Optical Network Automation and Programmability for 6G: State-of-the-Art, Vision, and Challenges

The current 6G vision foresees a massive increase in connected devices and more widespread adoption of local/distributed intelligence. To support this paradigm shift, optical networks will need to operate in a more dynamic and flexible fashion, and the control and management will need to be highly automated, programmable, and scalable. In this tutorial, we will analyze which of the 6G requirements can be supported by network automation and programmability, and what are the current developments in these areas. We will conclude by discussing the challenges that need to be addressed in the near future

The Optical RL-Gym: an open-source toolkit for applying reinforcement learning in optical networks

Reinforcement Learning (RL) is leading to important breakthroughs in several areas (e.g., self-driving vehicles, robotics, and network automation). Part of its success is due to the existence of toolkits (e.g., OpenAI Gym) to implement standard RL tasks. On the one hand, they allow for the quick implementation and testing of new ideas. On the other, these toolkits ensure easy reproducibility via quick and fair benchmarking. RL is also gaining traction in the optical networks research community, showing promising results while solving several use cases. However, there are many scenarios where the benefits of RL-based solutions remain still unclear. A possible reason for this is the steep learning curve required to tailor RL-based frameworks to each specific use case. This, in turn, might delay or even prevent the development of new ideas. This paper introduces the Optical Network Reinforcement-Learning-Gym (Optical RL-Gym), an open-source toolkit that can be used to apply RL to problems related to optical networks. The Optical RL-Gym follows the principles established by the OpenAI Gym, the de-facto standard for RL environments. Optical RL-Gym allows for the quick integration with existing RL agents, as well as the possibility to build upon several already available environments to implement and solve more elaborated use cases related to the optical networks research area. The capabilities and the benefits of the proposed toolkit are illustrated by using the Optical RL-Gym to solve two different service provisioning problems

Network automation: challenges, enablers, and benefits

Communication infrastructures are evolving towards an ad-hoc service provisioning scenario where programmability and flexibility are fundamental concepts. Network automation is expected to play a vital role in streamlining all aspects of the service provisioning process (i.e., deployment, maintenance, and tear down). However, to fully realize this autonomous operation vision, closed-loop automation procedures need to be developed.This tutorial will present the main motivations and challenges behind designing and operating closed-loop autonomous decision-making processes, including a brief overview of current standardization initiatives. The tutorial will then address several use cases showcasing how network automation can alleviate the complexity of the service provisioning processes and the benefits brought in by the introduction of network automation

Machine Learning for Cognitive Optical Network Security Management

This talk surveys the security threats pertinent to the optical network and outlines the progress and challenges in developing machine learning approaches for cognitive management of optical network security

Enhancing optical network security with machine learning

As critical communication infrastructure, optical networks have a vital role in safe and dependable transmission of massive amounts of data, supporting essential societal services. However, these networks are inherently vulnerable to a multitude of deliberate, man-made attacks targeting service disruption at the physical layer. Physical-layer attack techniques can range in their scope and effects, level of sophistication, locality, detectability, etc. An example of a relatively unsophisticated attack method is a deliberate fiber cut, typically targeting critical network elements (e.g., links with the highest betweenness) and resulting in straightforward transmission interruption [1]. More refined attack techniques rely on the insertion of harmful signal (e.g. in- and out-of-band jamming) [2], or on external tampering with the fiber to degrade the transmission quality (e.g., polarization scrambling via fiber squeezing) [3]. Diverse attack techniques cause different effects, which complicates their detectability. For example, some attacks add unfilterable noise, some reduce the power of the affected optical channels, while some inflict changes in the state of polarization too quick for the coherent receiver to compensate [3]. Therefore, monitoring only the spectrum [4], or individual signal parameters such as the power, optical signal-to-noise ratio (OSNR), or presence of errors may result in inaccurate diagnostics and root cause attribution. This obstacle in quick recovery of affected services is further pronounced for newly emerging attack techniques whose effects may deviate from the attack signatures previously known to the network management system [5].The complexity of the evolving physical-layer security landscape and the intricate interplay of different optical performance monitoring (OPM) parameters in the presence of diverse attack methods can greatly benefit from the application of machine learning techniques capable of deep data analysis. In this talk, we present how different data analytics and machine learning approaches can be applied to interpret the OPM data reported from the commercially available coherent receivers to identify anomalous operation and trigger security threat warnings. The analytical techniques are applied to experimental data obtained from an operator\u27s metropolitan testbed subjected to in- and out-of-band jamming, and external polarization scrambling attacks. We begin with an analysis of the optical signal degradation caused by the different attack methods. We then investigate the application of several supervised learning approaches that, once trained on the experimental data, can detect the presence of an attack and identify its type and intensity. The accuracy of several classifiers is scrutinized, along with the relevance of OPM parameters reported by the coherent receivers and the impact of missing features. To gain insight into the potential of the network to detect emerging, previously unseen attack techniques, we further analyse the performance of unsupervised learning techniques that detect the anomalies in signal parameters introduced by attacks. The presented findings help achieve timely and accurate detection of physical-layer attacks and serve as a prerequisite for fast and effective attack response and network recovery

Machine Learning for Optical Network Security Management

We discuss the role of supervised, unsupervised and semi-supervised learning techniques in identification of optical network security breaches. The applicability, performance and challenges related to practical deployment of these techniques are examined

Machine-Learning-as-a-Service for Optical Network Automation

MLaaS is introduced in the context of optical networks, and an architecture to take advantage of its potential is proposed.\ua0A use case of QoT classification using MLaaS techniques is benchmarked against state-of-the-art methods

Storage Protection with Connectivity and Processing Restoration for Survivable Cloud Services

The operation and management of software-based communication systems and services is a big challenge for infrastructure and service providers.The challenge is mainly associated with the larger number of configurable elements and the higher dynamicity in the software-based systems compared to the classical ones. On the other hand, the modularity and programmability in software-based networks enabled by technologies like Software Defined Networking (SDN) and Network Function Virtualization (NFV) provide new opportunities for operators to realize advanced network and service management strategies beyond the classical techniques.In our work, we elaborate on these new opportunities and propose a novel strategy for the management of survivable cloud services.In particular, we leverage the flexibility of SDN and NFV to combine proactive protection and reactive restoration mechanisms and we put forward a novel strategy for enhancing the survivability of cloud services. Through comprehensive evaluations, we demonstrate that the proposed strategy offers significant benefits in terms of availability and restorability of services while reducing, at the same time, the overhead caused by the relocation of cloud services in case of failures

Minimum Cost Design of Cellular Networks in Rural Areas with UAVs, Optical Rings, Solar Panels and Batteries

Bringing the cellular connectivity in rural zones is a big challenge, due to the large installation costs that are incurred when a legacy cellular network based on fixed Base Stations (BSs) is deployed. To tackle this aspect, we consider an alternative architecture composed of UAV-based BSs to provide cellular coverage, ground sites to connect the UAVs with the rest of the network, Solar Panels (SPs) and batteries to recharge the UAVs and to power the ground sites, and a ring of optical fiber links to connect the installed sites. We then target the minimization of the installation costs for the considered UAV-based cellular architecture, by taking into account the constraints of UAVs coverage, SPs energy consumption, levels of the batteries and the deployment of the optical ring. After providing the problem formulation, we derive an innovative methodology to ensure that a single ring of installed optical fibers is deployed. Moreover, we propose a new algorithm, called DIARIZE, to practically tackle the problem. Our results, obtained over a set of representative rural scenarios, show that DIARIZE performs very close to the optimal solution, and in general outperforms a reference design based on fixed BSs