Contents

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Underlying optimization platform: Code developed by University of Melbourne and Monash University

Pacakging Packaging and verification of OOF System: VMWare, NetCracker, AT&T

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The OOF execution environment contains the minizinc system, along with data/template rendering system that leverages the adapters to various systems. The flow of execution can be configured via the conifguration configuration file for the application and supports a choice of specific solver or invocations to custom/external solvers.  

Simple Example: Budget Constrained

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Maximum Network Flow Problem

Problem Description

Illustration of the bugdetbudget-constrained max-flow problem. Given a set of starting nodes and destination nodes, the objective is to calculate the maximum amount of "flow" across the nodes, subject to capacity constraints of nodes, constraints on the capacity of the connections (edges), as well as costs associated with utilization of each connection (edge). The objective is to maximize the flow subject to the budget constraint.

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;

% another "stringent" service-specific policy 

constraint sum (i in inNodes, j in outNodes) (x[i,j] * cost[i,j]) <= 0.8 * budget;

% 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]);

% 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) }};

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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This use case is meant to provide a basic skeleton for dscribing describing an example change management scheduling optimization problem. Typical CMSO applications involve a large range of constraints for describing the time constraints as well as complex inter-dependencies across different tasks and entitites entities involved in the change management process. For this initial use case, we provide a small set of simple constraints via a simple but non-trivial application as a means to show how the OOF can be leveraged. As the broader CMSO use case evolves, we anticipate to make the application more representative. 

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ONAP-MC currently provides infrastructure statistics and infrastructure fault notification/remediation as services. For ONAP R1, the MC service encompasse encompass detailed infrastructure utilization statistics across various subsystems compute, network, storage and energy. For ONAP R2 and beyond, this service plans to provide aggregate infrastructure utilization statistics at a multi-cloud instance level, clusters within a multi-cloud instance level etc.

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• Optimized SON dynamic spectrum allocation
• White space and un-licensed unlicensed spectrum use
• Optimized VNF placement across distributed DCs for network slicing
• Slice optimization
• Macro and micro cell interplays
• Energy optimization
• Zero touch new carrier integration

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Higher Order Control Loop: VNF and Service scaling across multiple cloud instances

For Day 1 depoloyment deployment of ONAP and going forward, dynamic scaling of VNF/service instances would entail the following:

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1. Amsterdam Release: OOF did not participate in the Amsterdam Release
2. Beinjing Beijing Release: 
   1. This is the first release. Details on the committmentscommitments, user stories, etc., are available at the Beijing Release Page
   2. Seed code from AT&T was onboarded by mid-January, 2018

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