GPU Neural Mutli Objective Solver for Optical Burst Grooming

Abstract

Transparent Optical Networks has been known as a potential solution for high speed flexible optical backbone networks since a long time ago. However, many scientific challenges remain to be overcome such as the problem of Optical Burst Grooming (OBG) with several conflicting objectives and constraints.OBG is a fundamental problem in the engineering, control, and management of optical traffic networks, and arises in most network design applications, including optical burst routing, survivability design, and traffic scheduling. The traffic grooming problem is to coalesce several high and low speed sub bursts close in time together to form a larger burst that will be switched as one unit.In this paper, we first formulate OBG as a Multi Objective Integer Linear Programming (MO-ILP) optimization problem. Then we propose to use a parallel and hierarchical solver based on Artificial Neural Networks (ANN) with Graphics Processing Unit (GPU) parallel implementation using Compute Unified Device Architecture (CUDA). The processing time remains fixed regardless of the input size and the number of optical constraints and conflicting objectives.Our OBG solver benefits of the joint use of advanced ILP-MO optimization methods, ANN large-scale inherent parallelism and CUDA-GPU High-Performance Computing (HPC) architecture. Through a comprehensive simulation study, we show that, our proposed grooming approach can significantly improve the optical traffic performance, resulting in less contention and low burst lost probability.