Affiliation:
1. School of Electronics Engineering Vellore Institute of Technology Chennai India
Abstract
AbstractIntegration of the machine learning (ML) technique in all‐optical networks can enhance the effectiveness of resource utilization, quality of service assurances, and scalability in optical networks. All‐optical multistage interconnection networks (MINs) are implicitly designed to withstand the increasing high‐volume traffic demands at data centers. However, the contention resolution mechanism in MINs becomes a bottleneck in handling such data traffic. In this paper, a select list of ML algorithms replaces the traditional electronic signal processing methods used to resolve contention in MIN. The suitability of these algorithms in improving the performance of the entire network is assessed in terms of injection rate, average latency, and latency distribution. Our findings showed that the ML module is recommended for improving the performance of the network. The improved performance and traffic grooming capabilities of the module are also validated by using a hardware testbed.
Subject
Electrical and Electronic Engineering,General Computer Science,Electronic, Optical and Magnetic Materials
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献