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EdgeFunction

Params

name

A human readable description of the object.

recurrent_edge_device_needs

A list of RecurrentEdgeProcesss.

recurrent_server_needs

A list of RecurrentServerNeeds.

Calculated attributes

fabrication_impact_repartition_weights

Dictionary with EdgeUsageJourney as keys and Edge usage journey weight in edge function impact repartition as values, in concurrent.

Example value: {
EdgeUsageJourney edge usage journey (56e892): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in M:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.000999, 0.002],
last 10 vals [0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.004, 0.003, 0.002, 0.000997],
}

Depends directly on:

through the following calculations:

You can also visit the link to edge usage journey weight in edge function impact repartition’s full calculation graph.

fabrication_impact_repartition_weight_sum

Sum of edge function 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 M:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.000999, 0.002],
last 10 vals [0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.004, 0.003, 0.002, 0.000997]

Depends directly on:

through the following calculations:

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

fabrication_impact_repartition

Dictionary with EdgeUsageJourney as keys and Edge function fabrication impact attribution to edge usage journey as values, in concurrent.

Example value: {
EdgeUsageJourney edge usage journey (56e892): 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 function fabrication impact attribution to edge usage journey’s full calculation graph.

usage_impact_repartition_weights

Dictionary with EdgeUsageJourney as keys and Edge usage journey weight in edge function impact repartition as values, in concurrent * gram / kilowatt_hour.

Example value: {
EdgeUsageJourney edge usage journey (56e892): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in ·g/kWh:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 84900, 170000],
last 10 vals [425000, 425000, 425000, 425000, 425000, 425000, 340000, 255000, 170000, 84700],
}

Depends directly on:

through the following calculations:

You can also visit the link to edge usage journey weight in edge function impact repartition’s full calculation graph.

usage_impact_repartition_weight_sum

Sum of edge function usage impact repartition weights in concurrent * gram / 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 ·g/kWh:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 84900, 170000],
last 10 vals [425000, 425000, 425000, 425000, 425000, 425000, 340000, 255000, 170000, 84700]

Depends directly on:

through the following calculations:

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

usage_impact_repartition

Dictionary with EdgeUsageJourney as keys and Edge function usage impact attribution to edge usage journey as values, in concurrent.

Example value: {
EdgeUsageJourney edge usage journey (56e892): 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 function usage impact attribution to edge usage journey’s full calculation graph.