Skip to content

RecurrentEdgeWorkload

Params

name

A human readable description of the object.

edge_device

An instance of EdgeAppliance.

recurrent_workload

Recurrent workload for edge workload, in typical week of hourly timeseries data, starting on Monday at midnight.

For example, 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]

Calculated attributes

fabrication_impact_repartition_weights

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

Example value: {
EdgeFunction edge function (48c43e): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in M:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.000999, 0.002],
last 10 vals [0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.004, 0.003, 0.002, 0.000997],
}

Depends directly on:

through the following calculations:

You can also visit the link to edge function weight in edge workload impact repartition’s full calculation graph.

fabrication_impact_repartition_weight_sum

Sum of edge workload fabrication impact repartition weights in concurrent.

Example value: 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in M:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.000999, 0.002],
last 10 vals [0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.004, 0.003, 0.002, 0.000997]

Depends directly on:

through the following calculations:

You can also visit the link to Sum of edge workload fabrication impact repartition weights’s full calculation graph.

fabrication_impact_repartition

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

Example value: {
EdgeFunction edge function (48c43e): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+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 fabrication impact attribution to edge function’s full calculation graph.

usage_impact_repartition_weights

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

Example value: {
EdgeFunction edge function (48c43e): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in ·g/kWh:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 84900, 170000],
last 10 vals [425000, 425000, 425000, 425000, 425000, 425000, 340000, 255000, 170000, 84700],
}

Depends directly on:

through the following calculations:

You can also visit the link to edge function weight in edge workload impact repartition’s full calculation graph.

usage_impact_repartition_weight_sum

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

Example value: 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in ·g/kWh:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 84900, 170000],
last 10 vals [425000, 425000, 425000, 425000, 425000, 425000, 340000, 255000, 170000, 84700]

Depends directly on:

through the following calculations:

You can also visit the link to Sum of edge workload usage impact repartition weights’s full calculation graph.

usage_impact_repartition

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

Example value: {
EdgeFunction edge function (48c43e): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+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 usage impact attribution to edge function’s full calculation graph.