Monitoring and Prediction of Thermal Emergencies in High Performance Computing Systems

Modern scientific discoveries are driven by an unsatisfiable demand for computational resources. High-Performance Computing (HPC) systems are an aggregation of computing power to deliver considerably higher performance than one typical desktop computer can provide, to solve large problems in science, engineering, or business. An HPC room in the datacenter is a complex controlled environment that hosts thousands of computing nodes that consume electrical power in the range of megawatts, which gets completely transformed into heat. Although a datacenter contains sophisticated cooling systems, our studies indicate quantitative evidence of thermal bottlenecks in real-life production workload, showing the presence of significant spatial and temporal thermal and power heterogeneity. Therefore minor thermal issues/anomalies can potentially start a chain of events that leads to an unbalance between the amount of heat generated by the computing nodes and the heat removed by the cooling system originating thermal hazards. Although thermal anomalies are rare events, anomaly detection/prediction in time is vital to avoid IT and facility equipment damage and outage of the datacenter, with severe societal and business losses. For this reason, automated approaches to detect thermal anomalies in datacenters have considerable potential. This thesis analyzed and characterized the power and thermal characteristics of a Tier0 datacenter (CINECA) during production and under abnormal thermal conditions. Then, a Deep Learning (DL)-powered thermal hazard prediction framework is proposed. The proposed models are validated against real thermal hazard events reported for the studied HPC cluster while in production. This thesis is the first empirical study of thermal anomaly detection and prediction techniques of a real large-scale HPC system to the best of my knowledge. For this thesis, I used a large-scale dataset, monitoring data of tens of thousands of sensors for around 24 months with a data collection rate of around 20 seconds.

Business Clusters in Eastern Europe: Policy Analysis and Cluster Performance

Clusters have increasingly become an essential part of policy discourses at all levels, EU, national, regional, dealing with regional development, competitiveness, innovation, entrepreneurship, SMEs. These impressive efforts in promoting the concept of clusters on the policy-making arena have been accompanied by much less academic and scientific research work investigating the actual economic performance of firms in clusters, the design and execution of cluster policies and going beyond singular case studies to a more methodologically integrated and comparative approach to the study of clusters and their real-world impact. The theoretical background is far from being consolidated and there is a variety of methodologies and approaches for studying and interpreting this phenomenon while at the same time little comparability among studies on actual cluster performances. The conceptual framework of clustering suggests that they affect performance but theory makes little prediction as to the ultimate distribution of the value being created by clusters. This thesis takes the case of Eastern European countries for two reasons. One is that clusters, as coopetitive environments, are a new phenomenon as the previous centrally-based system did not allow for such types of firm organizations. The other is that, as new EU member states, they have been subject to the increased popularization of the cluster policy approach by the European Commission, especially in the framework of the National Reform Programmes related to the Lisbon objectives. The originality of the work lays in the fact that starting from an overview of theoretical contributions on clustering, it offers a comparative empirical study of clusters in transition countries. There have been very few examples in the literature that attempt to examine cluster performance in a comparative cross-country perspective. It adds to this an analysis of cluster policies and their implementation or lack of such as a way to analyse the way the cluster concept has been introduced to transition economies. Our findings show that the implementation of cluster policies does vary across countries with some countries which have embraced it more than others. The specific modes of implementation, however, are very similar, based mostly on soft measures such as funding for cluster initiatives, usually directed towards the creation of cluster management structures or cluster facilitators. They are essentially founded on a common assumption that the added values of clusters is in the creation of linkages among firms, human capital, skills and knowledge at the local level, most often perceived as the regional level. Often times geographical proximity is not a necessary element in the application process and cluster application are very similar to network membership. Cluster mapping is rarely a factor in the selection of cluster initiatives for funding and the relative question about critical mass and expected outcomes is not considered. In fact, monitoring and evaluation are not elements of the cluster policy cycle which have received a lot of attention. Bulgaria and the Czech Republic are the countries which have implemented cluster policies most decisively, Hungary and Poland have made significant efforts, while Slovakia and Romania have only sporadically and not systematically used cluster initiatives. When examining whether, in fact, firms located within regional clusters perform better and are more efficient than similar firms outside clusters, we do find positive results across countries and across sectors. The only country with negative impact from being located in a cluster is the Czech Republic.

