Cold cloud climatology over Europe and the Mediterranean during the warm season from Meteosat IR imagery

Thermal infrared (IR, 10.5 – 12.5 m) images from the Meteosat Visible and Infrared Imager (MVIRI) of cold cloud episodes (cloud top brightness temperature < 241 K) are used as a proxy of precipitating clouds to derive a warm season (May-August) climatology of their coherency, duration, span, and speed over Europe and the Mediterranean. The analysis focuses over the 30°-54°N, 15°W-40°E domain in May-August 1996-2005. Harmonic analysis using discrete Fourier transforms is applied together with a statistical analysis and an investigation of the diurnal cycle. This study has the objective to make available a set of results on the propagation dynamics of the cloud systems with the aim of assist numerical modellers in improving summer convection parameterization. The zonal propagation of cold cloud systems is accompanied by a weak meridional component confined to narrow latitude belts. The persistence of cold clouds over the area evidences the role of orography, the Pyrenees, the Alps, the Balkans and Anatolia. A diurnal oscillation is found with a maximum marking the initiation of convection in the lee of the mountains and shifting from about 1400 UTC at 40°E to 1800 UTC at 0°. A moderate eastward propagation of the frequency maximum from all mountain chains across the domain exists and the diurnal maxima are completely suppressed west of 5°W. The mean power spectrum of the cold cloud frequency distribution evidences a period of one day all over Europe disappearing over the ocean (west of 10°W). Other maxima are found in correspondence of 6 to 10 days in the longitudes from 15° W to 0° and indicate the activity of the westerlies with frontal passage over the continent. Longer periods activities (from 15 up to 30 days) were stronger around 10° W and from 5° W to 15° E and are likely related to the Madden Julian Oscillation influence. The maxima of the diurnal signal are in phase with the presence of elevated terrain and with land masses. A median zonal phase speed of 16.1 ms-1 is found for all events ≥ 1000 km and ≥ 20 h and a full set of results divided by years and recurrence categories is also presented.

Automatic Deployment of Applications in the Cloud

In distributed systems like clouds or service oriented frameworks, applications are typically assembled by deploying and connecting a large number of heterogeneous software components, spanning from fine-grained packages to coarse-grained complex services. The complexity of such systems requires a rich set of techniques and tools to support the automation of their deployment process. By relying on a formal model of components, a technique is devised for computing the sequence of actions allowing the deployment of a desired configuration. An efficient algorithm, working in polynomial time, is described and proven to be sound and complete. Finally, a prototype tool implementing the proposed algorithm has been developed. Experimental results support the adoption of this novel approach in real life scenarios.

The twofold role of Cloud Computing in Digital Forensics: target of investigations and helping hand to evidence analysis

This PhD thesis discusses the impact of Cloud Computing infrastructures on Digital Forensics in the twofold role of target of investigations and as a helping hand to investigators. The Cloud offers a cheap and almost limitless computing power and storage space for data which can be leveraged to commit either new or old crimes and host related traces. Conversely, the Cloud can help forensic examiners to find clues better and earlier than traditional analysis applications, thanks to its dramatically improved evidence processing capabilities. In both cases, a new arsenal of software tools needs to be made available. The development of this novel weaponry and its technical and legal implications from the point of view of repeatability of technical assessments is discussed throughout the following pages and constitutes the unprecedented contribution of this work

Improving the cosmic distance ladder. Distance and structure of the Large Magellanic Cloud

The Large Magellanic Cloud (LMC) is widely considered as the first step of the cosmological distance ladder, since it contains many different distance indicators. An accurate determination of the distance to the LMC allows one to calibrate these distance indicators that are then used to measure the distance to far objects. The main goal of this thesis is to study the distance and structure of the LMC, as traced by different distance indicators. For these purposes three types of distance indicators were chosen: Classical Cepheids,``hot'' eclipsing binaries and RR Lyrae stars. These objects belong to different stellar populations tracing, in turn, different sub-structures of the LMC. The RR Lyrae stars (age >10 Gyr) are distributed smoothly and likely trace the halo of the LMC. Classical Cepheids are young objects (age 50-200 Myr), mainly located in the bar and spiral arm of the galaxy, while ``hot'' eclipsing binaries mainly trace the star forming regions of the LMC. Furthermore, we have chosen these distance indicators for our study, since the calibration of their zero-points is based on fundamental geometric methods. The ESA cornerstone mission Gaia, launched on 19 December 2013, will measure trigonometric parallaxes for one billion stars with an accuracy of 20 micro-arcsec at V=15 mag, and 200 micro-arcsec at V=20 mag, thus will allow us to calibrate the zero-points of Classical Cepheids, eclipsing binaries and RR Lyrae stars with an unprecedented precision.

