RecurrentEdgeWorkloadNeed
Internal RecurrentEdgeComponentNeed created automatically by a RecurrentEdgeWorkload, mirroring its 0..1 workload curve on the parent appliance's workload component.
Params
name
A human readable description of the object.
edge_component
Workload component on the parent EdgeAppliance that this need targets.
An instance of EdgeApplianceComponent.
Backwards links
Calculated attributes
recurrent_need
Recurrent workload, copied from the parent RecurrentEdgeWorkload's workload profile.
Example value: 168 values in :
first 10 vals [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],
last 10 vals [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
Depends directly on:
through the following calculations:
You can also visit the link to Recurrent need’s full calculation graph.
recurrent_need_validation
Validates that the recurrent need uses a unit compatible with its target component, and (for workload-style needs) that values stay between 0 and 1.
Example value: 168 values in :
first 10 vals [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],
last 10 vals [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
Depends directly on:
through the following calculations:
You can also visit the link to Validated recurrent need’s full calculation graph.
unitary_hourly_need_per_usage_pattern
Hourly resource demand for one edge device, generated by replaying the typical-week pattern across the modeling period in the country's timezone, and scaled by how often the need appears in the journey.
Example value: {
EdgeUsagePattern Default edge usage pattern (7d23cf): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in :
first 10 vals [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],
last 10 vals [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],
}
Depends directly on:
through the following calculations:
You can also visit the link to Unitary hourly need for Default edge usage pattern’s full calculation graph.
total_hourly_need_across_usage_patterns
Total hourly demand on the component, summed across every EdgeUsagePattern after multiplying by the hourly count of edge devices in deployment.
Example value: 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in ²:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 499, 999, 1500],
last 10 vals [2500, 2500, 2500, 2500, 2500, 2000, 1500, 998, 498, 0]
Depends directly on:
through the following calculations:
You can also visit the link to Total hourly need across usage patterns’s full calculation graph.
fabrication_impact_repartition_weights
Weights used to attribute fabrication-phase emissions of upstream impact sources to each container of this object.
Example value: {
RecurrentEdgeWorkload edge workload (e31345): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in M:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.000999, 0.002, 0.003],
last 10 vals [0.005, 0.005, 0.005, 0.005, 0.005, 0.004, 0.003, 0.002, 0.000997, 0],
}
Depends directly on:
through the following calculations:
You can also visit the link to edge workload weight in impact repartition’s full calculation graph.
fabrication_impact_repartition_weight_sum
Sum of fabrication impact repartition weights, used as the denominator when normalising into per-container shares.
Example value: 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in M:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.000999, 0.002, 0.003],
last 10 vals [0.005, 0.005, 0.005, 0.005, 0.005, 0.004, 0.003, 0.002, 0.000997, 0]
Depends directly on:
through the following calculations:
You can also visit the link to Fabrication impact repartition weights sum’s full calculation graph.
fabrication_impact_repartition
Normalised share of fabrication-phase emissions that this object attributes to each container.
Example value: {
RecurrentEdgeWorkload edge workload (e31345): 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, 1],
}
Depends directly on:
through the following calculations:
You can also visit the link to fabrication impact attribution to edge workload’s full calculation graph.
usage_impact_repartition_weights
Weights used to attribute usage-phase emissions of upstream impact sources to each container of this object.
Example value: {
RecurrentEdgeWorkload edge workload (e31345): 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in ·g/kWh:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 84900, 170000, 255000],
last 10 vals [425000, 425000, 425000, 425000, 425000, 340000, 255000, 170000, 84700, 0],
}
Depends directly on:
through the following calculations:
You can also visit the link to edge workload weight in impact repartition’s full calculation graph.
usage_impact_repartition_weight_sum
Sum of usage impact repartition weights, used as the denominator when normalising into per-container shares.
Example value: 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in ·g/kWh:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 84900, 170000, 255000],
last 10 vals [425000, 425000, 425000, 425000, 425000, 340000, 255000, 170000, 84700, 0]
Depends directly on:
through the following calculations:
You can also visit the link to Usage impact repartition weights sum’s full calculation graph.
usage_impact_repartition
Normalised share of usage-phase emissions that this object attributes to each container.
Example value: {
RecurrentEdgeWorkload edge workload (e31345): 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, 1],
}
Depends directly on:
through the following calculations:
You can also visit the link to usage impact attribution to edge workload’s full calculation graph.