RecurrentEdgeComponentNeed
A repeating week-long resource demand placed on one EdgeComponent (RAM, CPU, storage, or whole-device workload). The need pattern is replayed for the lifetime of every EdgeUsageJourney that includes it.
Params
name
A human readable description of the object.
edge_component
EdgeComponent on which the recurring need is placed. The need's unit must match what the component provides (RAM, compute, storage, or workload).
An instance of EdgeCPUComponent.
recurrent_need
Hourly resource consumption pattern over a typical week, starting Monday at midnight. The 168-hour pattern is repeated to cover the modeling period.
Recurrent need, in typical week of hourly timeseries data, starting on Monday at midnight. For example, 168 values in cpu core: 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]
Backwards links
Calculated attributes
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 cpu core:
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 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 cpu core:
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 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 ·cpu core:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 999, 2000, 3000],
last 10 vals [5000, 5000, 5000, 5000, 5000, 4000, 3000, 2000, 997, 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: {
RecurrentEdgeDeviceNeed custom edge device need (bf62c8): 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 custom edge device need 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: {
RecurrentEdgeDeviceNeed custom edge device need (bf62c8): 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 custom edge device need’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: {
RecurrentEdgeDeviceNeed custom edge device need (bf62c8): 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 custom edge device need 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: {
RecurrentEdgeDeviceNeed custom edge device need (bf62c8): 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 custom edge device need’s full calculation graph.