REMODELACIÓN SANTO TOMÁS

Resource dimensioning generally focuses on three main topics: physical wave- length channel provisioning on opaque networks (opto-electronic-opto (OEO) or fully wavelength convertible networks) [37, 56, 36], placement of elec- tronic/optical devices (wavelength converters, regenerators, or transponders) on transparent/translucent networks [57, 58, 59, 60, 61, 62, 40, 63, 64], and virtual topology design on a transparent WDM network [65, 66, 67, 68, 69, 70]. The work in [71] discussed a combined planning for both fiber resources and converters. However, many of the results were still applied to static or dynamic traffic models with fixed-path routing or fixed alternate routing. Dimensioning fiber/equipment resources on topologies that support dynamic traffic with complicated link-state-based routing algorithms still has not been sufficiently studied.

Regarding dimensioning an opaque network, Nayak and Sivarajan [37] pro- posed an asymptotic routing and dimensioning approach based on absorption probability analysis of a linear traffic growth model. The authors in [56] then proposed a time-dependent blocking probability approach to further reduce network capacity. Their work studied transient network behavior, starting from zero initial traffic, with the assumption that the network would be pe- riodically redimensioned and could be reconstructed in a timely manner to respond to traffic change. Further, the authors in [36] studied the dimension- ing problem for dynamic traffic using a system perspective; they proposed a heuristic basic dimensioning algorithm for boot-strapping network design and a few incremental dimensioning algorithms for future network growth.

that using a more flexible path-selection algorithm for dynamic demands on a well-dimensioned network can greatly improve blocking performance.

Previous work on dimensioning optical transparent/translucent networks with wavelength continuity constraints and physical impairments [72] mostly focused on placement of wavelength converters and regenerators. The au- thors of [57] investigated the usefulness of wavelength converters on varying topologies, traffic loads, and available wavelengths per fibers. Yates [58] provided a comprehensive modeling and performance study on wavelength converter placement in dynamic networks. This analysis showed that the performance improvement strongly depended on the wavelength assignment schemes and wavelength channel allocation. For static traffic demands, the light path provisioning problem is usually solved by formulating a mixed- integer Linear Programming (ILP) solution. The paper [60] proposed an ILP solution to provision wavelength fiber resources with or without wavelength converters. The authors in [71] formulated another ILP solution to allocate wavelength channels, optical cross-connects and wavelength converters on a physical topology dimensioned for a given traffic demand. They decomposed the large problem into dimensioning and routing subproblems and wavelength assignment subproblems to alleviate the computational hurdle. The authors in [64] studied a network coverage problem by minimizing switching nodes and transceivers on a topology.

Many have studied the regenerator placement problem on a given WDM network. The authors in [59] proposed several heuristic regenerator place- ment algorithms for dynamic traffic. The authors in [61] proposed an ILP formulation to compute optical regenerator placement for static demands. In [62], they studied the reduction needed in the cost of electro-optical equip- ment to increase the reachability of the network for a given traffic demand. Further, a more thorough discussion about regenerator based translucent optical network models is discussed in [40]; heuristic regenerator assignment algorithms were proposed with traffic demand predictions, where routing was done by heuristic online RWA algorithms.

Some studies address wavelength allocation for transparent networks but with limited problem scale. Baroni and Bayvel [73] studied the number of required wavelengths for a static and uniform demand on a given topology. The paper modelled the physical topological connectivity and derived the lowest bound of the wavelength number for each network. A heuristic RWA

was also proposed to compute the minimal required wavelength number. A dimensioning solution with a small number of wavelengths per link was dis- cussed in [74]. The authors in [63] compared the lowest theoretical bound of the capacity requirement for optical packet switching, optical burst switch- ing, and optical flow switching networks regardless of routing algorithms.

Another important study related to WDM network dimensioning is the design and reconfiguration of virtual topologies (also called “logical topol- ogy/layer” in some studies). A virtual topology consists of all-optical light- paths, reserved from the physical WDM topology, that provide a layer of ab- straction for IP customers to route and traffic engineer without consideration of wavelength continuity and physical reachability [65, 66, 67]. Many logical layers can share one or more WDM lower-level networks. Virtual topology design is particularly useful for static optical network models, where reserv- ing/releasing a bypass lightpath is time-consuming and expensive. As the lower-level WDM network becomes more dynamic, virtual topologies can be more frequently reconfigured according to traffic demand changes [68, 69, 75]. The authors in [68] proposed an online, logical topology reconfiguration based on live traffic measured on a daily basis. For a different dynamic traffic model, a subwavelength grooming resource optimization problem for multiple possi- ble static traffic matrices was introduced in [76]. In contrast, dimensioning the physical network as I do is increasingly important: upper layer traffic has a more direct impact on the WDM network, with increasing support for on-demand, high-bandwidth services.