Metadata-Version: 2.4
Name: topohub
Version: 1.5.1
Summary: Repository of reference Gabriel graph, Internet Topology Zoo, SNDlib, CAIDA and synthetic backbone topologies for networking research
Home-page: https://github.com/piotrjurkiewicz/topohub
Author: Piotr Jurkiewicz
Author-email: piotr.jerzy.jurkiewicz@gmail.com
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Topic :: Internet
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: System :: Networking
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: System Administrators
Classifier: Intended Audience :: Telecommunications Industry
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license
Dynamic: license-file
Dynamic: summary

# TopoHub: A repository of real-world topologies for networking research

Repository of reference Gabriel graph, Internet Topology Zoo, SNDlib, CAIDA and synthetic backbone topologies for
networking research. Synthetic Gabriel graphs very well represent properties of WAN / long-haul optical transport
networks. The repository offers 3600 Gabriel graph topologies with linearly increasing sizes ranging from 5 to 500
nodes, generated in a reproducible manner. Topologies are available in the code repository and can be previewed and
downloaded through a web interface, which allows visualization of individual topologies and exploration of their network
properties. An important additional feature is the visualization of pre-computed link loads in the network using the
Equal-Cost Multipath (ECMP) shortest path routing algorithm under different traffic demand models.

Web interface is available at: https://www.topohub.org

The package also includes a module that can be imported into the popular network emulator Mininet, enabling automatic
usage of the topologies from the repository.

Please cite the following paper if you use of TopoHub in your research:

    @article{topohub,
        title = {TopoHub: A repository of reference Gabriel graph and real-world topologies for networking research},
        journal = {SoftwareX},
        volume = {24},
        pages = {101540},
        year = {2023},
        issn = {2352-7110},
        doi = {10.1016/j.softx.2023.101540},
        author = {Piotr Jurkiewicz}
    }

The Python package can be installed from [Python Package Index (PyPI)](https://pypi.org/project/topohub/) using the following command:

    pip install topohub

Then you can obtain topologies stored in the repository using the `topohub.get()` method and create NetworkX graph objects
basing on them:

```python
import networkx as nx
import topohub

# Obtain topology dicts from JSON files stored in the repository
topo = topohub.get('gabriel/25/0')
topo = topohub.get('backbone/africa')
topo = topohub.get('topozoo/Abilene')
topo = topohub.get('sndlib/polska')

# Create NetworkX graph from node-link dict
g = nx.node_link_graph(topo, edges='edges')

# Access graph parameters
print(g.graph['name'])
print(g.graph['demands'])
print(g.graph['stats']['avg_degree'])

# By default, nodes are referred by their integer IDs
# You can obtain node names through the `name` attribute
print(g.nodes[0]['name'])
print(g.nodes[10]['name'])

# Obtain link length in kilometers between node 0 and 10
print(g.edges[0, 10]['dist'])
# Obtain percentage utilization of the link between node 0 and 10 under ECMP routing in the forward direction
print(g.edges[0, 10]['ecmp_fwd']['uni'])

# You can also load a topology using node names instead of integer IDs as node identifiers
# (this will not work for 'backbone' and 'caida' topologies which have unnamed or duplicated name nodes)
topo = topohub.get('sndlib/polska', use_names=True)
g = nx.node_link_graph(topo, edges='edges')

print(g.graph['demands'])
print(g.edges['Gdansk', 'Warsaw']['dist'])
print(g.edges['Gdansk', 'Warsaw']['ecmp_fwd']['uni'])
```

For usage in Mininet, you can use a helper which automatically creates Mininet Topo classes for selected topologies:

```python
import mininet.net
import topohub.mininet

# Obtain Mininet Topo classes for topologies stored in the repository
topo_cls = topohub.mininet.TOPO_CLS['gabriel/25/0']
topo_cls = topohub.mininet.TOPO_CLS['backbone/africa']

# Alternatively, you can also load a topology using node names instead integer IDs as node identifiers
# (this will not work for 'backbone' category topologies which have unnamed or duplicated name nodes)
topo_cls = topohub.mininet.TOPO_NAMED_CLS['topozoo/Abilene']
topo_cls = topohub.mininet.TOPO_NAMED_CLS['sndlib/polska']

# Initialize Mininet Topo object
topo = topo_cls()
# Create Mininet Network using the selected topology
net = mininet.net.Mininet(topo=topo)
# Start the network and Mininet shell
net.interact()
```

Detailed documentation, including API reference and Mininet usage example, is available at: https://topohub.readthedocs.io
