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RecurrentEdgeWorkloadNeed

Params

name

A human readable description of the object.

edge_component

An instance of EdgeApplianceComponent.

Calculated attributes

recurrent_need

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 edge workload workload need recurrent need’s full calculation graph.

recurrent_need_validation

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 of edge workload workload need’s full calculation graph.

unitary_hourly_need_per_usage_pattern

Dictionary with EdgeUsagePattern as keys and Edge workload workload need unitary hourly need for default edge usage pattern as values, in concurrent.

Example value: {
EdgeUsagePattern Default edge usage pattern (f80985): 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 edge workload workload need unitary hourly need for Default edge usage pattern’s full calculation graph.

total_hourly_need_across_usage_patterns

Edge workload workload need total hourly need across usage patterns in concurrent ** 2.

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 edge workload workload need total hourly need across usage patterns’s full calculation graph.

fabrication_impact_repartition_weights

Dictionary with RecurrentEdgeWorkload as keys and Edge workload weight in edge workload workload need impact repartition as values, in concurrent.

Example value: {
RecurrentEdgeWorkload edge workload (0f121d): 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 edge workload workload need impact repartition’s full calculation graph.

fabrication_impact_repartition_weight_sum

Sum of edge workload workload need fabrication impact repartition weights in concurrent.

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 Sum of edge workload workload need fabrication impact repartition weights’s full calculation graph.

fabrication_impact_repartition

Dictionary with RecurrentEdgeWorkload as keys and Edge workload workload need fabrication impact attribution to edge workload as values, in concurrent.

Example value: {
RecurrentEdgeWorkload edge workload (0f121d): 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 edge workload workload need fabrication impact attribution to edge workload’s full calculation graph.

usage_impact_repartition_weights

Dictionary with RecurrentEdgeWorkload as keys and Edge workload weight in edge workload workload need impact repartition as values, in concurrent * gram / kilowatt_hour.

Example value: {
RecurrentEdgeWorkload edge workload (0f121d): 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 edge workload workload need impact repartition’s full calculation graph.

usage_impact_repartition_weight_sum

Sum of edge workload workload need usage impact repartition weights in concurrent * gram / kilowatt_hour.

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 Sum of edge workload workload need usage impact repartition weights’s full calculation graph.

usage_impact_repartition

Dictionary with RecurrentEdgeWorkload as keys and Edge workload workload need usage impact attribution to edge workload as values, in concurrent.

Example value: {
RecurrentEdgeWorkload edge workload (0f121d): 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 edge workload workload need usage impact attribution to edge workload’s full calculation graph.