gnpy 0.1.3

route planning and optimization tool for mesh optical networks

gnpy is an open-source, community-developed library for building route planning and optimization tools in real-world mesh optical networks.

gnpy is:

• a sponsored project of the OOPT/PSE working group of the Telecom Infra Project.
• fully community-driven, fully open source library
• driven by a consortium of operators, vendors, and academic researchers
• intended for rapid development of production-grade route planning tools
• easily extensible to include custom network elements
• performant to the scale of real-world mesh optical networks

Documentation: https://gnpy.readthedocs.io

Installation

gnpy is hosted in the Python Package Index (gnpy). It can be installed via:

$ pip install gnpy

It can also be installed directly from the repo.

$ git clone https://github.com/telecominfraproject/gnpy
$ cd gnpy
$ python setup.py install

Both approaches above will handle installing any additional software dependencies.

Note: We recommend the use of the Anaconda Python distribution (https://www.anaconda.com/download) which comes with many scientific computing dependencies pre-installed.

Instructions for Use

gnpy is a library for building route planning and optimization tools.

It ships with a number of example programs. Release versions will ship with fully-functional programs.

Note: If you are a network operator or involved in route planning and optimization for your organization, please contact project maintainer James Powell <james.powell@telecominfraproject>. gnpy is looking for users with specific, delineated use cases to drive requirements for future development.

To get started, run the transmission example:

$ python examples/transmission_main_example.py

By default, this script operates on a single span network defined in examples/edfa/edfa_example_network.json

You can specify a different network at the command line as follows. For example, to use the CORONET Continental US (CONUS) network defined in examples/coronet_conus_example.json:

$ python examples/transmission_main_example.py examples/coronet_conus_example.json

This script will calculate the average signal osnr and snr across 93 network elements (transceiver, ROADMs, fibers, and amplifiers) between Abilene, Texas and Albany, New York.

This script calculates the average signal OSNR = P_ch/P_ase and SNR = P_ch/(P_nli+P_ase).

P_ase is the amplified spontaneous emission noise, and P_nli the non-linear interference noise.

The transmission_main_example.py script propagates a specrum of 96 channels at 32 Gbaud, 50 GHz spacing and 0 dBm/channel. These are not yet parametrized but can be modified directly in the script (via the SpectralInformation tuple) to accomodate any baud rate, spacing, power or channel count demand.

The amplifier’s gain is set to exactly compsenate for the loss in each network element. The amplifier is currently defined with gain range of 15 dB to 25 dB and 21 dBm max output power. Ripple and NF models are defined in examples/edfa_config.json

Contributing

gnpy is looking for additional contributors, especially those with experience planning and maintaining large-scale, real-world mesh optical networks.

To get involved, please contact James Powell <[e-mail]> or Gert Grammel <[e-mail]>.

gnpy contributions are currently limited to members of TIP. Membership is free and open to all.

See the Onboarding Guide for specific details on code contribtions.

See AUTHORS.rst for past and present contributors.

Project Background

Data Centers are built upon interchangeable, highly standardized node and network architectures rather than a sum of isolated solutions. This also translates to optical networking. It leads to a push in enabling multi-vendor optical network by disaggregating HW and SW functions and focussing on interoperability. In this paradigm, the burden of responsibility for ensuring the performance of such disaggregated open optical systems falls on the operators. Consequently, operators and vendors are collaborating in defining control models that can be readily used by off-the-shelf controllers. However, node and network models are only part of the answer. To take reasonable decisions, controllers need to incorporate logic to simulate and assess optical performance. Hence, a vendor-independent optical quality estimator is required. Given its vendor-agnostic nature, such an estimator needs to be driven by a consortium of operators, system and component suppliers.

Founded in February 2016, the Telecom Infra Project (TIP) is an engineering-focused initiative which is operator driven, but features collaboration across operators, suppliers, developers, integrators, and startups with the goal of disaggregating the traditional network deployment approach. The group’s ultimate goal is to help provide better connectivity for communities all over the world as more people come on-line and demand more bandwidth- intensive experiences like video, virtual reality and augmented reality.

Within TIP, the Open Optical Packet Transport (OOPT) project group is chartered with unbundling monolithic packet-optical network technologies in order to unlock innovation and support new, more flexible connectivity paradigms.

The key to unbundling is the ability to accurately plan and predict the performance of optical line systems based on an accurate simulation of optical parameters. Under that OOPT umbrella, the Physical Simulation Environment (PSE) working group set out to disrupt the planning landscape by providing an open source simulation model which can be used freely across multiple vendor implementations.

TIP OOPT/PSE & PSE WG Charter

We believe that openly sharing ideas, specifications, and other intellectual property is the key to maximizing innovation and reducing complexity

TIP OOPT/PSE’s goal is to build an end-to-end simulation environment which defines the network models of the optical device transfer functions and their parameters. This environment will provide validation of the optical performance requirements for the TIP OLS building blocks.

• The model may be approximate or complete depending on the network complexity. Each model shall be validated against the proposed network scenario.
• The environment must be able to process network models from multiple vendors, and also allow users to pick any implementation in an open source framework.
• The PSE will influence and benefit from the innovation of the DTC, API, and OLS working groups.
• The PSE represents a step along the journey towards multi-layer optimization.

License

gnpy is distributed under a standard BSD 3-Clause License.

See LICENSE for more details.