List of Objective functions in HAS
- krishna moorthy
- Shankaranarayanan Puzhavakath Narayanan
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
Objective Function Object
Attribute | Required | Content | Values | Description |
|---|---|---|---|---|
goal | Y | String | minimize, maximize | The goal of the optimization |
operation_function | Y | Operation function Object | The operation function that has to be optimized |
Operation 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 |
operation-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 |
operation_function | N | 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. |
Normalization object
Attribute | Required | Content | Values | Description |
|---|---|---|---|---|
start | Y | Decimal | Start of the range | |
end | Y | Decimal | End of the range |
JSON Schema
Examples
1. Minimize an attribute of the demand
{
"goal": "minimize",
"operation_function": {
"operands": [
{
"function": "attribute",
"params": {
"attribute": "latency",
"demand": "urllc_core"
}
}
],
"operator": "sum"
}
}2. Minimize the sum of the distance between the demand and the customer location.
objective function - distance_between(demand, location) + distance_between(demand, location)
{
"goal": "minimize",
"operation_function": {
"operator": "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:
Minimize the sum of latencies of slice subnets
objective function - latency(demand) + latency(demand)
{
"goal": "minimize",
"operation_function": {
"operator": "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",
"operation_function": {
"operator": "sum",
"operands": [
{
"operation_function": {
"operator": "min",
"operands": [
{
"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"
}
}
]
},
"normalization": {
"start": 100,
"end": 1000
},
"weight": 2.0
},
{
"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"
}
}
]
},
"normalization": {
"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 |