RecurrentEdgeProcess
A typical-week resource demand placed on an EdgeComputer, decomposed into separate hourly RAM, compute, and storage curves. Auto-creates the matching component-level needs at construction time.
Usage from Python
Building a RecurrentEdgeProcess automatically creates the matching RecurrentEdgeProcessRAMNeed, RecurrentEdgeProcessCPUNeed, and RecurrentEdgeProcessStorageNeed. Updating the linked edge computer rewires those component needs to the new computer's components.
Params
name
A human readable description of the object.
edge_device
EdgeComputer that runs the process.
An instance of EdgeComputer.
recurrent_compute_needed
Hourly compute usage over a typical week.
Recurrent compute needed, 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]
recurrent_ram_needed
Hourly RAM usage over a typical week.
Recurrent ram needed, in typical week of hourly timeseries data, starting on Monday at midnight. For example, 168 values in GB ram: first 10 vals [2, 2, 2, 2, 2, 2, 2, 2, 2, 2], last 10 vals [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
recurrent_storage_needed
Hourly net storage rate over a typical week (positive = writes, negative = deletes).
Recurrent storage needed, in typical week of hourly timeseries data, starting on Monday at midnight. For example, 168 values in kB stored: first 10 vals [200, 200, 200, 200, 200, 200, 200, 200, 200, 200], last 10 vals [200, 200, 200, 200, 200, 200, 200, 200, 200, 200]
Backwards links
Calculated attributes
fabrication_impact_repartition_weights
Weights used to attribute fabrication-phase emissions of upstream impact sources to each container of this object.
Example value: {
EdgeFunction edge function (db9c30): 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 function 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: {
EdgeFunction edge function (db9c30): 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 function’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: {
EdgeFunction edge function (db9c30): 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 function 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: {
EdgeFunction edge function (db9c30): 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 function’s full calculation graph.