdgeWorkloadNeed edge workload workload need (d162cc): 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 edge appliance usage impact attribution to edge workload workload need’s full calculation graph.