Abstract

In this work, the main goal is to develop and evaluate a number of optimization algorithms in the task of improving Quality of Service levels in TCP/IP based networks, by configuring the routing weights of link-state protocols such as OSPF. Since this is a complex problem, some meta-heuristics from the Evolutionary Computation arena were considered, working over a mathematical model that allows for flexible cost functions, taking into account several measures of the network behavior such as network congestion and end-to-end delays. A number of experiments were performed, resorting to a large set of network topologies, where Evolutionary Algorithms (EAs), Differential Evolution and some common heuristic methods including local search were compared. EAs make the most promising alternative leading to solutions with an effective network performance even under unfavorable scenarios. Document type: Part of book or chapter of book

Abstract

Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to mini- mize network congestion. In both tasks, the optimization considers sce- narios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came nat- urally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios. Document type: Part of book or chapter of book

Abstract

Energy consumption has already become a major challenge to the current Internet. Most researches aim at lowering energy consumption under certain fixed performance constraints. Since trade-offs exist between network performance and energy saving, Internet Service Providers (ISPs) may desire to achieve different Traffic Engineering (TE) goals corresponding to changeable requirements. The major contributions of this paper are twofold: 1) we present an OSPF-based routing mechanism, Routing On Demand (ROD), that considers both performance and energy saving, and 2) we theoretically prove that a set of link weights always exists for each trade-off variant of the TE objective, under which solutions (i.e., routes) derived from ROD can be converted into shortest paths and realized through OSPF. Extensive evaluation results show that ROD can achieve various trade-offs between energy saving and performance in terms of Maximum Link Utilization, while maintaining better packet delay than that of the energy-agnostic TE. © 2012 IFIP International Federation for Information Processing. Document type: Part of book or chapter of book

Abstract

number of Traffic Engineering (TE) approaches have been recently proposed to improve the performance of network routing protocols, both developed over MPLS and intra-domain protocols such as OSPF. In this work, a TE approach is proposed for routing optimization in scenarios where unicast and multicast demands are simultaneously present. Evolutionary Algorithms are used as the optimization engine with overall network congestion as the objective function. The optimization aim is to reach a set of (near-)optimal weights to configure the OSPF protocol, both in its standard version and also considering the possibility of using multitopology variants. The results show that the proposed optimization approach is able to obtain networks with low congestion, even under scenarios with heavy unicast/multicast demands. This work was supported by the Portuguese Foundation for Science and Technology under project POSC/EIA/59899/2004, partially funded by FEDER.

Abstract

Evolutionary Algorithms (EAs) have been used to develop methods for Traffic Engineering (TE) over IP-based networks in the last few years, being used to reach the best set of link weights in the configuration of intra-domain routing protocols, such as OSPF. In this work, the multiobjective nature of a class of optimization problems provided by TE with Quality of Service constraints is identified. Multiobjective EAs (MOEAs) are developed to tackle these tasks and their results are compared to previous approaches using single objective EAs. The effect of distinct genetic representations within the MOEAs is also explored. The results show that the MOEAs provide more flexible solutions for network management, but are in some cases unable to reach the level of quality obtained by single objective EAs. Furthermore, a freely available software application is described that allows the use of the mentioned optimization algorithms by network administrators, in an user-friendly way by providing adequate user interfaces for the main TE tasks. FCT - project ref. PTDC/EIA-EIA/115176/2009; grant UMINHO/BII/061/2009