Ownership, Multiple Blockholders and Performance: A study of the Indonesian Banking Industry

The objective of this study is to provide empirical evidence on how ownership structure and owner’s identity affect performance, in the banking industry by using a panel of Indonesia banks over the period 2000–2009. Firstly, we analysed the impact of the presence of multiple blockholders on bank ownership structure and performance. Building on multiple agency and principal-principal theories, we investigated whether the presence and shares dispersion across blockholders with different identities (i.e. central and regional government; families; foreign banks and financial institutions) affected bank performance, in terms of profitability and efficiency. We found that the number of blockholders has a negative effect on banks’ performance, while blockholders’ concentration has a positive effect. Moreover, we observed that the dispersion of ownership across different types of blockholders has a negative effect on banks’ performance. We interpret such results as evidence that, when heterogeneous blockholders are present, the disadvantage from conflicts of interests between blockholders seems to outweigh the advantage of the increase in additional monitoring by additional blockholder. Secondly, we conducted a joint analysis of the static, selection, and dynamic effects of different types of ownership on banks’ performance. We found that regional banks and foreign banks have a higher profitability and efficiency as compared to domestic private banks. In the short-run, foreign acquisitions and domestic M&As reduce the level of overhead costs, while in the long-run they increase the Net Interest Margin (NIM). Further, we analysed NIM determinants, to asses the impact of ownership on bank business orientation. Our findings lend support to our prediction that the NIM determinants differs accordingly to the type of bank ownership. We also observed that banks that experienced changes in ownership, such as foreign-acquired banks, manifest different interest margin determinants with respect to domestic or foreign banks that did not experience ownership rearrangements.

Mental states monitoring through passive Brain-Computer Interface systems

The monitoring of cognitive functions aims at gaining information about the current cognitive state of the user by decoding brain signals. In recent years, this approach allowed to acquire valuable information about the cognitive aspects regarding the interaction of humans with external world. From this consideration, researchers started to consider passive application of brain–computer interface (BCI) in order to provide a novel input modality for technical systems solely based on brain activity. The objective of this thesis is to demonstrate how the passive Brain Computer Interfaces (BCIs) applications can be used to assess the mental states of the users, in order to improve the human machine interaction. Two main studies has been proposed. The first one allows to investigate whatever the Event Related Potentials (ERPs) morphological variations can be used to predict the users’ mental states (e.g. attentional resources, mental workload) during different reactive BCI tasks (e.g. P300-based BCIs), and if these information can predict the subjects’ performance in performing the tasks. In the second study, a passive BCI system able to online estimate the mental workload of the user by relying on the combination of the EEG and the ECG biosignals has been proposed. The latter study has been performed by simulating an operative scenario, in which the occurrence of errors or lack of performance could have significant consequences. The results showed that the proposed system is able to estimate online the mental workload of the subjects discriminating three different difficulty level of the tasks ensuring a high reliability.

Applicazioni tecnologiche e nuovi metodi di valutazione della performance negli sport acquatici

Nello sport di alto livello l’uso della tecnologia ha raggiunto un ruolo di notevole importanza per l’analisi e la valutazione della prestazione. Negli ultimi anni sono emerse nuove tecnologie e sono migliorate quelle pre-esistenti (i.e. accelerometri, giroscopi e software per l’analisi video) in termini di campionamento, acquisizione dati, dimensione dei sensori che ha permesso la loro “indossabilità” e l’inserimento degli stessi all’interno degli attrezzi sportivi. La tecnologia è sempre stata al servizio degli atleti come strumento di supporto per raggiungere l’apice dei risultati sportivi. Per questo motivo la valutazione funzionale dell’atleta associata all’uso di tecnologie si pone lo scopo di valutare i miglioramenti degli atleti misurando la condizione fisica e/o la competenza tecnica di una determinata disciplina sportiva. L’obiettivo di questa tesi è studiare l’utilizzo delle applicazioni tecnologiche e individuare nuovi metodi di valutazione della performance in alcuni sport acquatici. La prima parte (capitoli 1-5), si concentra sulla tecnologia prototipale chiamata E-kayak e le varie applicazioni nel kayak di velocità. In questi lavori è stata verificata l’attendibilità dei dati forniti dal sistema E-kayak con i sistemi presenti in letteratura. Inoltre, sono stati indagati nuovi parametri utili a comprendere il modello di prestazione del paddler. La seconda parte (capitolo 6), si riferisce all’analisi cinematica della spinta verticale del pallanuotista, attraverso l’utilizzo della video analisi 2D, per l’individuazione delle relazioni Forza-velocità e Potenza-velocità direttamente in acqua. Questo studio pilota, potrà fornire indicazioni utili al monitoraggio e condizionamento di forza e potenza da svolgere direttamente in acqua. Infine la terza parte (capitoli 7-8), si focalizza sull’individuazione della sequenza di Fibonacci (sequenza divina) nel nuoto a stile libero e a farfalla. I risultati di questi studi suggeriscono che il ritmo di nuotata tenuto durante le medie/lunghe distanze gioca un ruolo chiave. Inoltre, il livello di autosomiglianza (self-similarity) aumenta con la tecnica del nuoto.

