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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 (d71fc8): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in :
first 10 vals [4, 16, 18, 16, 6, 2, 14, 12, 18, 4],
last 10 vals [6, 16, 18, 4, 16, 12, 10, 4, 12, 6],
EdgeUsagePattern Default edge usage pattern (eef937): 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 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 (d71fc8): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in :
first 10 vals [0.00111, 0.00444, 0.005, 0.00444, 0.00167, 0.000556, 0.00389, 0.00333, 0.005, 0.00111],
last 10 vals [0.00167, 0.00444, 0.005, 0.00111, 0.00444, 0.00333, 0.00278, 0.00111, 0.00333, 0.00167],
EdgeUsagePattern Default edge usage pattern (eef937): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in k:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.000278, 0.000555],
last 10 vals [0.00139, 0.00139, 0.00139, 0.00139, 0.00139, 0.00139, 0.00111, 0.000832, 0.000555, 0.000277],
}

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 (d71fc8): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in M:
first 10 vals [4.8, 19.2, 21.6, 19.2, 7.2, 2.4, 16.8, 14.4, 21.6, 4.8],
last 10 vals [7.2, 19.2, 21.6, 4.8, 19.2, 14.4, 12, 4.8, 14.4, 7.2],
EdgeUsagePattern Default edge usage pattern (eef937): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in B:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 1.2, 2.4],
last 10 vals [6, 6, 6, 6, 6, 6, 4.8, 3.6, 2.4, 1.2],
}

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 (d71fc8): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in M:
first 10 vals [3.2, 12.8, 14.4, 12.8, 4.8, 1.6, 11.2, 9.6, 14.4, 3.2],
last 10 vals [4.8, 12.8, 14.4, 3.2, 12.8, 9.6, 8, 3.2, 9.6, 4.8],
EdgeUsagePattern Default edge usage pattern (eef937): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in B:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.799, 1.6],
last 10 vals [4, 4, 4, 4, 4, 4, 3.2, 2.4, 1.6, 0.798],
}

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 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in k:
first 10 vals [0.00000111, 0.00000444, 0.000005, 0.00000444, 0.00000167, 0.000000556, 0.00000389, 0.00000333, 0.000283, 0.000556],
last 10 vals [0.00139, 0.00139, 0.00139, 0.00139, 0.00139, 0.00139, 0.00111, 0.000832, 0.000555, 0.000277]

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 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in B:
first 10 vals [0.0048, 0.0192, 0.0216, 0.0192, 0.0072, 0.0024, 0.0168, 0.0144, 1.22, 2.4],
last 10 vals [6, 6, 6, 6, 6, 6, 4.8, 3.6, 2.4, 1.2]

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 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in B:
first 10 vals [0.0032, 0.0128, 0.0144, 0.0128, 0.0048, 0.0016, 0.0112, 0.0096, 0.814, 1.6],
last 10 vals [4, 4, 4, 4, 4, 4, 3.2, 2.4, 1.6, 0.798]

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 (1417a0): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in :
first 10 vals [1.33, 5.33, 6, 5.33, 2, 0.667, 4.67, 4, 6, 1.33],
last 10 vals [2, 5.33, 6, 1.33, 5.33, 4, 3.33, 1.33, 4, 2],
RecurrentServerNeed Server need (858fe7): 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 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 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in M:
first 10 vals [0.00000133, 0.00000533, 0.000006, 0.00000533, 0.000002, 0.000000667, 0.00000467, 0.000004, 0.00101, 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 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 (1417a0): 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, 0.00597, 0.000667],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
RecurrentServerNeed Server need (858fe7): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in :
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.994, 0.999],
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 (1417a0): 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in ·g/kWh:
first 10 vals [113, 453, 510, 453, 170, 56.7, 397, 340, 510, 113],
last 10 vals [170, 453, 510, 113, 453, 340, 283, 113, 340, 170],
RecurrentServerNeed Server need (858fe7): 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 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 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in ·g/kWh:
first 10 vals [113, 453, 510, 453, 170, 56.7, 397, 340, 85500, 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 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 (1417a0): 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, 0.00597, 0.000667],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
RecurrentServerNeed Server need (858fe7): 105192 values from 2024-12-31 23:00:00+00:00 to 2036-12-31 23:00:00+00:00 in :
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0.994, 0.999],
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.