Deep reinforcement learning (DRL) enables autonomic optical networking by allowing the network control and management systems to self-learn successful networking policies from operational experiences. This paper proposes a transfer learning approach for effective and scalable DRL in optical networks. We first present a modular DRL agent design to facilitate retrieving and transferring relevant knowledge between tasks requiring different dimensions of network state data. In particular, we partition network state data into common states, which contain generic information critical to multiple tasks (e.g., the spectrum utilization on fiber links), and task-specific states that are only used by a specific task (e.g., the utilization of virtual network functions). Separate neural network blocks are employed to process different state data. Based on the modular agent design, a multi-task learning (MTL) aided knowledge transferring scheme is proposed. The proposed scheme trains an MTL agent that can master multiple tasks simultaneously and thus enables to learn and transfer better-generalized knowledge across tasks. We perform case studies on the proposed transfer DRL approach considering two scenarios, namely, knowledge transferring between routing, modulation and spectrum assignment (RMSA) tasks for different networks and knowledge transferring from RMSA tasks to anycast service provisioning tasks. The DRL designs for RMSA and anycast service provisioning, including the state, action, and reward formulations and the training mechanisms, are also elaborated. Performance evaluations under both scenarios show that the proposed approach can effectively expedite the training processes of the target tasks and improve the ultimate service throughput.

Accurate delay estimation is one of the enablers of future network connectivity services, as it facilitates the application layer to anticipate network performance. If such connectivity services require isolation (slicing), such delay estimation should not be limited to a maximum value defined in the Service Level Agreement, but to a finer-grained description of the expected delay in the form of, e.g., a continuous function of the load. Obtaining accurate end-to-end (e2e) delay modeling is even more challenging in a multi-operator (Multi-AS) scenario, where the provisioning of e2e connectivity services is provided across heterogeneous multi-operator (Multi-AS or just domains) networks. In this work, we propose a collaborative environment, where each domain Software Defined Networking (SDN) controller models intra-domain delay components of inter-domain paths and share those models with a broker system providing the e2e connectivity services. The broker, in turn, models the delay of inter-domain links based on e2e monitoring and the received intra-domain models. Exhaustive simulation results show that composing e2e models as the summation of intra-domain network and inter-domain link delay models provides many benefits and increasing performance over the models obtained from e2e measurements.

While the Fat-Tree network topology represents the dominant state-of-art solution for large-scale HPC networks, its scalability in terms of power, latency, complexity, and cost is significantly challenged by the ever-increasing communication bandwidth among tens of thousands of heterogeneous computing nodes. We propose 3D-Hyper-FleX-LION, a flat hybrid electronic-photonic interconnect network that leverages the multichannel nature of modern multi-terabit switch ASICs (with 100 Gb/s granularity) and a reconfigurable all-to-all photonic fabric called Flex-LIONS. Compared to a Fat-Tree network interconnecting the same number of nodes and with the same oversubscription ratio, the proposed 3D-Hyper-FleX-LION offers a 20% smaller diameter, 3x lower power consumption, 10X fewer cable connections, and 4x reduction in the number of transceivers. When bandwidth reconfiguration capabilities of Flex-LIONS are exploited for non-uniform traffic workloads, simulation results indicate that 3D-Hyper-FleX-LION can achieve up to 4x improvement in energy efficiency for synthetic traffic workloads with high locality compared to Fat-Tree.

In this paper, we propose an incentive-driven virtual network function service chaining (VNF-SC) framework for optimizing the cross-stratum resource provisioning in multi-broker orchestrated inter-datacenter elastic optical networks (IDC-EONs). The proposed framework employs a non-cooperative hierarchical game-theoretic mechanism, where the resource brokers and the VNF-SC users play the leader and the follower games, respectively. In the leader game, the brokers calculate VNF-SC service schemes for users and compete for the provisioning tasks. While in the follower game, the users compete for VNF-SC services for jointly optimizing the resource cost and the received quality-of-service. We first elaborate on the modeling of the follower game, discuss the existence of Nash equilibrium and propose a mixed-strategy gaming approach enabled by an auxiliary graph-based algorithm to facilitate users selecting the most appropriate service schemes. Then, under the assumption that the brokers are aware of the principle of the follower game, we present the model for the leader game and develop a time-efficient heuristic algorithm for brokers to compete for the provisioning tasks. Simulations show that the proposed incentive-driven VNF-SC framework significantly improves the network throughput (i.e., $>4.8 \times$ blocking reduction) while assisting users and brokers in achieving higher utilities compared with existing solutions.

