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Job

Params

name

A human readable description of the object.

server

An instance of Server.

data_transferred

Sum of all data uploads and downloads for request manually defined job in kilobyte.

data_stored

Data stored by request manually defined job in kilobyte.

request_duration

Request duration of manually defined job in second.

compute_needed

Cpu cores needed on server server to process manually defined job in cpu_core.

ram_needed

Ram needed on server server to process manually defined job in megabyte_ram.

Calculated attributes

hourly_occurrences_per_usage_pattern

Dictionary with UsagePattern as keys and Hourly manually defined job occurrences in usagepattern usage pattern as values, in occurrence.

Example value: {
UsagePattern usage pattern (7703de): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [12, 12, 2, 2, 14, 14, 2, 14, 18, 2],
last 10 vals [14, 2, 6, 2, 14, 10, 12, 6, 6, 8],
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 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 Hourly Manually defined job occurrences in UsagePattern usage pattern’s full calculation graph.

hourly_avg_occurrences_per_usage_pattern

Dictionary with UsagePattern as keys and Average hourly manually defined job occurrences in usage pattern as values, in concurrent.

Example value: {
UsagePattern usage pattern (7703de): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [0.00333, 0.00333, 0.000556, 0.000556, 0.00389, 0.00389, 0.000556, 0.00389, 0.005, 0.000556],
last 10 vals [0.00389, 0.000556, 0.00167, 0.000556, 0.00389, 0.00278, 0.00333, 0.00167, 0.00167, 0.00222],
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 k:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.000277, 0.000555, 0.000833],
last 10 vals [0.00139, 0.00139, 0.00139, 0.00139, 0.00139, 0.00111, 0.000832, 0.000555, 0.000277, 0],
}

Depends directly on:

through the following calculations:

You can also visit the link to Average hourly Manually defined job occurrences in usage pattern’s full calculation graph.

hourly_data_transferred_per_usage_pattern

Dictionary with UsagePattern as keys and Hourly data transferred for manually defined job in usage pattern as values, in megabyte.

Example value: {
UsagePattern usage pattern (7703de): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in MB:
first 10 vals [1.8, 1.8, 0.3, 0.3, 2.1, 2.1, 0.3, 2.1, 2.7, 0.3],
last 10 vals [2.1, 0.3, 0.9, 0.3, 2.1, 1.5, 1.8, 0.9, 0.9, 1.2],
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 TB:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.00015, 0.0003, 0.00045],
last 10 vals [0.00075, 0.000749, 0.000749, 0.00075, 0.00075, 0.000599, 0.000449, 0.000299, 0.00015, 0],
}

Depends directly on:

through the following calculations:

You can also visit the link to Hourly data transferred for Manually defined job in usage pattern’s full calculation graph.

hourly_data_stored_per_usage_pattern

Dictionary with UsagePattern as keys and Hourly data stored for manually defined job in usage pattern as values, in megabyte.

Example value: {
UsagePattern usage pattern (7703de): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in MB:
first 10 vals [1.2, 1.2, 0.2, 0.2, 1.4, 1.4, 0.2, 1.4, 1.8, 0.2],
last 10 vals [1.4, 0.2, 0.6, 0.2, 1.4, 1, 1.2, 0.6, 0.6, 0.8],
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 TB:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0.0000999, 0.0002, 0.0003],
last 10 vals [0.0005, 0.0005, 0.0005, 0.0005, 0.0005, 0.0004, 0.0003, 0.0002, 0.0000997, 0],
}

Depends directly on:

through the following calculations:

You can also visit the link to Hourly data stored for Manually defined job in usage pattern’s full calculation graph.

hourly_avg_occurrences_across_usage_patterns

Hourly manually defined job average occurrences across usage patterns 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 k:
first 10 vals [0.00000333, 0.00000333, 0.000000556, 0.000000556, 0.00000389, 0.00000389, 0.000000556, 0.000281, 0.00056, 0.000833],
last 10 vals [0.00139, 0.00139, 0.00139, 0.00139, 0.00139, 0.00111, 0.000832, 0.000555, 0.000277, 0]

Depends directly on:

through the following calculations:

You can also visit the link to Hourly Manually defined job average occurrences across usage patterns’s full calculation graph.

hourly_data_transferred_across_usage_patterns

Hourly manually defined job data transferred across usage patterns in megabyte.