Metrological characterization of sensors and instrumentation for distribution grid monitoring and electrical asset diagnostics

The Smart Grid needs a large amount of information to be operated and day by day new information is required to improve the operation performance. It is also fundamental that the available information is reliable and accurate. Therefore, the role of metrology is crucial, especially if applied to the distribution grid monitoring and the electrical assets diagnostics. This dissertation aims at better understanding the sensors and the instrumentation employed by the power system operators in the above-mentioned applications and studying new solutions. Concerning the research on the measurement applied to the electrical asset diagnostics: an innovative drone-based measurement system is proposed for monitoring medium voltage surge arresters. This system is described, and its metrological characterization is presented. On the other hand, the research regarding the measurements applied to the grid monitoring consists of three parts. The first part concerns the metrological characterization of the electronic energy meters’ operation under off-nominal power conditions. Original test procedures have been designed for both frequency and harmonic distortion as influence quantities, aiming at defining realistic scenarios. The second part deals with medium voltage inductive current transformers. An in-depth investigation on their accuracy behavior in presence of harmonic distortion is carried out by applying realistic current waveforms. The accuracy has been evaluated by means of the composite error index and its approximated version. Based on the same test setup, a closed-form expression for the measured current total harmonic distortion uncertainty estimation has been experimentally validated. The metrological characterization of a virtual phasor measurement unit is the subject of the third and last part: first, a calibrator has been designed and the uncertainty associated with its steady-state reference phasor has been evaluated; then this calibrator acted as a reference, and it has been used to characterize the phasor measurement unit implemented within a real-time simulator.

Qualification of optical fiber sensors for the structural health monitoring of aerospace structures

In recent years, composite materials have revolutionized the design of many structures. Their superior mechanical properties and light weight make composites convenient over traditional metal structures for many applications. However, composite materials are susceptible to complex and challenging to predict damage behaviors due to their anisotropy nature. Therefore, structural Health Monitoring (SHM) can be a valuable tool to assess the damage and understand the physics underneath. Distributed Optical Fiber Sensors (DOFS) can be used to monitor several types of damage in composites. However, their implementation outside academia is still unsatisfactory. One of the hindrances is the lack of a rigorous methodology for uncertainty quantification, which is essential for the performance assessment of the monitoring system. The concept of Probability of Detection (POD) must function as the guiding light in this process. However, precautions must be taken since this tool was established for Non-Destructive Evaluation (NDE) rather than Structural Health Monitoring (SHM). In addition, although DOFS have been the object of numerous studies, a well-established POD methodology for their performance assessment is still missing. This thesis aims to develop a methodology to produce POD curves for DOFS in composite materials. The problem is analyzed considering several critical points, such as the strain transfer characterizing the DOFS and the development of an experimental and model-assisted methodology to understand the parameters that affect the DOFS performance.

Towards a sustainable approach for Cultural Heritage Conservation: developing micro-structured green materials and analytical protocols for their performance assessment