Ocean-Atmosphere Interactions: Linking Oceanic Biological Activity to Marine Aerosol Physico-Chemical and Cloud Properties

In this work, in-situ measurements of aerosol chemical composition, particle number size distribution, cloud-relevant properties and ground-based cloud observations were combined with high-resolution satellite sea surface chlorophyll-a concentration and air mass back-trajectory data to investigate the impact of the marine biota on aerosol physico-chemical and cloud properties. Studies were performed over the North-Eastern Atlantic Ocean, the central Mediterranean Sea, and the Arctic Ocean, by deploying both multi-year datasets and short-time scale observations. All the data were chosen to be representative of the marine atmosphere, reducing to a minimum any anthropogenic input. A relationship between the patterns of marine biological activity and the time evolution of marine aerosol properties was observed, under a variety of aspects, from chemical composition to number concentration and size distribution, up to the most cloud‐relevant properties. At short-time scales (1-2 months), the aerosol properties tend to respond to biological activity variations with a delay of about one to three weeks. This delay should be considered in model applications that make use of Chlorophyll-a to predict marine aerosol properties at high temporal resolution. The impact of oceanic biological activity on the microphysical properties of marine stratiform clouds is also evidenced by our analysis, over the Eastern North Atlantic Ocean. Such clouds tend to have a higher number of smaller cloud droplets in periods of high biological activity with respect to quiescent periods. This confirms the possibility of feedback interactions within the biota-aerosol-cloud climate system. Achieving a better characterization of the time and space relationships linking oceanic biological activity to marine aerosol composition and properties may significantly impact our future capability of predicting the chemical composition of the marine atmosphere, potentially contributing to reducing the uncertainty of future climate predictions, through a better understanding of the natural climate system.

Enabling 5G and beyond: millimeter wave propagation and cloud RAN evolution

In the last few years, mobile wireless technology has gone through a revolutionary change. Web-enabled devices have evolved into essential tools for communication, information, and entertainment. The fifth generation (5G) of mobile communication networks is envisioned to be a key enabler of the next upcoming wireless revolution. Millimeter wave (mmWave) spectrum and the evolution of Cloud Radio Access Networks (C-RANs) are two of the main technological innovations of 5G wireless systems and beyond. Because of the current spectrum-shortage condition, mmWaves have been proposed for the next generation systems, providing larger bandwidths and higher data rates. Consequently, new radio channel models are being developed. Recently, deterministic ray-based models such as Ray-Tracing (RT) are getting more attractive thanks to their frequency-agility and reliable predictions. A modern RT software has been calibrated and used to analyze the mmWave channel. Knowledge of the electromagnetic properties of materials is therefore essential. Hence, an item-level electromagnetic characterization of common construction materials has been successfully achieved to obtain information about their complex relative permittivity. A complete tuning of the RT tool has been performed against indoor and outdoor measurement campaigns at 27 and 38 GHz, setting the basis for the future development of advanced beamforming techniques which rely on deterministic propagation models (as RT). C-RAN is a novel mobile network architecture which can address a number of challenges that network operators are facing in order to meet the continuous customers’ demands. C-RANs have already been adopted in advanced 4G deployments; however, there are still some issues to deal with, especially considering the bandwidth requirements set by the forthcoming 5G systems. Open RAN specifications have been proposed to overcome the new 5G challenges set on C-RAN architectures, including synchronization aspects. In this work it is described an FPGA implementation of the Synchronization Plane for an O-RAN-compliant radio system.