Example value: 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in TB:
first 10 vals [0.0000018, 0.0000018, 0.0000003, 0.0000003, 0.0000021, 0.0000021, 0.0000003, 0.000152, 0.000302, 0.00045],
last 10 vals [0.00075, 0.000749, 0.000749, 0.00075, 0.00075, 0.000599, 0.000449, 0.000299, 0.00015, 0]

Depends directly on:

through the following calculations:

You can also visit the link to Hourly Manually defined job data transferred across usage patterns’s full calculation graph.

hourly_data_stored_across_usage_patterns

Hourly manually defined job data stored across usage patterns in megabyte.

Example value: 105192 values from 2025-01-01 00:00:00+00:00 to 2037-01-01 00:00:00+00:00 in TB:
first 10 vals [0.0000012, 0.0000012, 0.0000002, 0.0000002, 0.0000014, 0.0000014, 0.0000002, 0.000101, 0.000202, 0.0003],
last 10 vals [0.0005, 0.0005, 0.0005, 0.0005, 0.0005, 0.0004, 0.0003, 0.0002, 0.0000997, 0]

Depends directly on:

through the following calculations:

You can also visit the link to Hourly Manually defined job data stored across usage patterns’s full calculation graph.

fabrication_impact_repartition_weights

Dictionary with UsageJourneyStep as keys and 20 min streaming weight in manually defined job impact repartition as values, in concurrent.

Example value: {
UsageJourneyStep 20 min streaming (624221): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [4, 4, 0.667, 0.667, 4.67, 4.67, 0.667, 4.67, 6, 0.667],
last 10 vals [4.67, 0.667, 2, 0.667, 4.67, 3.33, 4, 2, 2, 2.67],
RecurrentServerNeed Server need (9b0e65): 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 20 min streaming weight in Manually defined job impact repartition’s full calculation graph.

fabrication_impact_repartition_weight_sum

Sum of manually defined job 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 [0.000004, 0.000004, 0.000000667, 0.000000667, 0.00000467, 0.00000467, 0.000000667, 0.001, 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 Manually defined job fabrication impact repartition weights’s full calculation graph.

fabrication_impact_repartition

Dictionary with UsageJourneyStep as keys and Manually defined job fabrication impact attribution to 20 min streaming as values, in concurrent.

Example value: {
UsageJourneyStep 20 min streaming (624221): 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, 0.00465, 0.00299, 0.000222],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
RecurrentServerNeed Server need (9b0e65): 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, 0, 0, 0, 0, 0, 0, 0.995, 0.997, 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 Manually defined job fabrication impact attribution to 20 min streaming’s full calculation graph.

usage_impact_repartition_weights

Dictionary with UsageJourneyStep as keys and 20 min streaming weight in manually defined job impact repartition as values, in concurrent * gram / kilowatt_hour.

Example value: {
UsageJourneyStep 20 min streaming (624221): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in ·g/kWh:
first 10 vals [340, 340, 56.7, 56.7, 397, 397, 56.7, 397, 510, 56.7],
last 10 vals [397, 56.7, 170, 56.7, 397, 283, 340, 170, 170, 227],
RecurrentServerNeed Server need (9b0e65): 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 20 min streaming weight in Manually defined job impact repartition’s full calculation graph.

usage_impact_repartition_weight_sum

Sum of manually defined job 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 [340, 340, 56.7, 56.7, 397, 397, 56.7, 85300, 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 Manually defined job usage impact repartition weights’s full calculation graph.

usage_impact_repartition

Dictionary with UsageJourneyStep as keys and Manually defined job usage impact attribution to 20 min streaming as values, in concurrent.

Example value: {
UsageJourneyStep 20 min streaming (624221): 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, 0.00465, 0.00299, 0.000222],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
RecurrentServerNeed Server need (9b0e65): 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, 0, 0, 0, 0, 0, 0, 0.995, 0.997, 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 Manually defined job usage impact attribution to 20 min streaming’s full calculation graph.