4.6 Article

A Priority-Based Multiobjective Design for Routing, Spectrum, and Network Coding Assignment Problem in Network-Coding-Enabled Elastic Optical Networks

期刊

IEEE SYSTEMS JOURNAL
卷 14, 期 2, 页码 2358-2369

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSYST.2019.2938590

关键词

Dedicated protection; elastic optical networks; integer linear programming; intelligent optical networks; multiobjective optimization; network coding (NC); routing and spectrum assignment (RSA)

资金

  1. Vingroup Innovation Foundation Annual Research Grant Program [VINIF.2019, DA06]

向作者/读者索取更多资源

In elastic optical networks, the use of network coding (NC) represents a new dimension to further optimize spectrum efficiency, and indeed, combining NC and dedicated path protection has paved the way for achieving greater capacity efficiency, while retaining the merit of near-instantaneous recovery. In order to harness the NC benefits, a more complicated problem called routing, spectrum, and network coding assignment (RSNCA) has to be solved, and in this article, we propose a priority-based multi-objective design for the RSNCA problem aiming at maximizing the network throughput in the constrained bandwidth capacity and simultaneously minimizing the spectrum link usage for accepted demands. The multiobjective design is based on the weighting method, and we present a rigorous analysis on the impact of weight coefficients to the priority of constituent objectives. The efficacy of our design proposal is benchmarked with reference ones based on the traditional single-objective model and for both coding and non-coding approaches on various realistic topologies. It is highlighted that the application of NC brings about considerable throughput enhancement, and furthermore, the multiobjective RSNCA design is highly more efficient than the single-objective RSNCA, as up to more than 50% saving on spectrum link usage could be attained.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据