Existing Optimization Models
Minimize an unweighted value
{
"minimize":{
"attribute":
{
"distance_between":[
"customer_loc",
"vG"
]
}
}
}
Minimize a weighted value
{
"minimize": {
"attribute": {
"product": [
200,
{
"distance_between": [
"customer_loc",
"vG"
]
}
]
}
}
}
Maximize an unweighted value
{
"maximize": {
"attribute": {
"reliability": [
"URLLC"
]
}
}
}
Maximize a weighted value
{
"maximize": {
"attribute": {
"product": [
200,
{
"reliability": [
"URLLC"
]
}
]
}
}
}
Minimize the sum of unweighted values
{
"minimize": {
"sum": [
{
"distance_between": [
"customer_loc",
"vG"
]
},
{
"distance_between": [
"customer_loc",
"vG"
]
}
]
}
}
Minimize the sum of weighted values
{
"minimize": {
"sum": [
{
"product": [
100,
{
"distance_between": [
"customer_loc",
"vG"
]
}
]
},
{
"product": [
200,
{
"hpa_score": [
"vG"
]
}
]
}
]
}
}
New Optimization Model
| Attribute | Required | Content | Values | Description |
|---|
| goal | Y | String | minimize, maximize | The goal of the optimization |
| objective_function | Y | Objective function Object |
| The objective function that has to be optimized |
Objective function object
| Attribute | Required | Content | Values | Description |
|---|
| operation | N | String | sum, min, max | The operation which will be a part of the objective function |
| operands | N | 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 |
operand object
| Attribute | Required | Content | Values | Description |
|---|
| weight | N | decimal | default: 1.0 | Weight of the operand |
| objective_function | N | Objective 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
Scenario:
Scenario:
objective function - distance_between(demand, location) + distance_between(demand, location)
{
"goal": "minimize",
"objective_function": {
"operation": "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"
}
}
]
}
}
Scenario:
objective function - latency(demand) + latency(demand)
{
"goal": "minimize",
"objective_function": {
"operation": "sum",
"operands": [
{
"function": "attribute",
"weight": 1.0,
"params": {
"demand": "urllc_core",
"attribute": "latency"
}
},
{
"function": "attribute",
"weight": 1.0,
"params": {
"demand": "urllc_ran",
"attribute": "latency"
}
}
]
}
}
Scenario:
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)) ) ]
{
"goal": "maximize",
"objective_function": {
"operation": "sum",
"operands": [
{
"objective_function": {
"operation": "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
},
{
"objective_function": {
"operation": "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)
