Proportional QoS over WDM Networks: Blocking Probability
ISCC '01 Proceedings of the Sixth IEEE Symposium on Computers and Communications
Path selection methods with multiple constraints in service-guaranteed WDM networks
IEEE/ACM Transactions on Networking (TON)
Limited-range wavelength translation in all-optical networks
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 3
Lightpath (wavelength) routing in large WDM networks
IEEE Journal on Selected Areas in Communications
Service-specific resource allocation in WDM networks with quality constraints
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
Optimal Resource Allocation and Fairness Control in All-Optical WDM Networks
IEEE Journal on Selected Areas in Communications
A proof of wavelength conversion not improving the Lagrangian bound of the static RWA problem
IEEE Communications Letters
Resource criticality analysis of static resource allocations in WDM networks
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
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This paper presents a study on the Grade-of-Service (GoS) differentiation of static resource allocation in lightpath routed WDM networks, where lightpath requests between node pairs are given. Each request is associated with a service grade. The goal is to maintain certain service levels for the requests of all grades. The service levels are measured in terms of their acceptance ratios. We solve this network optimization problem by adopting a penalty-based framework, in which network design and operation goals can be evaluated based on cost/revenue. We propose a static GoS differentiation model as one minimizing the total rejection and cost penalty, in which the rejection penalty reflects the revenue of accepting a request, and the cost penalty reflects the resource consumption of providing a lightpath to a request. Then, a solution based on the Lagrangian relaxation and subgradient methods is used to solve the proposed optimization problem. Three different application scenarios are presented: static GoS differentiation of requests between the same node pair, static GoS differentiation of requests between different node pairs, and an integration of static GoS differentiation into the network profit objective. The fairness issues and the impact of relative penalty factors are discussed to provide guidelines for network planning.