As traffic volumes carried by optical networks continue to grow by tens of percent year over year, we are rapidly approaching the capacity limit of the conventional communication band within a ...single-mode fiber. New measures such as elastic optical networking, spectral extension to multi-bands, and spatial expansion to additional fiber overlays or new fiber types are all being considered as potential solutions, whether near term or far. In this tutorial paper, we survey the photonic switching hardware solutions in support of evolving optical networking solutions enabling capacity expansion based on the proposed approaches. We also suggest how reconfigurable add/drop multiplexing nodes will evolve under these scenarios and gauge their properties and relative cost scalings. We identify that the switching technologies continue to evolve and offer network operators the required flexibility in routing information channels in both the spectral and spatial domains. New wavelength-selective switch designs can now support greater resolution, increased functionality and packing density, as well as operation with multiple input and output ports. Various switching constraints can be applied, such as routing of complete spatial superchannels, in an effort to reduce the network cost and simplify the routing protocols and managed pathway count. However, such constraints also reduce the transport efficiency when the network is only partially loaded, and may incur fragmentation. System tradeoffs between switching granularity and implementation complexity and cost will have to be carefully considered for future high-capacity SDM–WDM optical networks. In this work, we present the first cost comparisons, to our knowledge, of the different approaches in an effort to quantify such tradeoffs.
We present the use of interconnected optical mesh networks for early earthquake detection and localization, exploiting the existing terrestrial fiber infrastructure. Employing a waveplate model, we ...integrate real ground displacement data from seven earthquakes with magnitudes ranging from four to six to simulate the strains within fiber cables and collect a large set of light polarization evolution data. These simulations help to enhance a machine learning model that is trained and validated to detect primary wave arrivals that precede earthquakes' destructive surface waves. The validation results show that the model achieves over 95% accuracy. The machine learning model is then tested against an M4.3 earthquake, exploiting three interconnected mesh networks as a smart sensing grid. Each network is equipped with a sensing fiber placed to correspond with three distinct seismic stations. The objective is to confirm earthquake detection across the interconnected networks, localize the epicenter coordinates via a triangulation method and calculate the fiber-to-epicenter distance. This setup allows early warning generation for municipalities close to the epicenter location, progressing to those further away. The model testing shows a 98% accuracy in detecting primary waves and a one second detection time, affording nearby areas 21 s to take countermeasures, which extends to 57 s in more distant areas.
This paper reports the first experimental demonstration of silicon photonic (SiPh) Flex-LIONS, a bandwidth-reconfigurable SiPh switching fabric based on wavelength routing in arrayed waveguide ...grating routers (AWGRs) and space switching. Compared with the state-of-the-art bandwidth-reconfigurable switching fabrics, Flex-LIONS architecture exhibits 21× less number of switching elements and 2.9× lower on-chip loss for 64 ports, which indicates significant improvements in scalability and energy efficiency. System experimental results carried out with an 8-port SiPh Flex-LIONS prototype demonstrate error-free one-to-eight multicast interconnection at 25 Gb/s and bandwidth reconfiguration from 25 Gb/s to 100 Gb/s between selected input and output ports. Besides, benchmarking simulation results show that Flex-LIONS can provide a 1.33× reduction in packet latency and >1.5× improvements in energy efficiency when replacing the core layer switches of Fat-Tree topologies with Flex-LIONS. Finally, we discuss the possibility of scaling Flex-LIONS up to N = 1024 ports (N = M × W) by arranging M 2 W-port Flex-LIONS in a Thin-CLOS architecture using W wavelengths.
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.
This paper investigates the problem of how to optimize the provisioning of virtual network function service chains (VNF-SCs) in elastic optical inter-datacenter networks (EO-IDCNs) under elastic ...optical networking and DC capacity constraints. We take advantage of the broker-based hierarchical control paradigm for the orchestration of cross-stratum resources and propose to realize incentive-driven VNF-SC provisioning with a noncooperative mixed-strategy gaming approach. The proposed gaming model enables tenants to compete for VNF-SC provisioning services due to revenue and quality-of-service incentives and therefore can motivate more reasonable selections of provisioning schemes. We detail the modeling of the game, discuss the existence of the Nash equilibrium states, and design an auxiliary graph-based heuristic algorithm for tenants to compute approximate equilibrium solutions in the games. A dynamic resource pricing strategy, which can set the prices of network resources in real time according to the actual network status, is also introduced for EO-IDCNs as a complementary method to the game-theoretic approach. Results from extensive simulations that consider both static network planning and dynamic service provisioning scenarios indicate that the proposed game-theoretic approach facilitates both higher tenant and network-wide profits and improves the network throughput as well compared with the baseline algorithms, while the dynamic pricing strategy can further reduce the request blocking probability with a factor of ∼2.4×.
