Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Internet QoS: Architectures and Mechanisms for Quality of Service
Internet QoS: Architectures and Mechanisms for Quality of Service
OSPF: Anatomy of an Internet Routing Protocol
OSPF: Anatomy of an Internet Routing Protocol
Traffic matrix estimation: existing techniques and new directions
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
BRITE: Universal Topology Generation from a User''s Perspective
BRITE: Universal Topology Generation from a User''s Perspective
Traffic matrix estimation on a large IP backbone: a comparison on real data
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Class-Based OSPF Traffic Engineering Inspired on Evolutionary Computation
WWIC '07 Proceedings of the 5th international conference on Wired/Wireless Internet Communications
Multiconstrained Optimization of Networks with Multicast and Unicast Traffic
MMNS '08 Proceedings of the 11th IFIP/IEEE international conference on Management of Multimedia and Mobile Networks and Services: Management of Converged Multimedia Networks and Services
Quality of Service constrained routing optimization using Evolutionary Computation
Applied Soft Computing
Efficient OSPF weight allocation for intra-domain qos optimization
IPOM'06 Proceedings of the 6th IEEE international conference on IP Operations and Management
An efficient process for estimation of network demand for qos-aware IP network planning
IPOM'06 Proceedings of the 6th IEEE international conference on IP Operations and Management
A framework for robust traffic engineering using evolutionary computation
AIMS'13 Proceedings of the 7th IFIP WG 6.6 international conference on Autonomous Infrastructure, Management, and Security: emerging management mechanisms for the future internet - Volume 7943
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Nowadays, network planning and management tasks can be of high complexity, given the numerous inputs that should be considered to effectively achieve an adequate configuration of the underlying network. This paper presents an optimization framework that helps network administrators in setting the optimal routing weights of link state protocols according to the required traffic demands, contributing in this way to improve the service levels quality provided by the network infrastructure. Since the envisaged task is a NP-hard problem, the framework resorts to Evolutionary Computation as the optimization engine. The focus is given to the use of multi-objective optimization approaches given the flexibility they provide to network administrators in selecting the adequate solutions in a given context. Resorting to the proposed optimization framework the administrator is able to automatically obtain highly optimized routing configurations adequate to support the requirements imposed by their customers. In this way, this novel approach effectively contributes to enhance and automate crucial network planning and management tasks.