VideoStreamingJob
One streaming session of a given resolution and duration consumed against a VideoStreaming service. Bandwidth, CPU, and RAM are derived from the resolution, refresh rate, and the service-level cost coefficients.
Params
name
A human readable description of the object.
service
VideoStreaming service that hosts the stream.
An instance of VideoStreaming.
resolution
Display resolution as a label like "1080p (1920 x 1080)". The pixel count is parsed from the label and used to estimate the dynamic bitrate.
For example, 1080p (1920 x 1080).
video_duration
Duration of one streaming session, used as the request duration.
Unit: minute.
refresh_rate
Frames-per-second rate of the stream. Higher refresh rates increase the bitrate proportionally.
Unit: 1 / second.
data_stored
Net change in stored data per session. Usually 0 for streamed video.
Unit: megabyte_stored.
Backwards links
Calculated attributes
request_duration
Request duration of one streaming session, equal to the chosen video duration.
Example value: 20 min
Depends directly on:
through the following calculations:
You can also visit the link to Request duration’s full calculation graph.
dynamic_bitrate
Estimated bitrate of the stream, equal to the pixel count parsed from the resolution times bits-per-pixel times refresh rate.
Example value: 0.778 MB/s
Depends directly on:
through the following calculations:
You can also visit the link to Dynamic bitrate’s full calculation graph.
data_transferred
Data transferred per session, equal to the dynamic bitrate times the video duration.
Example value: 933 MB
Depends directly on:
through the following calculations:
You can also visit the link to Data transferred’s full calculation graph.
compute_needed
CPU consumed per session, equal to the service's per-bitrate CPU cost times the dynamic bitrate.
Example value: 0.00311 cpu core
Depends directly on:
through the following calculations:
You can also visit the link to CPU needed’s full calculation graph.
ram_needed
RAM consumed per session, equal to the service's per-user RAM buffer.
Example value: 50 MB ram
Depends directly on:
through the following calculations:
You can also visit the link to RAM needed’s full calculation graph.
hourly_occurrences_per_usage_pattern
Hourly count of job invocations broken down by usage pattern, derived from when each usage pattern's journeys start and at what point in the journey this job is triggered.
Example value: {
UsagePattern usage pattern (60c504): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [7, 2, 3, 1, 5, 7, 6, 4, 8, 5],
last 10 vals [6, 3, 3, 4, 1, 5, 2, 6, 3, 5],
}
Depends directly on:
through the following calculations:
You can also visit the link to Hourly occurrences in usage pattern’s full calculation graph.
hourly_avg_occurrences_per_usage_pattern
Hourly count of job invocations averaged with respect to job duration, so a job that runs longer than an hour contributes a fractional occurrence to several modeling buckets.
Example value: {
UsagePattern usage pattern (60c504): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [2.33, 0.667, 1, 0.333, 1.67, 2.33, 2, 1.33, 2.67, 1.67],
last 10 vals [2, 1, 1, 1.33, 0.333, 1.67, 0.667, 2, 1, 1.67],
}
Depends directly on:
through the following calculations:
You can also visit the link to Average hourly occurrences in usage pattern’s full calculation graph.
hourly_data_transferred_per_usage_pattern
Hourly volume of data transferred over the network by this job, broken down by usage pattern.
Example value: {
UsagePattern usage pattern (60c504): 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 [6.53, 1.87, 2.8, 0.933, 4.67, 6.53, 5.6, 3.73, 7.46, 4.67],
last 10 vals [5.6, 2.8, 2.8, 3.73, 0.933, 4.67, 1.87, 5.6, 2.8, 4.67],
}
Depends directly on:
through the following calculations:
You can also visit the link to Hourly data transferred in usage pattern’s full calculation graph.
hourly_data_stored_per_usage_pattern
Hourly net change in storage volume caused by this job, broken down by usage pattern.
Example value: {
UsagePattern usage pattern (60c504): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in B stored:
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 in usage pattern’s full calculation graph.
hourly_avg_occurrences_across_usage_patterns
Total hourly count of duration-averaged job invocations summed over every usage pattern.
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 [2.33, 0.667, 1, 0.333, 1.67, 2.33, 2, 1.33, 2.67, 1.67],
last 10 vals [2, 1, 1, 1.33, 0.333, 1.67, 0.667, 2, 1, 1.67]
Depends directly on:
through the following calculations:
You can also visit the link to Hourly average occurrences across usage patterns’s full calculation graph.
hourly_data_transferred_across_usage_patterns
Total hourly volume of data transferred over the network by this job, summed over every usage pattern.
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 [6.53, 1.87, 2.8, 0.933, 4.67, 6.53, 5.6, 3.73, 7.46, 4.67],
last 10 vals [5.6, 2.8, 2.8, 3.73, 0.933, 4.67, 1.87, 5.6, 2.8, 4.67]
Depends directly on:
through the following calculations:
You can also visit the link to Hourly data transferred across usage patterns’s full calculation graph.
hourly_data_stored_across_usage_patterns
Total hourly net change in storage volume caused by this job, summed over every usage pattern.
Example value: 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in B stored:
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 across usage patterns’s full calculation graph.
fabrication_impact_repartition_weights
Weights used to attribute fabrication-phase emissions of upstream impact sources to each container of this object.
Example value: {
UsageJourneyStep 20 min streaming (264451): 26298 values from 2025-01-01 00:00:00+00:00 to 2028-01-01 18:00:00+00:00 in :
first 10 vals [2.33, 0.667, 1, 0.333, 1.67, 2.33, 2, 1.33, 2.67, 1.67],
last 10 vals [2, 1, 1, 1.33, 0.333, 1.67, 0.667, 2, 1, 1.67],
}
Depends directly on:
through the following calculations:
You can also visit the link to 20 min streaming weight in impact repartition’s full calculation graph.
fabrication_impact_repartition_weight_sum
Sum of fabrication impact repartition weights, used as the denominator when normalising into per-container shares.
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 [2.33, 0.667, 1, 0.333, 1.67, 2.33, 2, 1.33, 2.67, 1.67],
last 10 vals [2, 1, 1, 1.33, 0.333, 1.67, 0.667, 2, 1, 1.67]
Depends directly on:
through the following calculations:
You can also visit the link to Fabrication impact repartition weights sum’s full calculation graph.
fabrication_impact_repartition
Normalised share of fabrication-phase emissions that this object attributes to each container.
Example value: {
UsageJourneyStep 20 min streaming (264451): 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 fabrication impact attribution to 20 min streaming’s full calculation graph.
usage_impact_repartition_weights
Weights used to attribute usage-phase emissions of upstream impact sources to each container of this object.
Example value: {
UsageJourneyStep 20 min streaming (264451): 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 [198, 56.7, 85, 28.3, 142, 198, 170, 113, 227, 142],
last 10 vals [170, 85, 85, 113, 28.3, 142, 56.7, 170, 85, 142],
}
Depends directly on:
through the following calculations:
You can also visit the link to 20 min streaming weight in impact repartition’s full calculation graph.
usage_impact_repartition_weight_sum
Sum of usage impact repartition weights, used as the denominator when normalising into per-container shares.
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 [198, 56.7, 85, 28.3, 142, 198, 170, 113, 227, 142],
last 10 vals [170, 85, 85, 113, 28.3, 142, 56.7, 170, 85, 142]
Depends directly on:
through the following calculations:
You can also visit the link to Usage impact repartition weights sum’s full calculation graph.
usage_impact_repartition
Normalised share of usage-phase emissions that this object attributes to each container.
Example value: {
UsageJourneyStep 20 min streaming (264451): 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 usage impact attribution to 20 min streaming’s full calculation graph.