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VideoStreamingJob

Params

name

A human readable description of the object.

service

An instance of VideoStreaming.

resolution

Video streaming job resolution.

For example, 1080p (1920 x 1080).

video_duration

Video streaming job video duration in minute.

refresh_rate

Video streaming job frames per second in 1 / second.

data_stored

Data stored by request video streaming job in megabyte.

Calculated attributes

request_duration

ExplainableQuantity in minute, representing the Video streaming job request duration.

Example value: 20 min

Depends directly on:

through the following calculations:

You can also visit the link to Video streaming job request duration’s full calculation graph.

dynamic_bitrate

ExplainableQuantity in megabyte / second, representing the Video streaming job dynamic bitrate.

Example value: 0.778 MB/s

Depends directly on:

through the following calculations:

You can also visit the link to Video streaming job dynamic bitrate’s full calculation graph.

data_transferred

ExplainableQuantity in gigabyte, representing the Video streaming job data transferred.

Example value: 933 MB

Depends directly on:

through the following calculations:

You can also visit the link to Video streaming job data transferred’s full calculation graph.

compute_needed

ExplainableQuantity in cpu_core, representing the Video streaming job cpu needed.

Example value: 0.00311 cpu_core

Depends directly on:

through the following calculations:

You can also visit the link to Video streaming job CPU needed’s full calculation graph.

ram_needed

ExplainableQuantity in megabyte_ram, representing the Video streaming job ram needed.

Example value: 50 MB_ram

Depends directly on:

through the following calculations:

You can also visit the link to Video streaming job RAM needed’s full calculation graph.

hourly_occurrences_per_usage_pattern

Dictionary with UsagePattern as keys and Hourly video streaming 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 [6, 6, 1, 1, 7, 7, 1, 7, 9, 1],
last 10 vals [7, 1, 3, 1, 7, 5, 6, 3, 3, 4],
}

Depends directly on:

through the following calculations:

You can also visit the link to Hourly Video streaming job occurrences in UsagePattern usage pattern’s full calculation graph.

hourly_avg_occurrences_per_usage_pattern

Dictionary with UsagePattern as keys and Average hourly video streaming 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 [2, 2, 0.333, 0.333, 2.33, 2.33, 0.333, 2.33, 3, 0.333],
last 10 vals [2.33, 0.333, 1, 0.333, 2.33, 1.67, 2, 1, 1, 1.33],
}

Depends directly on:

through the following calculations:

You can also visit the link to Average hourly Video streaming 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 video streaming 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 GB:
first 10 vals [5.6, 5.6, 0.933, 0.933, 6.53, 6.53, 0.933, 6.53, 8.4, 0.933],
last 10 vals [6.53, 0.933, 2.8, 0.933, 6.53, 4.67, 5.6, 2.8, 2.8, 3.73],
}

Depends directly on:

through the following calculations:

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

hourly_data_stored_per_usage_pattern

Dictionary with UsagePattern as keys and Hourly data stored for video streaming 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 B:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
}

Depends directly on:

through the following calculations:

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

hourly_avg_occurrences_across_usage_patterns

Hourly video streaming job average occurrences across usage patterns in concurrent.

Example value: 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [2, 2, 0.333, 0.333, 2.33, 2.33, 0.333, 2.33, 3, 0.333],
last 10 vals [2.33, 0.333, 1, 0.333, 2.33, 1.67, 2, 1, 1, 1.33]

Depends directly on:

through the following calculations:

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

hourly_data_transferred_across_usage_patterns

Hourly video streaming job data transferred across usage patterns in megabyte.

Example value: 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in GB:
first 10 vals [5.6, 5.6, 0.933, 0.933, 6.53, 6.53, 0.933, 6.53, 8.4, 0.933],
last 10 vals [6.53, 0.933, 2.8, 0.933, 6.53, 4.67, 5.6, 2.8, 2.8, 3.73]

Depends directly on:

through the following calculations:

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

hourly_data_stored_across_usage_patterns

Hourly video streaming job data stored across usage patterns in megabyte.

Example value: 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in B:
first 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
last 10 vals [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

Depends directly on:

through the following calculations:

You can also visit the link to Hourly Video streaming 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 video streaming 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 [2, 2, 0.333, 0.333, 2.33, 2.33, 0.333, 2.33, 3, 0.333],
last 10 vals [2.33, 0.333, 1, 0.333, 2.33, 1.67, 2, 1, 1, 1.33],
}

Depends directly on:

through the following calculations:

You can also visit the link to 20 min streaming weight in Video streaming job impact repartition’s full calculation graph.

fabrication_impact_repartition_weight_sum

Sum of video streaming job fabrication impact repartition weights in concurrent.

Example value: 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [2, 2, 0.333, 0.333, 2.33, 2.33, 0.333, 2.33, 3, 0.333],
last 10 vals [2.33, 0.333, 1, 0.333, 2.33, 1.67, 2, 1, 1, 1.33]

Depends directly on:

through the following calculations:

You can also visit the link to Sum of Video streaming job fabrication impact repartition weights’s full calculation graph.

fabrication_impact_repartition

Dictionary with UsageJourneyStep as keys and Video streaming job fabrication impact attribution to 20 min streaming 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 [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 Video streaming 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 video streaming 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 [170, 170, 28.3, 28.3, 198, 198, 28.3, 198, 255, 28.3],
last 10 vals [198, 28.3, 85, 28.3, 198, 142, 170, 85, 85, 113],
}

Depends directly on:

through the following calculations:

You can also visit the link to 20 min streaming weight in Video streaming job impact repartition’s full calculation graph.

usage_impact_repartition_weight_sum

Sum of video streaming job usage impact repartition weights in concurrent * gram / kilowatt_hour.

Example value: 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 [170, 170, 28.3, 28.3, 198, 198, 28.3, 198, 255, 28.3],
last 10 vals [198, 28.3, 85, 28.3, 198, 142, 170, 85, 85, 113]

Depends directly on:

through the following calculations:

You can also visit the link to Sum of Video streaming job usage impact repartition weights’s full calculation graph.

usage_impact_repartition

Dictionary with UsageJourneyStep as keys and Video streaming job usage impact attribution to 20 min streaming 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 [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 Video streaming job usage impact attribution to 20 min streaming’s full calculation graph.