Interference mitigation techniques for cloud-RAN deployments in 5G and beyond systems

The deployment of ultra-dense networks is one of the most promising solutions to manage the phenomenon of co-channel interference that affects the latest wireless communication systems, especially in hotspots. To meet the requirements of the use-cases and the immense amount of traffic generated in these scenarios, 5G ultra-dense networks are being deployed using various technologies, such as distributed antenna system (DAS) and cloud-radio access network (C-RAN). Through these centralized densification schemes, virtualized baseband processing units coordinate the distributed access points and manage the available network resources. In particular, link adaptation techniques are shown to be fundamental to overall system operation and performance enhancement. The core of this dissertation is the result of an analysis and a comparison of dynamic and adaptive methods for modulation and coding scheme (MCS) selection applied to the latest mobile telecommunications standards. A novel algorithm based on the proportional-integral-derivative (PID) controller principles and block error rate (BLER) target has been proposed. Tests were conducted in a 4G and 5G system level laboratory and, by means of a channel emulator, the performance was evaluated for different channel models and target BLERs. Furthermore, due to the intrinsic sectorization of the end-users distribution in the investigated scenario, a preliminary analysis on the joint application of users grouping algorithms with multi-antenna and multi-user techniques has been performed. In conclusion, the importance and impact of other fundamental physical layer operations, such as channel estimation and power control, on the overall end-to-end system behavior and performance were highlighted.

Serverless middlewares to integrate heterogeneous and distributed services in cloud continuum environments

The application of modern ICT technologies is radically changing many fields pushing toward more open and dynamic value chains fostering the cooperation and integration of many connected partners, sensors, and devices. As a valuable example, the emerging Smart Tourism field derived from the application of ICT to Tourism so to create richer and more integrated experiences, making them more accessible and sustainable. From a technological viewpoint, a recurring challenge in these decentralized environments is the integration of heterogeneous services and data spanning multiple administrative domains, each possibly applying different security/privacy policies, device and process control mechanisms, service access, and provisioning schemes, etc. The distribution and heterogeneity of those sources exacerbate the complexity in the development of integrating solutions with consequent high effort and costs for partners seeking them. Taking a step towards addressing these issues, we propose APERTO, a decentralized and distributed architecture that aims at facilitating the blending of data and services. At its core, APERTO relies on APERTO FaaS, a Serverless platform allowing fast prototyping of the business logic, lowering the barrier of entry and development costs to newcomers, (zero) fine-grained scaling of resources servicing end-users, and reduced management overhead. APERTO FaaS infrastructure is based on asynchronous and transparent communications between the components of the architecture, allowing the development of optimized solutions that exploit the peculiarities of distributed and heterogeneous environments. In particular, APERTO addresses the provisioning of scalable and cost-efficient mechanisms targeting: i) function composition allowing the definition of complex workloads from simple, ready-to-use functions, enabling smarter management of complex tasks and improved multiplexing capabilities; ii) the creation of end-to-end differentiated QoS slices minimizing interfaces among application/service running on a shared infrastructure; i) an abstraction providing uniform and optimized access to heterogeneous data sources, iv) a decentralized approach for the verification of access rights to resources.

QoS-aware architectures, technologies, and middleware for the cloud continuum

The recent trend of moving Cloud Computing capabilities to the Edge of the network is reshaping how applications and their middleware supports are designed, deployed, and operated. This new model envisions a continuum of virtual resources between the traditional cloud and the network edge, which is potentially more suitable to meet the heterogeneous Quality of Service (QoS) requirements of diverse application domains and next-generation applications. Several classes of advanced Internet of Things (IoT) applications, e.g., in the industrial manufacturing domain, are expected to serve a wide range of applications with heterogeneous QoS requirements and call for QoS management systems to guarantee/control performance indicators, even in the presence of real-world factors such as limited bandwidth and concurrent virtual resource utilization. The present dissertation proposes a comprehensive QoS-aware architecture that addresses the challenges of integrating cloud infrastructure with edge nodes in IoT applications. The architecture provides end-to-end QoS support by incorporating several components for managing physical and virtual resources. The proposed architecture features: i) a multilevel middleware for resolving the convergence between Operational Technology (OT) and Information Technology (IT), ii) an end-to-end QoS management approach compliant with the Time-Sensitive Networking (TSN) standard, iii) new approaches for virtualized network environments, such as running TSN-based applications under Ultra-low Latency (ULL) constraints in virtual and 5G environments, and iv) an accelerated and deterministic container overlay network architecture. Additionally, the QoS-aware architecture includes two novel middlewares: i) a middleware that transparently integrates multiple acceleration technologies in heterogeneous Edge contexts and ii) a QoS-aware middleware for Serverless platforms that leverages coordination of various QoS mechanisms and virtualized Function-as-a-Service (FaaS) invocation stack to manage end-to-end QoS metrics. Finally, all architecture components were tested and evaluated by leveraging realistic testbeds, demonstrating the efficacy of the proposed solutions.