Silicon Photonics (SiPs) can overcome the energy and bandwidth limitations of electrical interconnects in networks-on-chip (NoCs) and enable efficient global all-to-all connectivity-the ideal from a performance perspective. Unfortunately, state-of-the-art SiP switching fabrics impose power overheads for thermo-optical control of microring resonators (MRs), excessive crosstalk, or challenging physical layout. The Arrayed Waveguide Grating Router (AWGR) is a SiP device that provides layout-efficient and scalable all-to-all connectivity through wavelength-routing on a passive and compact platform. Recent technological advances now enable AWGR integration with significantly reduced footprint (2), crosstalk (<;-38dB), and loss (<;2dB), making AWGRs emerge as a major enabler for energy-efficient all-to-all connectivity in NoCs. This paper, for the first time, populates the design space of AWGR-based NoC topologies and compares AWGRs to state-of-the-art SiP fabrics and aggressive electrical baselines. Our results make a compelling case for AWGRs in interposer-based systems with processor disintegration, which have high bandwidth demands, large on-chip distances, and less stringent area constraints. AWGR-based NoCs can offer an average of 1.67× power savings, 1.23× speed-up, and 1.5× lower energy-delay-product (EDP) on PARSEC3.0/SPLASH-2× workloads compared to electrical baselines, and fewer waveguides, wavelengths, or MRs than alternative SiP crossbar fabrics.

In future performance improvement of the basic building block of supercomputers has to come through increased integration enabled by 3D (vertical) and 2.5D (horizontal) die-stacking. But to take advantage of this integration we need an interconnection network between the memory and compute die that not only can provide an order of magnitude higher bandwidth but also consume an order of magnitude less power than today's state of the art electronic interconnects. We show how Arrayed Waveguide Grating Router-based photonic interconnects implemented on the silicon interposer can be used to realize a 16 × 16 photonic Network-on-Chip (NoC) with a bisection bandwidth of 16 Tb/s. We propose a baseline network, which consumes 2.57 pJ/bit assuming 100% utilization. We show that the power is dominated by the electro-optical interface of the transmitter, which can be reduced by a more aggressive design that improves the energy per bit to 0.454 pJ/bit at 100% utilization. Compared to recently proposed interposer-based electrical NoC's we show an average performance improvement of 25% on the PARSEC benchmark suite on a 64-core system using the Gem5 simulation framework.

Elastic optical networking (EON) has emerged in recent years as a promising solution for implementing flexible bandwidth channels (flexpaths) that efficiently match the allocated bandwidth with the traffic demand using agile granularities of spectrum allocation. However, the additional flexibility in such networks raises challenges in terms of efficient control and management of spectrum resources. Among them, three important issues are (1) mitigation of spectral fragmentation, (2) implementation of impairment awareness and enhancement of robustness against impairments for potentially large-bandwidth flexpaths, and (3) design of an efficient restoration scheme to combat network failures. This paper presents an adaptive spectrum control and management scheme, which includes: dynamic on-demand spectral defragmentation, adaptive combinational quality of transmission (QoT) restoration (ACQR) and supervisory channel-assisted active restoration, to account for the three issues above. We present scalable networking algorithms and experimental demonstrations that address these issues in an EON testbed. Simulation results show that the defragmentation technique is capable of reducing the provisioning blocking probability by half with only one defragmentation module on each link. Then, we also show that the ACQR can efficiently restore many degraded flexpaths on the same impaired link while reducing the restoration blocking probability by a factor of 10 compared with the conventional rerouting method. At last, we show via simulation the advantages of using supervisory channels to determine restoration path quality and selection in EON restorations. This paper also presents experimental demonstrations to corroborate the effectiveness and feasibility of implementing these capabilities in next generation optical networks.

This paper discusses the architecture and performance studies of Datacenter Optical Switch (DOS) designed for scalable and high-throughput interconnections within a data center. DOS exploits wavelength routing characteristics of a switch fabric based on an Arrayed Waveguide Grating Router (AWGR) that allows contention resolution in the wavelength domain. Simulation results indicate that DOS exhibits lower latency and higher throughput even at high input loads compared with electronic switches or previously proposed optical switch architectures such as OSMOSIS [4, 5] and Data Vortex [6, 7]. Such characteristics, together with very high port count on a single switch fabric make DOS attractive for data center applications where the traffic patterns are known to be bursty with high temporary peaks [13]. DOS exploits the unique characteristics of the AWGR fabric to reduce the delay and complexity of arbitration. We present a detailed analysis of DOS using a cycle-accurate network simulator. The results show that the latency of DOS is almost independent of the number of input ports and does not saturate even at very high (approx 90%) input load. Furthermore, we show that even with 2 to 4 wavelengths, the performance of DOS is significantly better than an electrical switch network based on state-of-the-art flattened butterfly topology.