OnPremise
Params
name
A human readable description of the object.
carbon_footprint_fabrication
carbon footprint fabrication of on premise server in kilogram.
power
power of on premise server in watt.
lifespan
lifespan of on premise server in year.
idle_power
idle power of on premise server in watt.
ram
ram of on premise server in gigabyte.
cpu_cores
nb cpus cores of on premise server in core.
power_usage_effectiveness
pue of on premise server in dimensionless.
average_carbon_intensity
average carbon intensity of on premise server electricity in gram / kilowatt_hour.
server_utilization_rate
on premise server utilization rate in dimensionless.
base_ram_consumption
base ram consumption of on premise server in megabyte.
base_cpu_consumption
base cpu consumption of on premise server in core.
storage
An instance of Storage.
fixed_nb_of_instances
user defined number of on premise server instances. Can be an EmptyExplainableObject in which case the optimum number of instances will be computed, or an ExplainableQuantity with a dimensionless value, in which case e-footprint will raise an error if the object needs more instances than available.
Backwards links
Calculated attributes
hour_by_hour_cpu_need
on premise server hour by hour cpu need in core.
Example value: 26299 values from 2024-12-31 23:00:00 to 2028-01-01 17:00:00 in core:
first 10 vals [0.6, 0.07, 0.27, 0.4, 0.53, 0.47, 0.6, 0.13, 0.33, 0.33],
last 10 vals [0.6, 0.27, 0.27, 0.53, 0.53, 0.6, 0.4, 0.13, 0.6, 0.13]
Depends directly on:
- Hourly streaming average occurrences across usage patterns
- CPU needed on server server to process streaming
through the following calculations:
You can also visit the link to on premise server hour by hour cpu need’s full calculation graph.
hour_by_hour_ram_need
on premise server hour by hour ram need in gigabyte.
Example value: 26299 values from 2024-12-31 23:00:00 to 2028-01-01 17:00:00 in GB:
first 10 vals [0.03, 0.0, 0.01, 0.02, 0.03, 0.02, 0.03, 0.01, 0.02, 0.02],
last 10 vals [0.03, 0.01, 0.01, 0.03, 0.03, 0.03, 0.02, 0.01, 0.03, 0.01]
Depends directly on:
- Hourly streaming average occurrences across usage patterns
- RAM needed on server server to process streaming
through the following calculations:
You can also visit the link to on premise server hour by hour ram need’s full calculation graph.
available_ram_per_instance
ExplainableQuantity in gigabyte, representing the available ram per on premise server instance.
Example value: 114.9 gigabyte
Depends directly on:
- RAM of on premise server
- on premise server utilization rate
- Base RAM consumption of on premise server
through the following calculations:
You can also visit the link to Available RAM per on premise server instance’s full calculation graph.
available_cpu_per_instance
ExplainableQuantity in core, representing the available cpu per on premise server instance.
Example value: 19.6 core
Depends directly on:
- Nb cpus cores of on premise server
- on premise server utilization rate
- Base CPU consumption of on premise server
through the following calculations:
You can also visit the link to Available CPU per on premise server instance’s full calculation graph.
raw_nb_of_instances
hourly raw number of on premise server instances in dimensionless.
Example value: 26299 values from 2024-12-31 23:00:00 to 2028-01-01 17:00:00 in dimensionless:
first 10 vals [0.03, 0.0, 0.01, 0.02, 0.03, 0.02, 0.03, 0.01, 0.02, 0.02],
last 10 vals [0.03, 0.01, 0.01, 0.03, 0.03, 0.03, 0.02, 0.01, 0.03, 0.01]
Depends directly on:
- on premise server hour by hour ram need
- Available RAM per on premise server instance
- on premise server hour by hour cpu need
- Available CPU per on premise server instance
through the following calculations:
You can also visit the link to Hourly raw number of on premise server instances’s full calculation graph.
nb_of_instances
hourly number of on premise server instances in dimensionless.
Example value: 26299 values from 2024-12-31 23:00:00 to 2028-01-01 17:00:00 in dimensionless:
first 10 vals [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
last 10 vals [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Depends directly on:
through the following calculations:
You can also visit the link to Hourly number of on premise server instances’s full calculation graph.
instances_fabrication_footprint
hourly on premise server instances fabrication footprint in kilogram.
Example value: 26299 values from 2024-12-31 23:00:00 to 2028-01-01 17:00:00 in kg:
first 10 vals [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01],
last 10 vals [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]
Depends directly on:
- Hourly number of on premise server instances
- Carbon footprint fabrication of on premise server
- Lifespan of on premise server
through the following calculations:
You can also visit the link to Hourly on premise server instances fabrication footprint’s full calculation graph.
instances_energy
hourly energy consumed by on premise server instances in kilowatt_hour.
Example value: 26299 values from 2024-12-31 23:00:00 to 2028-01-01 17:00:00 in kWh:
first 10 vals [0.07, 0.06, 0.06, 0.07, 0.07, 0.07, 0.07, 0.06, 0.07, 0.07],
last 10 vals [0.07, 0.06, 0.06, 0.07, 0.07, 0.07, 0.07, 0.06, 0.07, 0.06]
Depends directly on:
- Hourly number of on premise server instances
- Idle power of on premise server
- PUE of on premise server
- Hourly raw number of on premise server instances
- Power of on premise server
through the following calculations:
You can also visit the link to Hourly energy consumed by on premise server instances’s full calculation graph.
energy_footprint
hourly on premise server energy footprint in kilogram.
Example value: 26299 values from 2024-12-31 23:00:00 to 2028-01-01 17:00:00 in kg:
first 10 vals [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01],
last 10 vals [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]
Depends directly on:
- Hourly energy consumed by on premise server instances
- Average carbon intensity of on premise server electricity
through the following calculations:
You can also visit the link to Hourly on premise server energy footprint’s full calculation graph.