Applications running inside data centers are enabled through the cooperation of thousands of servers arranged in racks and interconnected together through the data center network. Current DCN ...architectures based on electronic devices are neither scalable to face the massive growth of DCs, nor flexible enough to efficiently and cost-effectively support highly dynamic application traffic profiles. The FP7 European Project LIGHTNESS foresees extending the capabilities of today's electrical DCNs throPugh the introduction of optical packet switching and optical circuit switching paradigms, realizing together an advanced and highly scalable DCN architecture for ultra-high-bandwidth and low-latency server-to-server interconnection. This article reviews the current DC and high-performance computing (HPC) outlooks, followed by an analysis of the main requirements for future DCs and HPC platforms. As the key contribution of the article, the LIGHTNESS DCN solution is presented, deeply elaborating on the envisioned DCN data plane technologies, as well as on the unified SDN-enabled control plane architectural solution that will empower OPS and OCS transmission technologies with superior flexibility, manageability, and customizability.
In this paper, we present a novel scheme that exploits cross-gain modulation (XGM) in semiconductor optical amplifiers (SOAs) without overall pattern effects. This technique uses two signals with ...reversed-intensity modulation and different wavelength to exploit propagation and gain-compression dynamics in gain-saturated SOAs at almost constant overall input power. The resulting cross-gain-compression mechanism between copropagating waves can lead to all-optical waveform reshaping. By using this technique, we experimentally demonstrate enhanced wavelength conversion by XGM and wavelength-preserving noise compression at 10 Gb/s
This paper proposes and experimentally demonstrates a fully-distributed All-Optical TOKEN (AO-TOKEN) contention resolution technique for AWGR-based optical interconnects. The AO-TOKEN technique is ...implemented by exploiting the saturation effect in SOAs placed at the AWGR outputs. A polarization-diversity scheme allows thoe data and control planes to share the same physical link. The AO-TOKEN is more scalable than alternative electrical/optical solutions since it eliminates the need for a centralized electrical control plane. Our experimental results show that the technique can work over a wavelength-range of ≈ 23 nm using off-the-shelf components. We also successfully demonstrate all-optical contention resolution, packet transmission, and switching with error-free operation at 10 Gb/s.
This paper proposes DeepRMSA, a deep reinforcement learning framework for routing, modulation and spectrum assignment (RMSA) in elastic optical networks (EONs). DeepRMSA learns the correct online ...RMSA policies by parameterizing the policies with deep neural networks (DNNs) that can sense complex EON states. The DNNs are trained with experiences of dynamic lightpath provisioning. We first modify the asynchronous advantage actor-critic algorithm and present an episode-based training mechanism for DeepRMSA, namely, DeepRMSA-EP. DeepRMSA-EP divides the dynamic provisioning process into multiple episodes (each containing the servicing of a fixed number of lightpath requests) and performs training by the end of each episode. The optimization target of DeepRMSA-EP at each step of servicing a request is to maximize the cumulative reward within the rest of the episode. Thus, we obviate the need for estimating the rewards related to unknown future states. To overcome the instability issue in the training of DeepRMSA-EP due to the oscillations of cumulative rewards, we further propose a window-based flexible training mechanism, i.e., DeepRMSA-FLX. DeepRMSA-FLX attempts to smooth out the oscillations by defining the optimization scope at each step as a sliding window, and ensuring that the cumulative rewards always include rewards from a fixed number of requests. Evaluations with the two sample topologies show that DeepRMSA-FLX can effectively stabilize the training while achieving blocking probability reductions of more than 20.3% and 14.3%, when compared with the baselines.
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