Robust upgrade in optical networks under traffic uncertainty

Aparicio-Pardo, Ramón; Pavón‐Mariño, Pablo; Mukherjee, Biswanath

doi:10.1109/ondm.2012.6210204

Cited by 12 publications

(8 citation statements)

References 14 publications

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“…Various optimization models to consider traffic uncertainty were presented in the literature, [13][14][15][16]18,[27][28][29][30][31] but none of the previous studies meets our requirement to consider the total traffic fluctuation over the network. Various optimization models to consider traffic uncertainty were presented in the literature, [13][14][15][16]18,[27][28][29][30][31] but none of the previous studies meets our requirement to consider the total traffic fluctuation over the network.…”

Section: Assumption 2 It Holds Thatmentioning

confidence: 99%

“…Our proposed robust optimization model can deal with total traffic fluctuation over the network using the ellipsoidal uncertainty, which is our goal of this research. Various optimization models to consider traffic uncertainty were presented in the literature, [13][14][15][16]18,[27][28][29][30][31] but none of the previous studies meets our requirement to consider the total traffic fluctuation over the network. The previous studies consider traffic fluctuation locally, or there are some additional bound on the traffic demands.…”

Section: Assumption 2 It Holds Thatmentioning

confidence: 99%

“…15,16 Ramon et al 15 applied robust optimization to incorporate traffic uncertainty into the network upgrade problem. 15,16 Ramon et al 15 applied robust optimization to incorporate traffic uncertainty into the network upgrade problem.…”

Section: Introductionmentioning

confidence: 99%

“…There are also some studies in the literature that applied robust optimization in communication networks considering traffic uncertainty. 15,16 Ramon et al 15 applied robust optimization to incorporate traffic uncertainty into the network upgrade problem. The network can be dimensioned by tuning the tradeoff between network cost and robustness level using their proposed model.…”

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confidence: 99%

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Approach to problem of minimizing network power consumption based on robust optimization

Das

Takahashi

Oki

et al. 2019

Int J Communication

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In this paper, a green and robust optimization model is introduced to minimize network power consumption, which allows fluctuations of traffic demands between source-destination pairs in the network. Our model is based on the green hose model, where the traffic is bounded by just total outgoing and incoming amount at each node. In addition to the green hose model, we use the ellipsoidal uncertainty set to allow a different type of fluctuations in traffic demands. Here, the total amount of squared errors in traffic demands is bounded by a constant which controls the total admissible fluctuations over the network.Applying the conic duality, we formulate our model in the form of mixed-integer second order cone programming (MISOCP) problem. Furthermore, we establish a relationship between our model and the green hose model with bound of link traffic (HLT) model, an developed version of the hose model known as the HLT.Numerical results demonstrate that each of the MISOCP problems can be solved to its optimality in a reasonable time by a general MISOCP solver, and that the proposed model has different tendency in performance with the green hose and green HLT models. KEYWORDSgreen computing, mixed-integer second-order cone programming, power consumption, robust optimization, second-order cone programming, traffic-demand matrix Int J Commun Syst. 2019;32:e3891.wileyonlinelibrary.com/journal/dac

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Section: Assumption 2 It Holds Thatmentioning

confidence: 99%

Section: Assumption 2 It Holds Thatmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Approach to problem of minimizing network power consumption based on robust optimization

Das

Takahashi

Oki

et al. 2019

Int J Communication

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“…One of the earlier works on applying robust optimization to networks is described in [8]. In [9], a study of applying robust optimization to optical networks is presented. Reference [9] discusses a given set of lightpaths and their behavior under uncertainty and applies a robust optimization technique to the specific domain of optical networks.…”

Section: Related Workmentioning

confidence: 99%

Multi-Layer High-Speed Network Design in Mobile Backhaul Using Robust Optimization

Mathew

Das

Gokhale

et al. 2015

J. Opt. Commun. Netw.

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The multi-layer network design problem and the mobile backhaul problem are both interesting from the perspective of choosing the correct technology and protocol as well as choosing the appropriate node architecture to meet a wide variety of overlay traffic demands. Network operators encounter two variants of the multi-layer/ backhaul problem: 1) For a given set of uncertain traffic demands, which set of technologies would minimize the network cost? 2) Would these technology choices be invariant to the changing traffic demands? This problem of technology choice can essentially be abstracted to a switching and grooming problem with the added complexity of unknown traffic demands, which at best may be approximated to some known statistical parameters. To solve this complex switching and grooming problem, our goal is to make use of the theory of robust optimization with the assumption of known boundary conditions on traffic. We present a comprehensive optimization model that considers technology choices in terms of protocols, physical layer parameters, link boundary conditions, and transmission layer constraints. Validated by simulations, our model shows the stand-off conditions between various technologies and how a network operator must take proactive steps to be able to meet requirements of the nextgeneration networks and services. Our main result showcases network design using two technology alternatives [1) Multi-Protocol Label Switching (MPLS) + Optical Transport Network (OTN) + Reconfigurable Optical Add-Drop Multiplexer (ROADM) and 2) Carrier Ethernet (CE) + OTN + ROADM] and the effect of robustness on these choices. A heuristic is used for comparative purposes as well as to exhaustively model the dynamic case of brownfield networks.Index Terms-Carrier Ethernet; Multi-layer optimization.http://dx.

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