Job
Params
name
A human readable description of the object.
server
An instance of Autoscaling.
data_upload
data upload of request streaming in megabyte.
data_download
data download of request streaming in megabyte.
data_stored
data stored by request streaming in megabyte.
request_duration
request duration of streaming to server in minute.
cpu_needed
cpu needed on server server to process streaming in core.
ram_needed
ram needed on server server to process streaming in megabyte.
job_type
description to be done
description
description to be done
Backwards links
Calculated attributes
hourly_occurrences_per_usage_pattern
Dictionary with UsagePattern as keys and hourly streaming occurrences in usage pattern as values, in dimensionless.
Example value: {
id-3275fb-usage-pattern: 26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in dimensionless:
first 10 vals [4, 5, 7, 3, 7, 9, 9, 3, 4, 4],
last 10 vals [2, 5, 8, 9, 8, 1, 6, 9, 6, 7],
}
Depends directly on:
through the following calculations:
You can also visit the link to Hourly streaming occurrences in usage pattern’s full calculation graph.
hourly_avg_occurrences_per_usage_pattern
Dictionary with UsagePattern as keys and average hourly streaming occurrences in usage pattern as values, in dimensionless.
Example value: {
id-3275fb-usage-pattern: 26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in dimensionless:
first 10 vals [0.27, 0.33, 0.47, 0.2, 0.47, 0.6, 0.6, 0.2, 0.27, 0.27],
last 10 vals [0.13, 0.33, 0.53, 0.6, 0.53, 0.07, 0.4, 0.6, 0.4, 0.47],
}
Depends directly on:
through the following calculations:
You can also visit the link to Average hourly streaming occurrences in usage pattern’s full calculation graph.
hourly_data_upload_per_usage_pattern
Dictionary with UsagePattern as keys and hourly data upload for streaming in usage pattern as values, in megabyte.
Example value: {
id-3275fb-usage-pattern: 26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in MB:
first 10 vals [0.2, 0.25, 0.35, 0.15, 0.35, 0.45, 0.45, 0.15, 0.2, 0.2],
last 10 vals [0.1, 0.25, 0.4, 0.45, 0.4, 0.05, 0.3, 0.45, 0.3, 0.35],
}
Depends directly on:
through the following calculations:
You can also visit the link to Hourly data upload for streaming in usage pattern’s full calculation graph.
hourly_data_download_per_usage_pattern
Dictionary with UsagePattern as keys and hourly data download for streaming in usage pattern as values, in megabyte.
Example value: {
id-3275fb-usage-pattern: 26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in MB:
first 10 vals [3200.0, 4000.0, 5600.0, 2400.0, 5600.0, 7200.0, 7200.0, 2400.0, 3200.0, 3200.0],
last 10 vals [1600.0, 4000.0, 6400.0, 7200.0, 6400.0, 800.0, 4800.0, 7200.0, 4800.0, 5600.0],
}
Depends directly on:
through the following calculations:
You can also visit the link to Hourly data download for streaming in usage pattern’s full calculation graph.
hourly_data_stored_per_usage_pattern
Dictionary with UsagePattern as keys and hourly data stored for streaming in usage pattern as values, in megabyte.
Example value: {
id-3275fb-usage-pattern: 26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in MB:
first 10 vals [0.2, 0.25, 0.35, 0.15, 0.35, 0.45, 0.45, 0.15, 0.2, 0.2],
last 10 vals [0.1, 0.25, 0.4, 0.45, 0.4, 0.05, 0.3, 0.45, 0.3, 0.35],
}
Depends directly on:
through the following calculations:
You can also visit the link to Hourly data stored for streaming in usage pattern’s full calculation graph.
hourly_occurrences_across_usage_patterns
hourly streaming occurrences across usage patterns in dimensionless.
Example value: 26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in dimensionless:
first 10 vals [4, 5, 7, 3, 7, 9, 9, 3, 4, 4],
last 10 vals [2, 5, 8, 9, 8, 1, 6, 9, 6, 7]
Depends directly on:
through the following calculations:
You can also visit the link to Hourly streaming occurrences across usage patterns’s full calculation graph.
hourly_avg_occurrences_across_usage_patterns
hourly streaming average occurrences across usage patterns in dimensionless.
Example value: 26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in dimensionless:
first 10 vals [0.27, 0.33, 0.47, 0.2, 0.47, 0.6, 0.6, 0.2, 0.27, 0.27],
last 10 vals [0.13, 0.33, 0.53, 0.6, 0.53, 0.07, 0.4, 0.6, 0.4, 0.47]
Depends directly on:
through the following calculations:
You can also visit the link to Hourly streaming average occurrences across usage patterns’s full calculation graph.
hourly_data_upload_across_usage_patterns
hourly streaming data upload across usage patterns in megabyte.
Example value: 26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in MB:
first 10 vals [0.2, 0.25, 0.35, 0.15, 0.35, 0.45, 0.45, 0.15, 0.2, 0.2],
last 10 vals [0.1, 0.25, 0.4, 0.45, 0.4, 0.05, 0.3, 0.45, 0.3, 0.35]
Depends directly on:
through the following calculations:
You can also visit the link to Hourly streaming data upload across usage patterns’s full calculation graph.
hourly_data_stored_across_usage_patterns
hourly streaming data upload across usage patterns in megabyte.
Example value: 26281 values from 2024-12-31 22:00:00 to 2027-12-31 22:00:00 in MB:
first 10 vals [0.2, 0.25, 0.35, 0.15, 0.35, 0.45, 0.45, 0.15, 0.2, 0.2],
last 10 vals [0.1, 0.25, 0.4, 0.45, 0.4, 0.05, 0.3, 0.45, 0.3, 0.35]
Depends directly on:
through the following calculations:
You can also visit the link to Hourly streaming data upload across usage patterns’s full calculation graph.