Metadata-Version: 1.1
Name: fnss
Version: 0.1.2
Summary: Fast creation and configuration of topologies, traffic matrices and event schedules for network simulations
Home-page: http://fnss.github.com/
Author: Lorenzo Saino, Cosmin Cocora
Author-email: fnss.dev@gmail.com
License: BSD license
Download-URL: http://fnss.github.com/
Description: 
        The Fast Network Simulation Setup (FNSS) core library is a Python library
        providing a set of features allowing network researchers and engineers to
        simplify the setup of a network simulation.
        
        These features include the ability to:
        
         * Parse a topology from a dataset, a topology generator or generate it
           according to a number of synthetic models.
         * Apply link capacities, link weights, link delays and buffer sizes.
         * Deploy protocol stacks and applications on network nodes.
         * Generate traffic matrices.
         * Generate event schedules.
        
        The core library can be used in conjunction with the FNSS Java and C++ API or
        the ns-2 and ns-3 adapters to import topologies, traffic matrices and event
        schedules in the desired target simulator.
        
Keywords: network,simulation,topology,traffic matrix,link capacity,delay,protocol stack
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Telecommunications Industry
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.1
Classifier: Programming Language :: Python :: 3.2
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering
Requires: networkx (>= 1.7)
Requires: numpy (>= 1.6)
