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VideoStreamingJob

Params

name

A human readable description of the object.

service

An instance of VideoStreaming.

resolution

description to be done

video_duration

Video streaming job video duration from e-footprint hypothesis in minute.

refresh_rate

Video streaming job frames per second from e-footprint hypothesis in 1 / second.

data_stored

Data stored by request video streaming job from e-footprint hypothesis in megabyte.

Calculated attributes

request_duration

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

Example value: 20.0 minute

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: 0.78 megabyte / second

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: 0.93 gigabyte

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: 0.0 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: 50.0 megabyte_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 usage pattern as values, in occurrence.

Example value: {
35f108: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in occurrence:
first 10 vals [2.0, 6.0, 8.0, 1.0, 6.0, 5.0, 3.0, 5.0, 7.0, 5.0],
last 10 vals [2.0, 1.0, 6.0, 1.0, 9.0, 8.0, 3.0, 3.0, 1.0, 3.0],
}

Depends directly on:

through the following calculations:

You can also visit the link to Hourly Video streaming job occurrences in 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: {
35f108: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in concurrent:
first 10 vals [0.67, 2.0, 2.67, 0.33, 2.0, 1.67, 1.0, 1.67, 2.33, 1.67],
last 10 vals [0.67, 0.33, 2.0, 0.33, 3.0, 2.67, 1.0, 1.0, 0.33, 1.0],
}

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 concurrent * gigabyte * hour / minute.

Example value: {
35f108: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in concurrent * GB * h / min:
first 10 vals [0.03, 0.09, 0.12, 0.02, 0.09, 0.08, 0.05, 0.08, 0.11, 0.08],
last 10 vals [0.03, 0.02, 0.09, 0.02, 0.14, 0.12, 0.05, 0.05, 0.02, 0.05],
}

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 concurrent * hour * megabyte / minute.

Example value: {
35f108: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in concurrent * h * MB / min:
first 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
last 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 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 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in concurrent:
first 10 vals [0.67, 2.0, 2.67, 0.33, 2.0, 1.67, 1.0, 1.67, 2.33, 1.67],
last 10 vals [0.67, 0.33, 2.0, 0.33, 3.0, 2.67, 1.0, 1.0, 0.33, 1.0]

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 concurrent * gigabyte * hour / minute.

Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in concurrent * GB * h / min:
first 10 vals [0.03, 0.09, 0.12, 0.02, 0.09, 0.08, 0.05, 0.08, 0.11, 0.08],
last 10 vals [0.03, 0.02, 0.09, 0.02, 0.14, 0.12, 0.05, 0.05, 0.02, 0.05]

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 concurrent * hour * megabyte / minute.

Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in concurrent * h * MB / min:
first 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
last 10 vals [0.0, 0.0, 0.0, 0.0, 0.0, 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.