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 (
This paper presents a cognitive flexible-bandwidth optical interconnect architecture for datacom networks. The proposed architecture leverages silicon photonic reconfigurable all-to-all switch fabrics interconnecting top-of-rack switches arranged in a Hyper-X-like topology with a cognitive control plane for optical reconfiguration by self-supervised 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 intra-pod 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 self-labeled 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 all-to-all interconnection, the proposed approach can improve the throughput by up to
- NSF-PAR ID:
- 10369221
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Journal of Optical Communications and Networking
- Volume:
- 14
- Issue:
- 2
- ISSN:
- 1943-0620; JOCNBB
- Page Range / eLocation ID:
- Article No. A113
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
error) and demonstrate up to throughput gain from the proposed approach compared with the baseline Hyper-X network using fixed all-to-all intra/inter-portable data center interconnects. -
more » « less
This paper proposes an evolutionary transfer learning approach (Evol-TL) for scalable quality-of-transmission (QoT) estimation in multi-domain elastic optical networks (MD-EONs). Evol-TL exploits a broker-based MD-EON architecture that enables cooperative learning between the broker plane (end-to-end) and domain-level (local) machine learning functions while securing the autonomy of each domain. We designed a genetic algorithm to optimize the neural network architectures and the sets of weights to be transferred between the source and destination tasks. We evaluated the performance of Evol-TL with three case studies considering the QoT estimation task for lightpaths with (i) different path lengths (in terms of the numbers of fiber links traversed), (ii) different modulation formats, and (iii) different device conditions (emulated by introducing different levels of wavelength-specific attenuation to the amplifiers). The results show that the proposed approach can reduce the average amount of required training data by up to
while achieving an estimation accuracy above 95%. -
more » « less
The ambition of this review is to provide an up-to-date synopsis of the state of 3D printing technology for optical and photonic components, to gauge technological advances, and to discuss future opportunities. While a range of approaches have been developed and some have been commercialized, no single approach can yet simultaneously achieve small detail and low roughness at large print volumes and speed using multiple materials. Instead, each approach occupies a niche where the components/structures that can be created fit within a relatively narrow range of geometries with limited material choices. For instance, the common Fused Deposition Modeling (FDM) approach is capable of large print volumes at relatively high speeds but lacks the resolution needed for small detail (
) with low roughness ( ). At the other end of the spectrum, two-photon polymerization can achieve roughness ( ) and detail ( ) comparable to commercial molded and polished optics. However, the practical achievable print volume and speed are orders of magnitude smaller and slower than the FDM approach. Herein, we discuss the current state-of-the-art 3D printing approaches, noting the capability of each approach and prognosticate on future innovations that could close the gaps in performance. -
more » « less
A spatial channel network (SCN) was recently proposed toward the forthcoming spatial division multiplexing (SDM) era, in which the optical layer is explicitly evolved to the hierarchical SDM and wavelength division multiplexing layers, and an optical node is decoupled into a spatial cross-connect (SXC) and wavelength cross-connect to achieve an ultrahigh-capacity optical network in a highly economical manner. In this paper, we report feasibility demonstrations of an evolution scenario regarding the SCN architecture to enhance the flexibility and functionality of spatial channel networking from a simple
fixed-core-access anddirectional spatial channel ring network to a multidegree,any-core-access ,nondirectional , andcore-contentionless mesh SCN. As key building blocks of SXCs, we introduce what we believe to be novel optical devices: amulticore fiber (MCF) splitter, a core selector (CS), and a core and port selector (CPS). We construct free-space optics-based prototypes of these devices using five-core MCFs. Detailed performance evaluations of the prototypes in terms of the insertion loss (IL), polarization-dependent loss (PDL), and intercore cross talk (XT) are conducted. The results show that the prototypes provide satisfactorily low levels of IL, PDL, and XT. We construct a wide variety of reconfigurable spatial add/drop multiplexers (RSADMs) and SXCs in terms of node degree, interport cross-connection architecture, and add/drop port connectivity flexibilities. Such RSADMs/SXCs include a fixed-core-access and directional RSADM using a MCF splitter; an any-core-access, nondirectional SXC with core-contention using a CS; and an any-core-access, nondirectional SXC without core-contention using a CPS. Bit error rate performance measurements for SDM signals that traverse the RSADMs/SXCs confirm that there is no or a very slight optical signal-to-noise-ratio penalty from back-to-back performance. We also experimentally show that the flexibilities in the add/drop port of the SXCs allow us to recover from a single or concurrent double link failure with a wide variety of options in terms of availability and cost-effectiveness. -
more » « less
Wavelength transduction of single-photon signals is indispensable to networked quantum applications, particularly those incorporating quantum memories. Lithium niobate nanophotonic devices have demonstrated favorable linear, nonlinear, and electro-optical properties to deliver this crucial function while offering superior efficiency, integrability, and scalability. Yet, their quantum noise level—a crucial metric for any single-photon-based application—has yet to be investigated. In this work, we report the first, to the best of our knowledge, study with the focus on telecom to near-visible conversion driven by a small detuned telecom pump for practical considerations in distributed quantum processing over fiber networks. Our results find the noise level to be on the order of
photons per time-frequency mode for high conversion, allowing faithful pulsed operations. Through carefully analyzing the origins of such noise and each’s dependence on the pump power and wavelength detuning, we have also identified a formula for noise suppression to photons per mode. Our results assert a viable, low-cost, and modular approach to networked quantum processing and beyond using lithium niobate nanophotonics.
