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Existing Optimization Models
Minimize an unweighted value
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LIST OF OPTIMiZATION GOALS
- minimize
- maximize
LIST OF OBJECTIVE FUNCTIONS
- attribute
- sum
Minimize an unweighted value
{ "minimize":{ "attribute": { "distance_between":[ "customer_loc", "vG" ] } } } |
...
Minimize a weighted value
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{
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{ "minimize": { "attribute": { "product": [ 200, { 200, { "distance_between": [ "customer_loc", "vG" ] } ] } } } |
Maximize an unweighted value
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{
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{ "maximize": { "attribute": { { "reliability": [ "URLLC" ] } } } |
Maximize a weighted value
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{
"maximize": {
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JSON Viewer | ||||
{ "minimize":{ "attribute": { "product": [ 200, { 200, { "reliability": [ "URLLC" ] } ] } } } |
Minimize the sum of unweighted values
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{
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{ "minimize": { "sum": [ { { "distance_between": [ "customer_loc", "vG" ] }, { "distance_between": [ "customer_loc", "vG" ] } ] } } |
Minimize the sum of weighted values
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{ "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 |
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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 |
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operator | Y | String | attribute*, 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 |
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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 | |||||
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normalization | N | normalization object | Set of values used to normalize the operand | ||||||
weight | N | Decimal | Default: 1.0 | Weight of the function | |||||
function | N | function | Y | String | distance_between, latency_between, attribute | Function to be performed on the parameters | |||
fucntion_params | N | decimal | default: 1.0 | Weight of the operand | params | Y | dict | parameters on which the function will be applied. The parameters will change for each function. |
Normalization object
Attribute | Required | Content | Values | Description |
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start | Y | Decimal | Start of the range | |
end | Y | Decimal | End of the range |
JSON Schema
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ExamplesScenario:
1. Minimize an attribute of the demand
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{
"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)
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{ "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)
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{ "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)) ) ]
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{
"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 |