This paper presents a cognitive flexiblebandwidth optical interconnect architecture for datacom networks. The proposed architecture leverages silicon photonic reconfigurable alltoall switch fabrics interconnecting topofrack switches arranged in a HyperXlike topology with a cognitive control plane for optical reconfiguration by selfsupervised learning. The proposed approach makes use of a clustering algorithm to learn the traffic patterns from historical traces. We developed a heuristic algorithm for optimizing the intrapod connectivity graph for each identified traffic pattern. Further, to mitigate the scalability issue induced by frequent clustering operations, we parameterized the learned traffic patterns by a support vector machine classifier. The classifier is trained offline by selflabeled data to enable the classification of traffic matrices during online operations, thereby facilitating cognitive reconfiguration decision making. The simulation results show that compared with a static alltoall interconnection, the proposed approach can improve the throughput by up to
This paper proposes a machinelearning (ML)aided cognitive approach for effective bandwidth reconfiguration in optically interconnected datacenter/highperformance computing (HPC) systems. The proposed approach relies on a HyperXlike architecture augmented with flexiblebandwidth photonic interconnections at large scales using a hierarchical intra/interPOD photonic switching layout. We first formulate the problem of the connectivity graph and routing scheme optimization as a mixedinteger linear programming model. A twophase heuristic algorithm and a joint optimization approach are devised to solve the problem with low time complexity. Then, we propose an MLbased endtoend 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 (
 NSFPAR ID:
 10210534
 Publisher / Repository:
 Optical Society of America
 Date Published:
 Journal Name:
 Journal of Optical Communications and Networking
 Volume:
 13
 Issue:
 6
 ISSN:
 19430620; JOCNBB
 Format(s):
 Medium: X Size: Article No. C10
 Size(s):
 Article No. C10
 Sponsoring Org:
 National Science Foundation
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