An architecture for network acceleration as a service in the cloud continuum

The pervasive availability of connected devices in any industrial and societal sector is pushing for an evolution of the well-established cloud computing model. The emerging paradigm of the cloud continuum embraces this decentralization trend and envisions virtualized computing resources physically located between traditional datacenters and data sources. By totally or partially executing closer to the network edge, applications can have quicker reactions to events, thus enabling advanced forms of automation and intelligence. However, these applications also induce new data-intensive workloads with low-latency constraints that require the adoption of specialized resources, such as high-performance communication options (e.g., RDMA, DPDK, XDP, etc.). Unfortunately, cloud providers still struggle to integrate these options into their infrastructures. That risks undermining the principle of generality that underlies the cloud computing scale economy by forcing developers to tailor their code to low-level APIs, non-standard programming models, and static execution environments. This thesis proposes a novel system architecture to empower cloud platforms across the whole cloud continuum with Network Acceleration as a Service (NAaaS). To provide commodity yet efficient access to acceleration, this architecture defines a layer of agnostic high-performance I/O APIs, exposed to applications and clearly separated from the heterogeneous protocols, interfaces, and hardware devices that implement it. A novel system component embodies this decoupling by offering a set of agnostic OS features to applications: memory management for zero-copy transfers, asynchronous I/O processing, and efficient packet scheduling. This thesis also explores the design space of the possible implementations of this architecture by proposing two reference middleware systems and by adopting them to support interactive use cases in the cloud continuum: a serverless platform and an Industry 4.0 scenario. A detailed discussion and a thorough performance evaluation demonstrate that the proposed architecture is suitable to enable the easy-to-use, flexible integration of modern network acceleration into next-generation cloud platforms.

Adaptable security in the cloud-to-thing continuum

Recent technological advancements have played a key role in seamlessly integrating cloud, edge, and Internet of Things (IoT) technologies, giving rise to the Cloud-to-Thing Continuum paradigm. This cloud model connects many heterogeneous resources that generate a large amount of data and collaborate to deliver next-generation services. While it has the potential to reshape several application domains, the number of connected entities remarkably broadens the security attack surface. One of the main problems is the lack of security measures to adapt to the dynamic and evolving conditions of the Cloud-To-Thing Continuum. To address this challenge, this dissertation proposes novel adaptable security mechanisms. Adaptable security is the capability of security controls, systems, and protocols to dynamically adjust to changing conditions and scenarios. However, since the design and development of novel security mechanisms can be explored from different perspectives and levels, we place our attention on threat modeling and access control. The contributions of the thesis can be summarized as follows. First, we introduce a model-based methodology that secures the design of edge and cyber-physical systems. This solution identifies threats, security controls, and moving target defense techniques based on system features. Then, we focus on access control management. Since access control policies are subject to modifications, we evaluate how they can be efficiently shared among distributed areas, highlighting the effectiveness of distributed ledger technologies. Furthermore, we propose a risk-based authorization middleware, adjusting permissions based on real-time data, and a federated learning framework that enhances trustworthiness by weighting each client's contributions according to the quality of their partial models. Finally, since authorization revocation is another critical concern, we present an efficient revocation scheme for verifiable credentials in IoT networks, featuring decentralization, demanding minimum storage and computing capabilities. All the mechanisms have been evaluated in different conditions, proving their adaptability to the Cloud-to-Thing Continuum landscape.