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Contents

Table of Contents

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  1. Most optimization problems can be solved in a declarative manner using a high-level modeling language.
  2. Recent advances in open source optimization platforms allow the solution process to be mostly solver-independent.
  3. By leveraging the library of standard/global constraints, optimization models can be rapidly developed.
  4. By developing a focused set of platform components, we can realize a policy-driven, declarative system that allows ONAP optimization applications be composed rapidly and managed easily
    1. Policy and data adapters
    2. Execution and management environment
    3. Curated "knowledge base" and recipes to provide information on typical optimization examples and how to use the OOF 
  5. More importantly, by providing a way to support both "traditional" optimization applications and model-driven applications, we can provide a choice for users to adapt the platform based on their business needs and skills/expertise.

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Section
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Minizinc Model

Panel
titleData file used in the for the example application. The file format is dzn (Minizinc data format) and the file uses the widely used jinja2 templating for Python, with support for OOF to objects such as "input" (the input API request), SDC (a dummy object that provides network capacities of nodes, as well as cost per unit network utilization), and AAI (another dummy object that provides bandwith for links among different nodes). This data template is rendered into a data file (dzn format), which, together with the model file defines a complete optimization problem.
int: N;  % input nodes 
int: M;  % output nodes 
int: maxbw; % max bandwidth (for convenience) 
float: budget;

set of int: inNodes = 1..N;
set of int: outNodes = 1..M;

array[inNodes] of int: inCap;  % capacities for input nodes 
array[outNodes] of int: outCap;  % capacity for output nodes 
array[inNodes, outNodes] of int: bw;  % max bandwidth of link 
array[inNodes, outNodes] of float: cost;  % unit cost for the link 
array[inNodes, outNodes] of var 0..maxbw: x;  % amount through this link 

constraint forall (i in inNodes) (sum (j in outNodes) (x[i,j]) <= inCap[i]);
constraint forall (j in outNodes) (sum (i in inNodes) (x[i,j]) <= outCap[j]);
constraint forall (i in inNodes, j in outNodes) (x[i,j] <= bw[i,j]);

constraint sum (i in inNodes, j in outNodes) (x[i,j] * cost[i,j]) <= budget;
Info

% The following policy can due to run-time insertion of a policy specified by the service provider

% another "stringent" service-specific policy 
constraint sum (i in inNodes, j in outNodes) (x[i,j] * cost[i,j]) <= 0.8 * budget;

Info

% Another example of a policy inserted at run-time after evaluation of relevant policies for this service

% each link cannot have more than 20% of traffic from a customer 
var flow = sum (i in inNodes, j in outNodes) (x[i,j]);
constraint forall (i in inNodes, j in outNodes) (x[i,j] <= 0.2 * flow);

solve maximize sum (i in inNodes, j in outNodes) (x[i,j]);

Minizinc Data Template

Panel
titleData file used in the for the example application. The file format is dzn (Minizinc data format) and the file uses the widely used jinja2 templating for Python, with support for OOF to objects such as "input" (the input API request), SDC (a dummy object that provides network capacities of nodes, as well as cost per unit network utilization), and AAI (another dummy object that provides bandwith for links among different nodes). This data template is rendered into a data file (dzn format), which, together with the model file defines a complete optimization problem.
% Relevant calls to APIs
{% inNodes, outNodes, budget = input.get("inNodes", "outNodes", "budget") %}
{% inCap, outCap = SDC.getCapacities(inNodes, outNodes) %};
{% bw = AAI.getBandwidthMatrix(inNodes, outNodes) %};
{% cost = SDC.getNetworkCostMatrix(inNodes, outNodes) %};

N = {{ len(inNodes) }};
M = {{ len(outNodes) }};
maxbw = {{ max(max(bw)) }};
budget = {{ budget }};

inCap = {{ inCap }};
outCap = {{ outCap }};

bw = {{ mzn.toMatrix(bw) }}; % writes it out as minizinc matrix
cost = {{ mzn.toMatrix(cost) }};

Minizinc Data File

Panel
titleRendered Minizinc Data File (from Template)
N = 5;
M = 4;
maxbw = 20;
budget = 50;

inCap = [10, 5, 0, 4, 20];

outCap = [10, 0, 5, 4];

bw = [| 10,  5,  0,  0
      |  2,  4, 10,  0
      |  4,  4, 10,  0
      |  2,  0,  0,  5
      |  0,  0,  0,  1 |];

cost = [|  1,  1, 10, 20
        | 90, 90, 90, 90
        |  2,  1,  1,  1
        |  2, 10, 10,  1
        |  9,  9,  9, 99.9 |];

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