Evolutionary algorithms for optimized SDN controllers ... - Google Sites

Jean-Michel SANNER 1, MERYEM OUZZIF 1, Y.HADJADJ-AOUL 2 ,Jean-Emile DARTOIS 3 1 ORANGE-LABS 1 2 University of Rennes 1 3 BCOM

Evolutionary algorithms for optimized SDN controllers & NVFs’ placement in SDN networks ABSTRACT Telco & cloud operators need to conform to SLA constraints negotiated with customers such as latency, reliability, downtime, affinity, response time or duplication… Placement of virtual machines in a data center and placing Virtual Network Function or SDN controllers in telco networks to fulfill theses SLA, is a multiobjective problem. Evolutionary algorithms are considered as ones of the most efficient approach for generating Pareto optimal solutions to multi-objective optimization problems (for example optimizing consumption & global bandwidth simultaneously….) In this presentation we illustrate the use of a genetic algorithm with an ad hoc cross-over operator designed to solve a mono-objective controller placement problem. At a second step, we demonstrate a BCOM designed generic framework for solving multi-objective optimization problems based on the state-of-the -art algorithms such as NSGA-II, NSGA-III, PESA2,eMOEA...

Objective of the controllers’ placement algorithm G(V,E) is the network graph with a set of vertices V & a set of edges E and their associated latencies. The number of controllers is fixed to K. The goal is to minimize each associated cluster maximum diameter i.e. the maximum distance between the controller and the nodes it controls. Let define C as the set of potential controllers nodes, where C is a subset of V. Let also define c ∈ C as a potential controller with

We want to minimize: max

∈ , ∈

Where

,

a node attached to the controller c.

,

represents the shortest path between controller c and node

.

Random initialization of a population of N individuals with K clusters

Evaluation of each individual in the population

Light elitism

Selection Random tournament selection of N*K parents P selection pressure Cross over step N children are built with K parents, A cluster is randomly taken from each parent and attributed to the children, Local optimization Redundant or missing nodes are reallocated or allocated Mutation step Some nodes are randomly exchanged between clusters using a mutation rate based on the global number of nodes

Stop after I iterations and selection of the best individual

Evaluation Comparison with an ILP modeling approach and results produced by an open source solver. •

Solving this particular problem converges quickly with the solver. However, the addition of a new constraint increases significantly the algorithm convergence delay.

•

The incidence of the population size seems low. It is a strong point for convergence speed. We don’t need to have a large population.

•

20 iterations are sufficient for all tested network instances.

•

A mutation rate is useful to prevent local minima. It must be low to act only on some nodes and to maintain convergence.

•

Solutions produced are often optimal and on all tests are close to optimal.

•

Complexity is bounded by: O( I ∙

∙| | )

Max diameter: tens of µs

Conclusions •

Using a simple genetic algorithm with local optimization produces good solutions compared to Integer Linear Programming.

•

Can be used with a multi-constraint and a multiobjective approach.

•

Pave the road for multi-objective technics like NSGA-II with local optimization.

Population: 50 Nb iterations: 20

Networks: Nb of nodes