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# 2019-networking

# Overview 2019-networking

Public repository for reproducibility of our IFIP Networking 2019 paper

 No newline at end of file

This repository contains a simple framework for routing for reconfigurable demand-aware networks.

The code is written in **Python3.6** and requires some few packages (see next section)

The code has been the basis for computational evaluations within the following publication:

**[1]** Thomas Fenz, Klaus-Tycho Foerster, Stefan Schmid, Anaïs Villedieu: **"Efficient Non-Segregated Routing for Reconfigurable Demand-Aware Networks"**. IFIP Networking 2019 Conference.

# Dependencies and Requirements

Python 3.6. Required python libraries: gurobipy, networkx, munkres, matplotlib. 

**Note**: Our source is tested only on Linux (Ubuntu 18.04.1 LTS) and macOs (Mojave 10.14.3).  

# Structure

* The folder **[algorithms/heuristics](algorithms/heuristics/)** contains all implemented heuristic algorithms.

* The folder **[algorithms/ilps](algorithms/ilps/)** contains all different versions of the implemented ilp algorithm.

* The folder **[data_sets](data_sets/)** contains two sets of input data to test the algorithms (static topology, optical topology, demands).

# Code Structure

The code is structured as follows:

 * **run_simple_tests.py** is the main entry point of the program. It fetches the input data from the data sets and executes all implemented algorithm.

 * **data_sets/simple_data_set1.py** contains simple input data (static topology, optical topology, demands)

 * **data_sets/simple_data_set2.py** contains simple input data (static topology, optical topology, demands)

 * **algorithms/heuristics/demand_first_directed.py** contains the implementation of the Demand First (directed) algorithm

 * **algorithms/heuristics/demand_first_undirected.py** contains the implementation of the Demand First (undirected) algorithm

 * **algorithms/heuristics/gain_demand_directed.py** contains the implementation of the Gain Demand (directed) algorithm

 * **algorithms/heuristics/gain_demand_undirected.py** contains the implementation of the Gain Demand (undirected) algorithm

 * **algorithms/heuristics/gain_update_directed.py** contains the implementation of the Gain Update (directed) algorithm

 * **algorithms/heuristics/gain_update_undirected.py** contains the implementation of the Gain Update (undirected) algorithm

 * **algorithms/heuristics/greedy_links_directed.py** contains the implementation of the Greedy Links (directed) algorithm

 * **algorithms/heuristics/greedy_links_undirected.py** contains the implementation of the Greedy Links (undirected) algorithm

 * **algorithms/heuristics/heuristics_helper_directed.py** contains shared methods used by various heuristic algorithms

 * **algorithms/heuristics/heuristics_helper_undirected.py** contains shared methods used by various heuristic algorithms

 * **algorithms/heuristics/segregated_directed.py** contains the implementation of the Segregated (directed) algorithm

 * **algorithms/heuristics/segregated_undirected.py** contains the implementation of the Segregated (undirected) algorithm

 * **algorithms/ilps/ilp_directed.py** contains the implementation of the Ilp (directed) algorithm

 * **algorithms/ilps/ilp_directed_quadratic_constraints.py** contains the implementation of the Ilp (directed) algorithm

 * **algorithms/ilps/ilp_helper.py** contains shared methods used by various ilp algorithms

 * **algorithms/ilps/ilp_undirected.py** contains the implementation Ilp (undirected) algorithm

 * **algorithms/ilps/ilp_undirected_quadratic_constraints.py** contains the implementation of the Ilp (undirected) algorithm

import networkx as nx

import heuristics_helper_directed as helper

def __demand_first(G, nx_static, D, optical_topology, nN, nR):

    demand_values = []

    demand_pair = []

    for i, j in D:

        demand_pair += [(i, j)]

        demand_values += [D[(i, j)]]

    for k in range(len(D)):

        max_demand = max(demand_values)

        max_index = demand_values.index(max_demand)

        (start, end) = demand_pair[max_index]

        if nx.shortest_path_length(nx_static, start, end, "weight") > nx.shortest_path_length(G, start, end, "weight"):

            helper.update_graph(G, nx_static, nx.shortest_path(G, start, end, "weight"), True, optical_topology, nN, nR)

        del demand_values[max_index]

        del demand_pair[max_index]

    return (nx_static)

def solve(s, o, D):

    ex = None

    nx_full_topology = nx.MultiDiGraph()  # represents all currently possible edges

    nx_static_topology = nx.MultiDiGraph()  # represents static edges and matchings made

    all_arcs_ij = set([(i, j) for _, i, j in o])

    all_arcs_ij.update(s)

    nN = int(max(max(i, j) for i, j in all_arcs_ij) + 1)

    nR = int(max(R for R, _, _ in o) + 1)

    optical_topology = o

    for i, j in s:

        nx_static_topology.add_edge(i, j, weight=s[(i, j)])

        nx_full_topology.add_edge(i, j, key=0, weight=s[(i, j)])

    for r, i, j in o:

        nx_full_topology.add_edge(i, j, key=r + 1, weight=o[(r, i, j)])

    try:

        solution = helper.get_objective(

            __demand_first(nx_full_topology, nx_static_topology, D, optical_topology, nN, nR), D)

    except ex as Exception:

        return False, str(ex), ""

    return True, "", solution