Relaxation methods for problems with strictly convex separable costs and linear constraints
Mathematical Programming: Series A and B
Convex separable optimization is not much harder than linear optimization
Journal of the ACM (JACM)
Probability, stochastic processes, and queueing theory: the mathematics of computer performance modeling
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Call blocking probabilities in a traffic-groomed tandem optical network
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: In memroy of Olga Casals
Reconfiguration of Traffic Grooming Optical Networks
BROADNETS '04 Proceedings of the First International Conference on Broadband Networks
DYNAMIC TRAFFIC GROOMING: THE CHANGING ROLE OF TRAFFIC GROOMING
IEEE Communications Surveys & Tutorials
Traffic grooming in WDM networks: past and future
IEEE Network: The Magazine of Global Internetworking
Forward-Looking WDM Network Reconfiguration with Per-Link Congestion Control
Journal of Network and Systems Management
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Resource provisioning has for long been an important area of research in network design. The traffic grooming problem in optical networks is a design problem of aggregating sub-wavelength traffic demands onto lightpaths and lightpaths onto fiber links such that the required electronic switching capability, hence network cost, can be minimized. Because of the reconfiguration cost in optical grooming networks, a reactive resource provisioning approach may become inefficient, and result in revenue loss. In this paper, we propose an over-provisioning scheme, which pre-allocates the spare capacity of lightpaths to dynamic sub-wavelength traffic demands such that the network can be more agile in responding to traffic increment requests. For the single-link case, we formulate the problem as a non-linear programming problem, and for under reasonable assumptions, we prove the objective function is convex. We provide an exact algorithm to find the optimal solution. The problem with general topologies is then studied. We prove the NP-hardness in this case, and propose heuristics. Numerical results show our heuristics perform well.