In high performance data center networks, switching the data from source node to destination node needs a proper signal processing to decode the address bits and switch the data and to avoid contention. In this article, we propose the hardware design for switching the data from one node to other bi‐directionally. The design is verified in an experimental test bed.
In the multistage bidirectional networks, the same node is accessed multiple times in the same time slot resulting in contention. In this article, we experimentally demonstrate the contention resolution with minimum electronics. This is verified in the experimental testbed and the data is switched from one node to other node without contention. The results are verified in terms of eye diagram in both the directions.
Integration 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.
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