This paper proposes a machine-learning (ML)-aided cognitive approach for effective bandwidth reconfiguration in optically interconnected datacenter/high-performance computing (HPC) systems. The proposed approach relies on a Hyper-X-like architecture augmented with flexible-bandwidth photonic interconnections at large scales using a hierarchical intra/inter-POD photonic switching layout. We first formulate the problem of the connectivity graph and routing scheme optimization as a mixed-integer linear programming model. A two-phase heuristic algorithm and a joint optimization approach are devised to solve the problem with low time complexity. Then, we propose an ML-based end-to-end performance estimator design to assist the network control plane with intelligent decision making for bandwidth reconfiguration. Numerical simulations using traffic distribution profiles extracted from HPC applications traces as well as random traffic matrices verify the accuracy performance of the ML design estimator (${\lt}9\%$<9% error) and demonstrate up to $5 \times$5× throughput gain from the proposed approach compared with the baseline Hyper-X network using fixed all-to-all intra/inter-portable data center interconnects.

中文翻译：

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机器学习辅助的认知重构，用于灵活带宽的HPC和数据中心网络[已邀请]

本文提出了一种机器学习（ML）辅助的认知方法，用于在光互连数据中心/高性能计算（HPC）系统中进行有效的带宽重新配置。所提出的方法依赖于使用分级的帧内/帧间POD光子交换布局大规模扩展了灵活带宽光子互连的类Hyper-X体系结构。我们首先将连通性图和路由方案优化的问题表述为混合整数线性规划模型。设计了一种两阶段启发式算法和一种联合优化方法来解决时间复杂度低的问题。然后，我们提出了一种基于ML的端到端性能估计器设计，以协助网络控制平面进行带宽重新配置的智能决策。