Engineering software for next-generation networks in a sustainable way.

The virtualization and softwarization of network functions is the networking industry's latest achievement. Software-Defined Networks (SDN) and Network Function Virtualization (NFV) propose novel software architectures and development process adapted to for instance mobile networks (e.g., 6G). However, these architectures and processes are mainly defined by the telecommunications community, without much regard for the contributions of software engineering to generic software processes. This paper explores how the fields of software engineering (SE) and telecommunications can work together to improve service virtualization, cloud computing, and edge computing in the context of next-generation networks. It also highlights the potential of SE fields like software architecture, variability, and configuration to greatly enhance the development of virtual network functions (VNFs). On the other hand, the new contributions should be energy efficient, since this is a primary goal in next-gen networks. Finally, current software processes should consider the impact of communication networks on the correct functioning of software products, since network functioning can affect the QoE of users.Work supported by the projects \emph{IRIS} PID2021-122812OB-I00 (co-financed by FEDER funds), and \emph{DAEMON} H2020-101017109; and by Universidad de Málaga

A Survey on Explainable AI for 6G O-RAN: Architecture, Use Cases, Challenges and Research Directions

The recent O-RAN specifications promote the evolution of RAN architecture by function disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop control architecture managed by RAN Intelligent Controllers (RICs) entities. This paves the road to novel data-driven network management approaches based on programmable logic. Aided by Artificial Intelligence (AI) and Machine Learning (ML), novel solutions targeting traditionally unsolved RAN management issues can be devised. Nevertheless, the adoption of such smart and autonomous systems is limited by the current inability of human operators to understand the decision process of such AI/ML solutions, affecting their trust in such novel tools. eXplainable AI (XAI) aims at solving this issue, enabling human users to better understand and effectively manage the emerging generation of artificially intelligent schemes, reducing the human-to-machine barrier. In this survey, we provide a summary of the XAI methods and metrics before studying their deployment over the O-RAN Alliance RAN architecture along with its main building blocks. We then present various use-cases and discuss the automation of XAI pipelines for O-RAN as well as the underlying security aspects. We also review some projects/standards that tackle this area. Finally, we identify different challenges and research directions that may arise from the heavy adoption of AI/ML decision entities in this context, focusing on how XAI can help to interpret, understand, and improve trust in O-RAN operational networks.Comment: 33 pages, 13 figure

Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case

Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges in volume and variety. In that context, distributed computing solutions such as cloud systems are leveraged to parallelise the data processing and reduce computation time. As the cloud systems become increasingly popular, there is increased demand that more users that were originally not cloud experts (such as data scientists, domain experts) deploy their solutions on the cloud systems. However, it is non-trivial to address both the high demand for cloud system users and the excessive time required to train them. To this end, we propose SemCloud, a semantics-enhanced cloud system, that couples cloud system with semantic technologies and machine learning. SemCloud relies on domain ontologies and mappings for data integration, and parallelises the semantic data integration and data analysis on distributed computing nodes. Furthermore, SemCloud adopts adaptive Datalog rules and machine learning for automated resource configuration, allowing non-cloud experts to use the cloud system. The system has been evaluated in industrial use case with millions of data, thousands of repeated runs, and domain users, showing promising results.Comment: Paper accepted at ISWC2023 In-Use trac

Applying Formal Methods to Networking: Theory, Techniques and Applications

Despite its great importance, modern network infrastructure is remarkable for the lack of rigor in its engineering. The Internet which began as a research experiment was never designed to handle the users and applications it hosts today. The lack of formalization of the Internet architecture meant limited abstractions and modularity, especially for the control and management planes, thus requiring for every new need a new protocol built from scratch. This led to an unwieldy ossified Internet architecture resistant to any attempts at formal verification, and an Internet culture where expediency and pragmatism are favored over formal correctness. Fortunately, recent work in the space of clean slate Internet design---especially, the software defined networking (SDN) paradigm---offers the Internet community another chance to develop the right kind of architecture and abstractions. This has also led to a great resurgence in interest of applying formal methods to specification, verification, and synthesis of networking protocols and applications. In this paper, we present a self-contained tutorial of the formidable amount of work that has been done in formal methods, and present a survey of its applications to networking.Comment: 30 pages, submitted to IEEE Communications Surveys and Tutorial

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented

Toward Automated Network Management and Operations.

Network management plays a fundamental role in the operation and well-being of today's networks. Despite the best effort of existing support systems and tools, management operations in large service provider and enterprise networks remain mostly manual. Due to the larger scale of modern networks, more complex network functionalities, and higher network dynamics, human operators are increasingly short-handed. As a result, network misconfigurations are frequent, and can result in violated service-level agreements and degraded user experience. In this dissertation, we develop various tools and systems to understand, automate, augment, and evaluate network management operations. Our thesis is that by introducing formal abstractions, like deterministic finite automata, Petri-Nets and databases, we can build new support systems that systematically capture domain knowledge, automate network management operations, enforce network-wide properties to prevent misconfigurations, and simultaneously reduce manual effort. The theme for our systems is to build a knowledge plane based on the proposed abstractions, allowing network-wide reasoning and guidance for network operations. More importantly, the proposed systems require no modification to the existing Internet infrastructure and network devices, simplifying adoption. We show that our systems improve both timeliness and correctness in performing realistic and large-scale network operations. Finally, to address the current limitations and difficulty of evaluating novel network management systems, we have designed a distributed network testing platform that relies on network and device virtualization to provide realistic environments and isolation to production networks.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78837/1/chenxu_1.pd

