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

Backwards links

UsageJourneyStep

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 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:

usage pattern UTC

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 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 [0.0, 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 concurrent * hour * megabyte / second.

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 / s:
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, 0.01, 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 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 concurrent * hour * kilobyte / second.

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 * kB / s:
first 10 vals [0.06, 0.17, 0.22, 0.03, 0.17, 0.14, 0.08, 0.14, 0.19, 0.14],
last 10 vals [0.06, 0.03, 0.17, 0.03, 0.25, 0.22, 0.08, 0.08, 0.03, 0.08],
}

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_avg_occurrences_across_usage_patterns

Hourly web application 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 [0.0, 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:

Average hourly Web application job occurrences in usage pattern

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 concurrent * hour * megabyte / second.

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 / s:
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, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0]

Depends directly on:

Hourly data transferred for Web application job in usage pattern

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 concurrent * hour * kilobyte / second.

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 * kB / s:
first 10 vals [0.06, 0.17, 0.22, 0.03, 0.17, 0.14, 0.08, 0.14, 0.19, 0.14],
last 10 vals [0.06, 0.03, 0.17, 0.03, 0.25, 0.22, 0.08, 0.08, 0.03, 0.08]

Depends directly on:

Hourly data stored for Web application job in usage pattern

through the following calculations:

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