On Incentive-Driven VNF Service Chaining in Inter-Datacenter Elastic Optical Networks: A Hierarchical Game-Theoretic Mechanism

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× blocking reduction) while assisting users and brokers in achieving higher utilities compared with existing solutions

Self-Taught Anomaly Detection With Hybrid Unsupervised/Supervised Machine Learning in Optical Networks

This paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised machine learning scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to 99% anomaly detection accuracy can be achieved with a false positive rate below 1%

Blind modulation format identification using nonlinear power transformation

This paper proposes and experimentally demonstrates a blind modulation format identification (MFI) method delivering high accuracy (> 99%) even in a low OSNR regime (< 10 dB). By using nonlinear power transformation and peak detection, the proposed MFI can recognize whether the signal modulation format is BPSK, QPSK, 8-PSK or 16-QAM. Experimental results demonstrate that the proposed MFI can achieve a successful identification rate as high as 99% when the incoming signal OSNR is 7 dB. Key parameters, such as FFT length and laser phase noise tolerance of the proposed method, have been characterized

DeepRMSA: A Deep Reinforcement Learning Framework for Routing, Modulation and Spectrum Assignment in Elastic Optical Networks

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

Demonstration of distributed collaborative learning with end-to-end QoT estimation in multi-domain elastic optical networks

This paper proposes a distributed collaborative learning approach for cognitive and autonomous multi-domain elastic optical networking (EON). The proposed approach exploits a knowledge-defined networking framework which leverages a broker plane to coordinate the operations of multiple EON domains and applies machine learning (ML) to support autonomous and cognitive inter-domain service provisioning. By employing multiple distributed ML blocks learning domain-level features and working with broker plane aggregation ML blocks (through the chain rule-based training), the proposed approach enables to develop cognitive networking applications that can fully exploit the multi-domain EON states while obviating the need for the raw and confidential intra-domain data. In particular, we investigate end-to-end quality-of-transmission estimation application using the distributed learning approach and propose three estimator designs incorporating the concepts of multi-task learning (MTL) and transfer learning (TL). Evaluations with experimental data demonstrate that the proposed designs can achieve estimation accuracies very close to (with differences less than 0.5%) or even higher than (with MTL/TL) those of the baseline models assuming full domain visibility

3D-Hyper-FleX-LION: A Flat and Reconfigurable Hyper-X Network for Datacenters

We propose a flat datacenter network using silicon photonic switches. Simulations show up to 2× improvement in throughput-per-watt over a non-oversubscribed Fat-Tree while providing > 2× reduction in the number of switching ASICs and transceivers

Leveraging mixed-strategy gaming to realize incentive-driven VNF service chain provisioning in broker-based elastic optical inter-datacenter networks

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×

Integrated SiPh Flex-LIONS Module for All-to-All Optical Interconnects with Bandwidth Steering

We experimentally demonstrate the first all-to-all optical interconnects with bandwidth steering using an integrated 8×8 SiPh Flex-LIONS module. Experimental results show a 5-dB worst-case crosstalk penalty and 25 Gb/s to 100 Gb/s bandwidth steerin

Silicon Photonic Flex-LIONS for Bandwidth-Reconfigurable Optical Interconnects

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

Experimental demonstration of machine-learning-aided QoT estimation in multi-domain elastic optical networks with alien wavelengths

In multi-domain elastic optical networks with alien wavelengths, each domain needs to consider intradomain and interdomain alien traffic to estimate and guarantee the required quality of transmission (QoT) for each lightpath and perform provisioning operations. This paper experimentally demonstrates an alien wavelength performance monitoring technique and machine-learning-aided QoT estimation for lightpath provisioning of intradomain/interdomain traffic. Testbed experiments demonstrate modulation format recognition, QoT monitoring, and cognitive routing for a 160 Gbaud alien multi-wavelength lightpath. By using experimental training datasets from the testbed and an artificial neural network, we demonstrated an accurate optical-signal-to-noise ratio prediction with an accuracy of ∼95% when using 1200 data points