Modern networks provide a variety of interrelated services including routing, traffic monitoring, load balancing, and access control. Unfortunately, the languages used to program today's networks lack modern features - they are usually defined at the low level of abstraction supplied by the underlying hardware and they fail to provide even rudimentary support for modular programming. As a result, network programs tend to be complicated, error-prone, and difficult to maintain.This paper presents Frenetic, a high-level language for programming distributed collections of network switches. Frenetic provides a declarative query language for classifying and aggregating network traffic as well as a functional reactive combinator library for describing high-level packet-forwarding policies. Unlike prior work in this domain, these constructs are - by design - fully compositional, which facilitates modular reasoning and enables code reuse. This important property is enabled by Frenetic's novel run-time system which manages all of the details related to installing, uninstalling, and querying low-level packet-processing rules on physical switches.Overall, this paper makes three main contributions: (1) We analyze the state-of-the art in languages for programming networks and identify the key limitations; (2) We present a language design that addresses these limitations, using a series of examples to motivate and validate our choices; (3) We describe an implementation of the language and evaluate its performance on several benchmarks.

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Frenetic: a network programming language

Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research.

Bayesian Networks in Fault Diagnosis

The explosive rise of Internet of Things (IoT) systems have notably increased the potential attack surfaces for cybercriminals. Accounting for the features and constraints of IoT devices, traditional security countermeasures can be inefficient in dynamic IoT environments. In this vein, the advantages introduced by software defined networking (SDN) and network function virtualization (NFV) have the potential to reshape the landscape of cybersecurity for IoT systems. To this aim, we provide a comprehensive analysis of security features introduced by NFV and SDN, describing the manifold strategies able to monitor, protect, and react to IoT security threats. We also present lessons learned in the adoption of SDN/NFV-based protection approaches in IoT environments, comparing them with conventional security countermeasures. Finally, we deeply discuss the open challenges related to emerging SDN- and NFV-based security mechanisms, aiming to provide promising directives to conduct future research in this fervent area.

A Survey on Emerging SDN and NFV Security Mechanisms for IoT Systems

Internet of things (IoT) facilitates billions of devices to be enabled with network connectivity to collect and exchange real-time information for providing intelligent services. Thus, IoT allows connected devices to be controlled and accessed remotely in the presence of adequate network infrastructure. Unfortunately, traditional network technologies such as enterprise networks and classic timeout-based transport protocols are not capable of handling such requirements of IoT in an efficient, scalable, seamless, and cost-effective manner. Besides, the advent of software-defined networking (SDN) introduces features that allow the network operators and users to control and access the network devices remotely, while leveraging the global view of the network. In this respect, we provide a comprehensive survey of different SDN-based technologies, which are useful to fulfill the requirements of IoT, from different networking aspects— edge , access , core , and data center networking. In these areas, the utility of SDN-based technologies is discussed, while presenting different challenges and requirements of the same in the context of IoT applications. We present a synthesized overview of the current state of IoT development. We also highlight some of the future research directions and open research issues based on the limitations of the existing SDN-based technologies.

Software-Defined Networking for Internet of Things: A Survey

Digital twin is a significant way to achieve smart manufacturing, and provides a new paradigm for fault diagnosis. Traditional data-based fault diagnosis methods mostly assume that the training data and test data are following the same distribution and can acquire sufficient data to train a reliable diagnosis model, which is unrealistic in the dynamic changing production process. In this paper, we present a two-phase digital-twin-assisted fault diagnosis method using deep transfer learning (DFDD), which realizes fault diagnosis both in the development and maintenance phases. At first, the potential problems that are not considered at design time can be discovered through front running the ultra-high-fidelity model in the virtual space, while a deep neural network (DNN)-based diagnosis model will be fully trained. In the second phase, the previously trained diagnosis model can be migrated from the virtual space to physical space using deep transfer learning for real-time monitoring and predictive maintenance. This ensures the accuracy of the diagnosis as well as avoids wasting time and knowledge. A case study about fault diagnosis using DFDD in a car body-side production line is presented. The results show the superiority and feasibility of our proposed method.

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A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning

We propose a new WDM passive optical-packet ring design for metropolitan applications. We argue the network performance and physical feasibility of such a network with up 160 Gbit/s capacity and 210 km of perimeter.

A novel, low cost optical packet metropolitan ring architecture

Fault diagnosis is a critical task for operators in the context of e-TOM (enhanced Telecom Operations Map) assurance process. Its purpose is to reduce network maintenance costs and to improve availability, reliability and performance of network services. Although necessary, this operation is complex and requires significant involvement of human expertise. The study of the fundamental properties of fault diagnosis shows that the diagnosis process complexity needs to be addressed using more intelligent and efficient approaches. In this paper, we present a hybrid approach that combines Bayesian networks and case-based reasoning in order to overcome the usual limits of fault diagnosis techniques and to reduce human intervention in this process. The proposed mechanism allows the identification of the root cause with a finer precision and a higher reliability. At the same time, it helps to reduce computation time while taking into account the network dynamicity. Furthermore, a study case is presented to show the feasibility and performance of the proposed approach based on a real-world use case: a virtual private network topology.

Self-Diagnosis Technique for Virtual Private Networks Combining Bayesian Networks and Case-Based Reasoning

Autonomic systems were first described in 2001. The fundamental goal
is self-management, including self-configuration, self-optimization,
self-healing, and self-protection. This is achieved by an autonomic
function having minimal dependencies on human administrators or
centralized management systems. It usually implies distribution across
network elements. This document defines common language and outlines
design goals (and what are not design goals) for autonomic functions.
A high-level reference model illustrates how functional elements in an
Autonomic Network interact. This document is a product of the
IRTF's Network Management Research Group.

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Autonomic Networking: Definitions and Design Goals

Intent and Intent-Based Networking (IBN) are taking the industry by
storm. At the same time, those terms are used loosely and often
inconsistently, in many cases overlapping and confused with other
concepts such as "Policy". This document clarifies the concept of
"Intent" and provides an overview of functionality that is associated
with it. The goal is to contribute towards a common and shared
understanding of terms, concepts, and functionality that can be used
as foundation to guide further definition of associated research and
engineering problems and their solutions.

Intent-Based Networking - Concepts and Definitions

In this paper, we propose a solution to delegate the control and the management of the network connecting the many devices of a smart environment to a software entity, while keeping end-users in control of what is happening in their networks. For this, we rely on the logical manipulation of all connected devices through device abstraction and network programmability. Applying Software Defined Networking (SDN) principles, we propose a software-based solution that we call Software-Defined LANs in order to interconnect devices of smart environments according to the services the users are requesting or expecting.We define the adequate virtualization framework based on Virtual Objects and Communities of Virtual Objects. Using these virtual entities, we apply the SDN architectural principles to define a generic architecture that can be applied to any smart environment. Then we describe a prototype implementing these concepts in the home networking context, through a scenario in which users of two different homes can easily interconnect two private but shareable DLNA devices in a dedicated videodelivery SD-LAN. Finally we provide a discussion of the benefits and challenges of our approach regarding the generalization of SDN principles, autonomic features, Internet of Things scalability, security and privacy aspects enabled by SD-LANs intrinsic properties.

Laurent Ciavaglia

Papers

A novel, low cost optical packet metropolitan ring architecture

Self-Diagnosis Technique for Virtual Private Networks Combining Bayesian Networks and Case-Based Reasoning

Autonomic Networking: Definitions and Design Goals

Intent-Based Networking - Concepts and Definitions

Software-Defined LANs for Interconnected Smart Environment