Machine Learning Techniques for Stellar Light Curve Classification

We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time series data. We preprocessed over 94 GB of Kepler light curves from MAST to classify according to ten distinct physical properties using both representation learning and feature engineering approaches. Studies using machine learning in the field have been primarily done on simulated data, making our study one of the first to use real light curve data for machine learning approaches. We tuned our data using previous work with simulated data as a template and achieved mixed results between the two approaches. Representation learning using a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) produced no successful predictions, but our work with feature engineering was successful for both classification and regression. In particular, we were able to achieve values for stellar density, stellar radius, and effective temperature with low error (~ 2 - 4%) and good accuracy (~ 75%) for classifying the number of transits for a given star. The results show promise for improvement for both approaches upon using larger datasets with a larger minority class. This work has the potential to provide a foundation for future tools and techniques to aid in the analysis of astrophysical data.Comment: Accepted to The Astronomical Journa

Generating semantically enriched diagnostics for radiological images using machine learning

Development of Computer Aided Diagnostic (CAD) tools to aid radiologists in pathology detection and decision making relies considerably on manually annotated images. With the advancement of deep learning techniques for CAD development, these expert annotations no longer need to be hand-crafted, however, deep learning algorithms require large amounts of data in order to generalise well. One way in which to access large volumes of expert-annotated data is through radiological exams consisting of images and reports. Using past radiological exams obtained from hospital archiving systems has many advantages: they are expert annotations available in large quantities, covering a population-representative variety of pathologies, and they provide additional context to pathology diagnoses, such as anatomical location and severity. Learning to auto-generate such reports from images presents many challenges such as the difficulty in representing and generating long, unstructured textual information, accounting for spelling errors and repetition or redundancy, and the inconsistency across different annotators. In this thesis, the problem of learning to automate disease detection from radiological exams is approached from three directions. Firstly, a report generation model is developed such that it is conditioned on radiological image features. Secondly, a number of approaches are explored aimed at extracting diagnostic information from free-text reports. Finally, an alternative approach to image latent space learning from current state-of-the-art is developed that can be applied to accelerated image acquisition.Open Acces

Relational Approach to Knowledge Engineering for POMDP-based Assistance Systems as a Translation of a Psychological Model

Assistive systems for persons with cognitive disabilities (e.g. dementia) are difficult to build due to the wide range of different approaches people can take to accomplishing the same task, and the significant uncertainties that arise from both the unpredictability of client's behaviours and from noise in sensor readings. Partially observable Markov decision process (POMDP) models have been used successfully as the reasoning engine behind such assistive systems for small multi-step tasks such as hand washing. POMDP models are a powerful, yet flexible framework for modelling assistance that can deal with uncertainty and utility. Unfortunately, POMDPs usually require a very labour intensive, manual procedure for their definition and construction. Our previous work has described a knowledge driven method for automatically generating POMDP activity recognition and context sensitive prompting systems for complex tasks. We call the resulting POMDP a SNAP (SyNdetic Assistance Process). The spreadsheet-like result of the analysis does not correspond to the POMDP model directly and the translation to a formal POMDP representation is required. To date, this translation had to be performed manually by a trained POMDP expert. In this paper, we formalise and automate this translation process using a probabilistic relational model (PRM) encoded in a relational database. We demonstrate the method by eliciting three assistance tasks from non-experts. We validate the resulting POMDP models using case-based simulations to show that they are reasonable for the domains. We also show a complete case study of a designer specifying one database, including an evaluation in a real-life experiment with a human actor

Threat Landscape and Good Practice Guide for Software Defined Networks/5G

5G represents the next major phase of mobile telecommunication systems and network architectures beyond the current 4G standards, aiming at extreme broadband and ultra-robust, low latency connectivity, to enable the programmable connectivity for the Internet of Everything2. Despite the significant debate on the technical specifications and the technological maturity of 5G, which are under discussion in various fora3, 5G is expected to affect positively and significantly several industry sectors ranging from ICT to industry sectors such as car and other manufacturing, health and agriculture in the period up to and beyond 2020. 5G will be driven by the influence of software on network functions, known as Software Defined Networking (SDN) and Network Function Virtualization (NFV). The key concept that underpins SDN is the logical centralization of network control functions by decoupling the control and packet forwarding functionality of the network. NFV complements this vision through the virtualization of these functionalities based on recent advances in general server and enterprise IT virtualization. Considering the technological maturity of the technologies that 5G can leverage on, SDN is the one that is moving faster from development to production. To realize the business potential of SDN/5G, a number of technical issues related to the design and operation of Software Defined Networks need to be addressed. Amongst them, SDN/5G security is one of the key issues, that needs to be addressed comprehensively in order to avoid missing the business opportunities arising from SDN/5G. In this report, we review threats and potential compromises related to the security of SDN/5G networks. More specifically, this report contains a review of the emerging threat landscape of 5G networks with particular focus on Software Defined Networking. It also considers security of NFV and radio network access. To provide a comprehensive account of the emerging threat SDN/5G landscape, this report has identified related network assets and the security threats, challenges and risks arising for these assets. Driven by the identified threats and risks, this report has also reviewed and identified existing security mechanisms and good practices for SDN/5G/NFV, and based on these it has analysed gaps and provided technical, policy and organizational recommendations for proactively enhancing the security of SDN/5G