Traffic Engineering Next Generation IP Networks Using Gene Expression Programming

This paper addresses the problem of traffic engineering (TE) to evaluate the performance of evolutionary algorithms when used as IP routing optimizers and assess the relevance of using "gene expression programming (GEP)" as a new fine-tuning algorithm in destination- and flow-based TE. We consider a TE scheme where link weights are computed using GEP and used as either fine-tuning parameters in open shortest path first (OSPF ) routing or static routing cost in constraint based routing (CBR ). The resulting OSPF and CBR algorithms are referred to as OSPF_gep and CBR_gep. The GEP algorithm is based on a hybrid optimisation model where local search complements the global search implemented by classical evolutionary algorithms to improve the genetic individuals fitness through hill-climbing. We apply the newly proposed TE scheme to compute the routing paths for the traffic offered to a 23-, 28- and 30-node test networks under different traffic conditions and differentiated services situations. We evaluate the performance achieved by the OSPF_gep, CBR_gep algorithms and OSPF_ma, a destination-based routing algorithm where OSPF path selection is driven by the link weights computed by a memetic algorithm (MA ). We compare the performance achieved by the OSPF _gep algorithm to the performance of the OSPF_ma and OSPF algorithms in a simulated routing environment using NS. We also compare the quality of the paths found by the CBR_gep algorithm to the quality of the paths computed by the constraint shortest path first (CSPF ) algorithm when routing bandwidth-guaranteed tunnels using connection-level simulation. Preliminary results reveal the relative efficiency of (1) the OSPF_gep algorithm compared to both the OSPF_ma and OSPF algorithms and (2) the CBR_gep algorithm compared to CSPF routing