Impact of Wavelength and Modulation Conversion on Translucent Elastic Optical Networks Using MILP

Abstract

Compared to legacy wavelength division multiplexing networks, elastic optical networks (EONs) have added flexibility to network deployment and management. EONs can include previously available functionality, such as signal regeneration and wavelength conversion, as well as new features such as finer-granularity spectrum assignment and modulation conversion. Yet each added feature adds to the cost of the network. In order to quantify the potential benefit of each functionality, we present a linkbased mixed-integer linear programming (MILP) formulation to solve the optimal resource allocation problem. We then propose a recursive model in order to either augment existing network deployments (spectrum and regenerators) or speed up the resource allocation computation time for larger networks with higher traffic demand requirements than can be solved using an MILP. We show through simulation that systems equipped with signal regenerators or wavelength converters require a notably smaller total bandwidth, depending on the topology of the network. We also show that the suboptimal recursive solution speeds up the calculation and makes the running time more predictable, compared to the optimal MILP.

Resource Allocation in Elastic Optical Networks with Physical-Layer Impairments

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show that the SA algorithm has notably better optimization solutions, compared with a published algorithm, the recursive MILP [2]. Moreover, we also show that our proposed system, which is based on the CLGN model and the SA algorithm, speeds up the optimization process and provides similar resource usage, compared to the published benchmark system in [3].

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“…Therefore, optimal solutions are not able to be obtained within a reasonable time using MILPs. Especially with large problem dimensions, obtaining the optimal solutions requires astronomically high computation resources [2].…”

“…Therefore, in order to overcome this shortcoming, heuristic algorithms have been proposed to provide a sub-optimal solution within a reasonable time. Scalability, near-optimality, and time-consumption remain a problem for heuristic algorithms in published literature [2][3][4]17]. Therefore, we propose a heuristic algorithm, the sequential allocation (SA) algorithm, that performs at relatively high speeds, works with different PLI models, and has superb performance.…”

“…The regeneration (including re-timing, re-shaping and re-amplification) is an electrical process functioning at the intermediate nodes. We assume the PLIs are fully negated through the regeneration process [2].…”

“…This equipment's high cost necessarily implies a similar cost for OEO conversion. These considerations require a careful and conservative aforementioned allocation plan [2]. Because one regeneration circuit can only serve one signal, and a limited number of regeneration circuits per regeneration node is assumed, not all signals can be regenerated at a regeneration nodes.…”