...
| Code Block | ||||
|---|---|---|---|---|
| ||||
{
"minimize": {
"sum": [
{
"product": [
100,
{
"distance_between": [
"customer_loc",
"vG"
]
}
]
},
{
"product": [
200,
{
"hpa_score": [
"vG"
]
}
]
}
]
}
}
|
New Optimization Model
Normalization???
unique solutions ???
Objective Function Object
...
| Attribute | Required | Content | Values | Description |
|---|---|---|---|---|
| operator | Y | String | sum, min, max | The operation which will be a part of the objective function |
| operands | Y | List of operand object | The operand on which the operation is to be performed. The operand can be an attribute or result of a function | |
| inverse | N | Boolean | default: False | Flag to specify whether the objective function has to be inverted. |
operand object
| Attribute | Required | Content | Values | Description | ||
|---|---|---|---|---|---|---|
| weightnormalization | N | decimal | default: 1.0 | Weight of normalization object | Set of values used to normalize the operand | |
| operation_function | N | operation function object | ||||
| function | N | String | distance_between, latency_between, attribute | Function to be performed on the parameters | ||
| fucntion_params | N | dict | parameters on which the function will be applied. The parameters will change for each function. |
Examples
1. Minimize an attribute of the demand
...
Normalization object
| Attribute | Required | Content | Values | Description |
|---|---|---|---|---|
| start | Y | Decimal | Start of the range | |
| end | Y | Decimal | End of the range | |
| weight | N | Decimal | Default: 1.0 | Weight of the objective function |
Examples
1. Minimize an attribute of the demand
| Code Block | ||||
|---|---|---|---|---|
| ||||
{
"goal": "minimize",
"operation_function": {
"operand": [
{
"function": "attribute",
"params": {
"attribute": "latency",
"demand": "urllc_core"
}
}
],
"operator": "sum"
}
} |
...
| Code Block | ||||
|---|---|---|---|---|
| ||||
{
"goal": "maximize",
"operation_function": {
"operator": "sum",
"operands": [
{
"operation_function": {
"operator": "min",
"operand": [
{
"weight": 1.0,
"function": "attribute",
"params": {
"demand": "urllc_core",
"attribute": "throughput"
}
},
{
"weight": 1.0,
"function": "attribute",
"params": {
"demand": "urllc_ran",
"attribute": "throughput"
}
},
{
"weight": 1.0,
"function": "attribute",
"params": {
"demand": "urllc_transport",
"attribute": "throughput"
}
}
]
},
"weight": 2.0
},
{
"operation_function": {
"inverse": true,
"operator": "sum",
"operand": [
{
"weight": 1.0,
"function": "attribute",
"params": {
"demand": "urllc_core",
"attribute": "latency"
}
},
{
"weight": 1.0,
"function": "attribute",
"params": {
"demand": "urllc_ran",
"attribute": "latency"
}
},
{
"weight": 1.0,
"function": "attribute",
"params": {
"demand": "urllc_transport",
"attribute": "latency"
}
}
]
},
"weight": 1.0
}
]
}
} |
_bw = [100, 200, 300]
ran_nssi → property bw → func(slice_profile[])
core_nssi → property bw → func(slice_profile[])
tn_nssi → property bw→ func(slice_profile[])
Maximize (min (ran_nssi_bw, core_nssi_bw, tr_nssi_bw))
Max [ sum ( W_bw * min (ran_nssi_bw, core_nssi_bw, tr_nssi_bw), 1/(W_lat * ( sum (w1 * ran_nssi_lat, w2 core_lat, W3* tn_lat)) ) ]
Min/max operator: list of operands
Sum operator: list of operands
prod operator: weight, operand
normalized_unit = func(bw, weight, unit)
normalized_unit = func(lat, weight, unit)
normalization:
function(value, normalization:
function(value, range(start, end), weight)
All ranges are converted to 0 to 1. The inverse operation is not needed , since it is already implied in the range.
normalized value = (value - start) / (end-start)
...