RecurrentEdgeStorageNeed
A RecurrentEdgeComponentNeed targeting an EdgeStorage component. Tracks the cumulative storage consumed by writes and freed by negative values across the typical week.
Common pitfalls
Values represent net storage rate (positive = writes, negative = deletes). The cumulative integral must stay non-negative within each week, otherwise the cumulative storage need would go below zero and break downstream sizing.
Params
name
A human readable description of the object.
edge_component
EdgeStorage component holding the data.
An instance of EdgeStorage.
recurrent_need
Hourly net storage rate over a typical week (positive for writes, negative for deletes). Cumulated to derive the cumulative volume actually held.
Recurrent need, in typical week of hourly timeseries data, starting on Monday at midnight. For example, 168 values in MB stored: first 10 vals [50, 50, 50, 50, 50, 50, 50, 50, 50, 50], last 10 vals [-50, -50, -50, -50, -50, -50, -50, -50, -50, -50]
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 MB stored:
first 10 vals [50, 50, 50, 50, 50, 50, 50, 50, 50, 50],
last 10 vals [-50, -50, -50, -50, -50, -50, -50, -50, -50, -50]
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 MB stored:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
last 10 vals [50, 50, 50, 50, 50, 50, 50, 50, 50, 50],
}
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 [3.15, 3.2, 3.25, 3.3, 3.35, 3.4, 3.45, 3.5, 3.55, 3.6],
}
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 [15.7, 16, 16.2, 16.5, 16.7, 13.6, 10.3, 6.99, 3.54, 0]
Depends directly on:
- Cumulative unitary storage need for Default edge usage pattern
- Hourly nb of edge usage journeys in parallel
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.