GPU parallel neural hierarchical multi objective solver for burst routing and wavelength assignment

摘要

Optical Burst Switching (OBS) is a promising technology for next generation of Transparent Optical Networks (TON). However, many scientific challenges remain to be overcome such as the problem of Burst Routing and Wavelength Assignment (BRWA) with several conflicting objectives and constraints. In this paper, we first formulate the BRWA as a Multi Objective Integer Linear Programming (MO-ILP) optimization problem.In the formulated problem, the proposed BRWA policy will satisfy several constraints in order to guarantee a high-speed management of processes, required by the transparent optical traffic. Then, since the obtained ILP problem contains a large number of optical constraints and conflicting objectives, we propose to use an exact parallel Neural Hierarchical (epNH) MO-ILP solution with Graphics Processing Unit (GPU) parallel implementation using Compute Unified Device Architecture (CUDA). This also allows doing a concurrent search for multiple solutions, reducing processing cost, making hybrid interfaces to other search techniques, and achieving better overall effectiveness.In addition, our architecture based on Artificial Neural Networks (ANN) allows flexibility and scalability. The processing time remains fixed regardless of the input size. Our BRWA GPU-based epNH MO-ILP solver is based on the joint use of advanced MO-ILP optimization methods, ANN large-scale inherent parallelism and CUDA-GPU High-Performance Computing (HPC) architecture.