Abstract

Segment Routing (SR) combines the simplicity of Link-State routing protocols with the flexibility of Multiprotocol Label Switching (MPLS). By decomposing forwarding paths into segments, identified by labels, SR improves Traffic Engineering (TE) and enables new solutions for the optimization of network resources utilization. This work proposes an Evolutionary Computation approach that enables Path Computation Element (PCE) or Software-defined Network (SDN) controllers to optimize label switching paths for congestion avoidance while using at the most three labels to configure each label switching path. This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT Fundac¸˜ao para a Ciˆencia e Tecnologia within the Project Scope: UID/CEC/00319/2013. info:eu-repo/semantics/publishedVersion

Abstract

Link State routing protocols, such as Open Shortest Path First (OSPF), are widely applied to intra-domain routing in todays IP networks. They provide a good scalability without lost of simplicity. A router running OSPF distributes traf- fic uniformly over Equal-cost Multi-path (ECMP), enabling a better distribution of packets among the existent links. More recently, other load balancing strategies, that consider non even splitting of traffic, have been put forward. Such is the case of the Distributed Exponentially-weighted Flow SpliTting (DEFT), that enables traf- fic to be directed through non equal-cost multi-paths, while preserving the OSPF simplicity. As the optimal link weight computation is known to be NP-hard, intel- ligence heuristics are particularly suited to address this optimization problem. In this context, this work compares the solutions provided by Evolutionary Al- gorithms (EA) for the weight setting problem, considering both ECMP and DEFT load balancing alternatives. In addition to a single objective network congestion optimization problem, both load balancing schemes are also applied to a multi- objective optimization approach able to attain routing configurations resilient to traffic demand variations. COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT -Fundação para a Ciência e Tecnologia within the ProjectScope: UID/CEC/00319/2013.