| Table of Contents |
|---|
Existing Optimization Models
Minimize an unweighted value
...
| Code Block | ||||
|---|---|---|---|---|
| ||||
{
"minimize": {
"sum": [
{
"product": [
100,
{
"distance_between": [
"customer_loc",
"vG"
]
}
]
},
{
"product": [
200,
{
"hpa_score": [
"vG"
]
}
]
}
]
}
}
|
New Optimization Model
Objective Function Object
| Attribute | Required | Content | Values | Description |
|---|---|---|---|---|
| goal | Y | String | minimize, maximize | The goal of the optimization |
| objectiveoperation_function | Y | Objective Operation function Object | The objective operation function that has to be optimized |
Objective Operation function object
| Attribute | Required | Content | Values | Description |
|---|---|---|---|---|
| operationoperator | Y | String | sum, min, max | The operation which will be a part of the objective function |
| operands | Y | List of operand object | EIther an operation-function or a function | 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. |
operation-function operand object
| Attribute | Required | Content | Values | Description | |
|---|---|---|---|---|---|
| normalization | N | normalization object | Set of values used to normalize the operand | ||
| weight | N | decimalDecimal | defaultDefault: 1.0 | Weight of the | operandfunction |
| objectiveoperation_function | N | Objective operation function object |
function operand object
| Attribute | Required | Content | Values | Description |
|---|---|---|---|---|
| normalization | N | normalization object | Set of values used to normalize the operand | |
| weight | N | Decimal | Default: 1.0 | Weight of the function |
| 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 |
JSON Schema
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|---|---|---|---|---|
|
Examples
1. Minimize an attribute of the demand
| Code Block | ||||
|---|---|---|---|---|
| ||||
{
"goal": "minimize",
"objectiveoperation_function": {
"operandoperands": [
{
"function": "attribute",
"params": {
"attribute": "latency",
"demand": "urllc_core"
}
}
],
"operationoperator": "sum"
}
} |
2. Minimize the sum of the distance between the demand and the customer location.
...
| Code Block | ||||
|---|---|---|---|---|
| ||||
{
"goal": "minimize",
"objectiveoperation_function": {
"operationoperator": "sum",
"operands": [
{
"function": "distance_between",
"weight": 1.0,
"params": {
"demand": "vG",
"location": "customer_loc"
}
},
{
"function": "distance_between",
"weight": 1.0,
"params": {
"demand": "vFW",
"location": "customer_loc"
}
}
]
}
} |
...
| Code Block | ||||
|---|---|---|---|---|
| ||||
{
"goal": "minimize",
"objectiveoperation_function": {
"operationoperator": "sum",
"operands": [
{
"function": "attribute",
"weight": 1.0,
"params": {
"demand": "urllc_core",
"attribute": "latency"
}
},
{
"function": "attribute",
"weight": 1.0,
"params": {
"demand": "urllc_ran",
"attribute": "latency"
}
}
]
}
} |
...
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|---|---|---|---|---|
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{
"goal": "maximize",
"objectiveoperation_function": {
"operationoperator": "sum",
"operands": [
{
"objectiveoperation_function": {
"operationoperator": "min",
"operandoperands": [
{
"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"
}
}
]
},
"weightnormalization": {
2.0 }, "start": 100,
{ "objective_functionend": {1000
"inverse": true},
"operationweight": "sum",2.0
},
"operand": [{
"operation_function": {
"operator": "sum",
"operands": [
{
"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"
}
}
]
},
"weightnormalization": 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)
Impact Analysis
API
Controller
Data
Solver
...
"start": 50,
"end": 5
},
"weight": 1.0
}
]
}
} |
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)
Eg:
latency range: 50 ms to 5 ms
| candidate latency | Normalized value |
|---|---|
| 20 ms | 0.667 |
| 40 ms | 0.222 |
throughput range: 100 Mbps to 1000Mbps
| candidate throughput | Normalized value |
|---|---|
| 300 Mbps | 0.222 |
| 800 Mbps | 0.778 |