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WebApplicationJob

Params

name

A human readable description of the object.

service

An instance of WebApplication.

data_transferred

sum of all data uploads and downloads for request web application job from e-footprint hypothesis in megabyte.

data_stored

data stored by request web application job from e-footprint hypothesis in kilobyte.

implementation_details

description to be done

Calculated attributes

request_duration

ExplainableQuantity in second, representing the web application job request duration from e-footprint hypothesis.

Example value: 1.0 second

Depends directly on:

through the following calculations:

You can also visit the link to Web application job request duration from e-footprint hypothesis’s full calculation graph.

compute_needed

ExplainableQuantity in cpu_core, representing the from e-footprint analysis of boavizta’s ecobenchmark data.

Example value: 0.08 cpu_core

Depends directly on:

through the following calculations:

You can also visit the link to from e-footprint analysis of Boavizta’s Ecobenchmark data’s full calculation graph.

ram_needed

ExplainableQuantity in megabyte, representing the from e-footprint analysis of boavizta’s ecobenchmark data.

Example value: 6.15 megabyte

Depends directly on:

through the following calculations:

You can also visit the link to from e-footprint analysis of Boavizta’s Ecobenchmark data’s full calculation graph.

hourly_occurrences_per_usage_pattern

Dictionary with UsagePattern as keys and hourly web application job occurrences in usage pattern as values, in dimensionless.

Example value: {
id-46655e-usage-pattern: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in dimensionless:
first 10 vals [5.0, 9.0, 1.0, 4.0, 4.0, 8.0, 4.0, 6.0, 5.0, 5.0],
last 10 vals [8.0, 6.0, 9.0, 3.0, 9.0, 1.0, 5.0, 1.0, 9.0, 1.0],
}

Depends directly on:

through the following calculations:

You can also visit the link to Hourly Web application job occurrences in usage pattern’s full calculation graph.

hourly_avg_occurrences_per_usage_pattern

Dictionary with UsagePattern as keys and average hourly web application job occurrences in usage pattern as values, in dimensionless.

Example value: {
id-46655e-usage-pattern: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in dimensionless:
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 Average hourly Web application 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 web application job in usage pattern as values, in megabyte.

Example value: {
id-46655e-usage-pattern: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in MB:
first 10 vals [11.0, 19.8, 2.2, 8.8, 8.8, 17.6, 8.8, 13.2, 11.0, 11.0],
last 10 vals [17.6, 13.2, 19.8, 6.6, 19.8, 2.2, 11.0, 2.2, 19.8, 2.2],
}

Depends directly on:

through the following calculations:

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

hourly_data_stored_per_usage_pattern

Dictionary with UsagePattern as keys and hourly data stored for web application job in usage pattern as values, in kilobyte.

Example value: {
id-46655e-usage-pattern: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in kB:
first 10 vals [500.0, 900.0, 100.0, 400.0, 400.0, 800.0, 400.0, 600.0, 500.0, 500.0],
last 10 vals [800.0, 600.0, 900.0, 300.0, 900.0, 100.0, 500.0, 100.0, 900.0, 100.0],
}

Depends directly on:

through the following calculations:

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

hourly_occurrences_across_usage_patterns

hourly web application job occurrences across usage patterns in dimensionless.

Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in dimensionless:
first 10 vals [5.0, 9.0, 1.0, 4.0, 4.0, 8.0, 4.0, 6.0, 5.0, 5.0],
last 10 vals [8.0, 6.0, 9.0, 3.0, 9.0, 1.0, 5.0, 1.0, 9.0, 1.0]

Depends directly on:

through the following calculations:

You can also visit the link to Hourly Web application job occurrences across usage patterns’s full calculation graph.

hourly_avg_occurrences_across_usage_patterns

hourly web application job average occurrences across usage patterns in dimensionless.

Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in dimensionless:
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 Web application job average occurrences across usage patterns’s full calculation graph.

hourly_data_transferred_across_usage_patterns

hourly web application job data transferred across usage patterns in megabyte.

Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in MB:
first 10 vals [11.0, 19.8, 2.2, 8.8, 8.8, 17.6, 8.8, 13.2, 11.0, 11.0],
last 10 vals [17.6, 13.2, 19.8, 6.6, 19.8, 2.2, 11.0, 2.2, 19.8, 2.2]

Depends directly on:

through the following calculations:

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

hourly_data_stored_across_usage_patterns

hourly web application job data stored across usage patterns in kilobyte.

Example value: 26298 values from 2024-12-31 23:00:00+00:00 to 2028-01-01 17:00:00+00:00 in kB:
first 10 vals [500.0, 900.0, 100.0, 400.0, 400.0, 800.0, 400.0, 600.0, 500.0, 500.0],
last 10 vals [800.0, 600.0, 900.0, 300.0, 900.0, 100.0, 500.0, 100.0, 900.0, 100.0]

Depends directly on:

through the following calculations:

You can also visit the link to Hourly Web application job data stored across usage patterns’s full calculation graph.