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RecurrentEdgeProcessStorageNeed

Internal RecurrentEdgeStorageNeed created automatically by a RecurrentEdgeProcess, mirroring its storage need on the parent EdgeComputer's storage.

Params

name

A human readable description of the object.

edge_component

Component on the parent EdgeComputer that this need targets.

An instance of EdgeStorage.

Calculated attributes

recurrent_need

Recurrent storage need, copied from the parent RecurrentEdgeProcess's storage profile.

Example value: 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]

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 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]

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 kB stored:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
last 10 vals [200, 200, 200, 200, 200, 200, 200, 200, 200, 200],
}

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.

cumulative_unitary_storage_need_per_usage_pattern

Hourly cumulative storage held by one edge device, integrating the net storage rate from the start of the modeling period.

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 GB stored:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
last 10 vals [21, 21, 21, 21, 21, 21, 21, 21, 21, 21],
}

Depends directly on:

through the following calculations:

You can also visit the link to Cumulative unitary storage need for Default edge usage pattern’s full calculation graph.

total_hourly_need_across_usage_patterns

Total hourly storage volume held across the deployed fleet, summing per-device cumulative storage weighted 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 TB stored:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
last 10 vals [105, 105, 105, 105, 105, 83.9, 62.9, 41.9, 20.9, 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: {
RecurrentEdgeProcess edge process (7067e2): 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 process 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: {
RecurrentEdgeProcess edge process (7067e2): 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 process’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: {
RecurrentEdgeProcess edge process (7067e2): 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 process 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: {
RecurrentEdgeProcess edge process (7067e2): 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 process’s full calculation graph.