Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica Sara Carcangiu Anna Mereu An Optimization Tool for Telecommunication Networks

Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica Sara Carcangiu Anna Mereu The Network

Sara Carcangiu Anna Mereu Control Scheme Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica

Sara Carcangiu Anna Mereu Starting from a random or a given initial configuration (a given set of weights), at each iteration the algorithm explores all the neighbour configurations, selecting the best one. Adaptive Memory (Tabu List) Neighbourhood: cartesian moves configurations reachable changing one variable at time m discrete variables each variable is defined by n values Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica The neighbourhood consists at most of configurations Tabu Search metaheuristic:

Sara Carcangiu Anna Mereu The Optimization Algorithm Given a set of metrics, for each source-destination pair, we look for the path that minimizes the maximum link occupation between the link and that avoids the congestion in case of single link failure. G=(N,A) is the graph representing the network: N number of nodes and A number of arcs. M(s,t) is the traffic matrix: for each pair (s,t) of nodes it contains the amount of traffic that will flow from s to t. Different scenarios: normal working condition scenario and failure condition scenarios. The Tabu Search strategy is used to change the link weights in order to find the minimum of the objective function. Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica

Sara Carcangiu Anna Mereu The Optimization Algorithm: the pseudo-code 1. Set an initial weights configuration 2. Store the current solution as best solution 3. for each scenario do a. compute shortest paths b. compute link utilization c. select the maximum link utilization 4. Find the maximum utilization among all scenarios 5. Perform a Tabu Search iteration 6. if the current solution is better than the best solution a. Upgrade the best solution 7. if stop criteria is not reached 8. return to step 3 9. else return the best visited solution and the corresponding optimized weights set Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica

Sara Carcangiu Anna Mereu Dichotomic Search It takes advantage from the presence of the so-called “group of networks” which are networks that behave similarly. Exploring the domain in a more efficient way: taking advantage from restricted number of variations with respect to the huge number of weight values. Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica

Sara Carcangiu Anna Mereu Files Structure Input Files: Network Description Jones Lustig Format comm :(k,s,t) file.nod: contains informations about the general topology of the network. file.arc: contains more detailed informations about the network; in particular it contains the origin and the destination of the arcs. file.sup: contains origin and destination node of the flows (obtained from the traffic matrix). file.mut: contains the maximum physical capacity of the arcs. Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica

Sara Carcangiu Anna Mereu Workflow Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica

Sara Carcangiu Anna Mereu Main file Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica Creation of the object obj: Gh = new Graph ( argv[ 1 ], type ); The C++ class Graph provides a unified mean for reading descriptions of (Linear) Multicommodity Min Cost Flow Problems and storing them in memory, along with a simple interface that can be used to access and change the data. The topology is loaded by reading the input files written in the JLF. Ir = new ISISRouterDeriv(); It creates the object ISISRouterDeriv that reroutes the traffic flows by observing the behaviour of the ISIS routing protocol, that is by looking for the shortest path.

Sara Carcangiu Anna Mereu TSearch procedure Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica

Sara Carcangiu Anna Mereu TSDicotomicExplore: pseudo-code Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica 1.For each variable do a)Set central as the value of the variable in the optimal configuration found in the last iteration, inf as the inferior point of the interval and sup as the superior point of the interval. b)Compute the objective function for the central, inf and sup point c)If fobj(inf)=fobj(sup) return to step 1 d)else if fobj(inf)!=fobj(central) sup=central central=inf+(sup-inf)/2 return to point b e)else if fobj(sup)!=fobj(central) inf=central central=inf+(sup-inf)/2 return to point b

Sara Carcangiu Anna Mereu Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica

Sara Carcangiu Anna Mereu Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica Parallelization

Sara Carcangiu Anna Mereu Objective Function fobj(X) runs the computation of objective function for the configuration defined by X. It is possible to investigate different aspects of IP flow optimization: to each of these aspects different objective functions can be associated. The objective function is a stand-alone procedure and there can be different implementations of it. The one that we consider is the maximum link utilization in case of single link failure. Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica

Sara Carcangiu Anna Mereu The CalFobj function Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica

Sara Carcangiu Anna Mereu The CalFobj function (continue) Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica

Sara Carcangiu Anna Mereu Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica

Sara Carcangiu Anna Mereu

Example 18 nodes 54 arcs 27 physical links Normal Search: n° of iterations to find the optimal configuration: 5 n° of calls to objective function to find the optimal configuration: 2621 time required to find the optimal configuration : sec Dichotomic Search: n° of iterations to find the optimal configuration: 4 n° of calls to objective function to find the optimal configuration: 395 time required to find the optimal configuration : sec Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica

Sara Carcangiu Anna Mereu Results Maximum link occupation with survivability constraint Default configuration Optimum configuration Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica

Sara Carcangiu Anna Mereu Thank you for your attention! Università degli Studi di Cagliari Dipartimento di Ingegneria Elettrica ed Elettronica