| 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
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 | 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 |
| operation_function | N | operation function object |
function operand object
| N | 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. |
Normalization object
Examples
1. Minimize an attribute of the demand
...
| language | js |
|---|---|
| collapse | true |
...
| Attribute | Required | Content | Values | Description |
|---|---|---|---|---|
| start | Y | Decimal | Start of the range | |
| end | Y | Decimal | End of the range |
JSON Schema
| View file | ||||
|---|---|---|---|---|
|
Examples
1. Minimize an attribute of the demand
| Code Block | ||||
|---|---|---|---|---|
| ||||
{ "goal": "minimize", "operation_function": { "operandoperands": [ { "function": "attribute", "params": { "attribute": "latency", "demand": "urllc_core" } } ], "operator": "sum" } } |
...
| Code Block | ||||
|---|---|---|---|---|
| ||||
{
"goal": "maximize",
"operation_function": {
"operator": "sum",
"operands": [
{
"operation_function": {
"operator": "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,
{ "operation_functionend": 1000
{ },
"inverseweight": true 2.0
},
{
"operation_function": {
"operator": "sum",
"operandoperands": [
{
"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 {
"start": 50,
"end": 5
},
"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, ideal_value, weight, inverse)
(ideal_value/value) * weight, if ideal_value < value
(value/ideal_value) * weight, if ideal_value > value
Eg:
latency - 1ms (ideal)
candidate_latency - 20 ms
normalized - 1/20 → 0.05
throughput - 1000Mbps
candidate_throughput - 100 Mbps
normalized - 100/1000 → 0.1
Impact Analysis
API - no impact
Controller
Template version upgrade
New parser for the optimization model
Data - no impact
Solver
A new solver for the model (recursive solver?)
...
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 |