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README.md

Mapping Algorithms provided for ESCAPE

Introduction

This orchestration algorithm maps service graphs (consisting of (virtual) network functions and logical connections) to resource graphs (consisting of virtualized node and network resources) in a greedy backtracking manner, based on heuristics and customizable preference value calculations.

Requirements

  • Python 2.7.6+
  • NFFG 1.0
    • NetworkX 1.11+
  • Gurobi

Structure

Files required to run the algorithm:

* MappingAlgorithms.py ---> function MAP() is the entry point
* Alg1_Core.py
* GraphPreprocessor.py
* Alg1_Helper.py
* BacktrackHandler.py
* UnifyExceptionTypes.py

Other files (appropriate PYTHONPATH setting maybe required, see StressTest-small.py):

* StressTest-small.py
* CarrierTopoBuilder.py
* MIPBaseline.py
* milp_solution_in_nffg.py

Utilities (can be outdated):

* BatchTest-params.py
* ParameterSearch.py
* SimulatedAnnealing.py
* StressTest.py
* StressTest-agressive.py
* StressTest-decent.py
* StressTest-gwin.py
* StressTest-normal.py
* StressTest-sc8decent.py
* StressTest-sharing.py
* calc_mapping_times.py
* calc_res_util_metrics.py
* count_bt_successes.py
* count_milp_successes.py
* night_test.py

Running parameters

The parameters of the algorithm are:

* ``enable_shortest_path_cache`` -- saves the calculated shortest paths for 
  the resource graph into a file for later usage.
* ``bw_factor``, ``res_factor``, ``lat_factor`` -- the coefficients of 
  bandwidth, node resources and latency respectively, during network 
  function placement preference value. Their sum is suggested to be 3.0.
* ``bt_limit`` -- Backtracking depth limit of the algorithm.
* ``bt_branching_factor`` -- The number of the top preferred placement 
  options to remember.
* ``mode`` -- Mapping operation mode:
         _NFFG.MODE_REMAP_ -- All network function and every reservation 
             attribute of the resource graph are ignored.
         _NFFG.MODE_ADD_ -- The stored VNF information in the substrate
             graph is interpreted as reservation state. Their
             resource requirements are subtracted from the available.
             If an ID is present in both the substrate and request
             graphs, the resource requirements (and the whole
             instance) will be updated.
         _NFFG.MODE_DEL_ -- All the elements of the request will be 
             deleted from the resource graph which has all of its
             connected components speficied in the service graph.
* (``shortest_paths`` -- The shortest path matrix can be added as an input 
  Python object.)
* (``return_dist`` -- The MAP function returns a tuple of the mapped NFFG 
  and the shortest path Python object)

Documentation

An example invocation of the orchestration algorithm for mapping a service graph to a resource graph both given by an NFFG file, can be found in the main of MappingAlgorithms.py.

The documentation for the input structure formats can be found in nffg-doc.pdf.

The project was mainly created for the needs of UNIFY, FP7 project (http://fp7-unify.eu/). The algorithm is incorporated into the ESCAPE framework available at https://sb.tmit.bme.hu/escape/.

For more details on the context and design of the algorithm is (will be) available in the paper published in

IEEE NFV-SDN -- 2nd Workshop for Orchestration for Software Defined Infrastructure (O4SDI), 07th November 2016, Palo Alto, CA, USA. title: Efficient Service Graph Embedding: A Practical Approach authors: Balázs Németh, Balázs Sonkoly (Budapest University of Technology and Economics), Matthias Rost (Technische Universität Berlin), Stefan Schmid (Aalborg University)

License

Licensed under the Apache License, Version 2.0; see LICENSE file.

Copyright (C) 2017 by
Balazs Nemeth <balazs.nemeth@tmit.bme.hu>
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