Cleaning is one of the most important and delicate procedures that are part of the restoration process. When developing new systems, it is fundamental to consider its selectivity towards the layer to-be-removed, non-invasiveness towards the one to-be-preserved, its sustainability and non-toxicity. Besides assessing its efficacy, it is important to understand its mechanism by analytical protocols that strike a balance between cost, practicality, and reliable interpretation of results. In this thesis, the development of cleaning systems based on the coupling of electrospun fabrics (ES) and greener organic solvents is proposed. Electrospinning is a versatile technique that allows the production of micro/nanostructured non-woven mats, which have already been used as absorbents in various scientific fields, but to date, not in the restoration field. The systems produced proved to be effective for the removal of dammar varnish from paintings, where the ES not only act as solvent-binding agents but also as adsorbents towards the partially solubilised varnish due to capillary rise, thus enabling a one-step procedure. They have also been successfully applied for the removal of spray varnish from marble substrates and wall paintings. Due to the materials' complexity, the procedure had to be adapted case-by-case and mechanical action was still necessary. According to the spinning solution, three types of ES mats have been produced: polyamide 6,6, pullulan and pullulan with melanin nanoparticles. The latter, under irradiation, allows for a localised temperature increase accelerating and facilitating the removal of less soluble layers (e.g. reticulated alkyd-based paints). All the systems produced, and the mock-ups used were extensively characterised using multi-analytical protocols. Finally, a monitoring protocol and image treatment based on photoluminescence macro-imaging is proposed. This set-up allowed the study of the removal mechanism of dammar varnish and semi-quantify its residues. These initial results form the basis for optimising the acquisition set-up and data processing.

Hybrid ground-aerial mesh networks for IoT monitoring applications: network design and software platform development

The Internet of Things (IoT) has grown rapidly in recent years, leading to an increased need for efficient and secure communication between connected devices. Wireless Sensor Networks (WSNs) are composed of small, low-power devices that are capable of sensing and exchanging data, and are often used in IoT applications. In addition, Mesh WSNs involve intermediate nodes forwarding data to ensure more robust communication. The integration of Unmanned Aerial Vehicles (UAVs) in Mesh WSNs has emerged as a promising solution for increasing the effectiveness of data collection, as UAVs can act as mobile relays, providing extended communication range and reducing energy consumption. However, the integration of UAVs and Mesh WSNs still poses new challenges, such as the design of efficient control and communication strategies. This thesis explores the networking capabilities of WSNs and investigates how the integration of UAVs can enhance their performance. The research focuses on three main objectives: (1) Ground Wireless Mesh Sensor Networks, (2) Aerial Wireless Mesh Sensor Networks, and (3) Ground/Aerial WMSN integration. For the first objective, we investigate the use of the Bluetooth Mesh standard for IoT monitoring in different environments. The second objective focuses on deploying aerial nodes to maximize data collection effectiveness and QoS of UAV-to-UAV links while maintaining the aerial mesh connectivity. The third objective investigates hybrid WMSN scenarios with air-to-ground communication links. One of the main contribution of the thesis consists in the design and implementation of a software framework called "Uhura", which enables the creation of Hybrid Wireless Mesh Sensor Networks and abstracts and handles multiple M2M communication stacks on both ground and aerial links. The operations of Uhura have been validated through simulations and small-scale testbeds involving ground and aerial devices.

Sensor networks optimization and software development for Structural Health Monitoring based on ultrasonic guided waves

The Structural Health Monitoring (SHM) research area is increasingly investigated due to its high potential in reducing the maintenance costs and in ensuring the systems safety in several industrial application fields. A growing demand of new SHM systems, permanently embedded into the structures, for savings in weight and cabling, comes from the aeronautical and aerospace application fields. As consequence, the embedded electronic devices are to be wirelessly connected and battery powered. As result, a low power consumption is requested. At the same time, high performance in defects or impacts detection and localization are to be ensured to assess the structural integrity. To achieve these goals, the design paradigms can be changed together with the associate signal processing. The present thesis proposes design strategies and unconventional solutions, suitable both for real-time monitoring and periodic inspections, relying on piezo-transducers and Ultrasonic Guided Waves. In the first context, arrays of closely located sensors were designed, according to appropriate optimality criteria, by exploiting sensors re-shaping and optimal positioning, to achieve improved damages/impacts localisation performance in noisy environments. An additional sensor re-shaping procedure was developed to tackle another well-known issue which arises in realistic scenario, namely the reverberation. A novel sensor, able to filter undesired mechanical boundaries reflections, was validated via simulations based on the Green's functions formalism and FEM. In the active SHM context, a novel design methodology was used to develop a single transducer, called Spectrum-Scanning Acoustic Transducer, to actively inspect a structure. It can estimate the number of defects and their distances with an accuracy of 2[cm]. It can also estimate the damage angular coordinate with an equivalent mainlobe aperture of 8[deg], when a 24[cm] radial gap between two defects is ensured. A suitable signal processing was developed in order to limit the computational cost, allowing its use with embedded electronic devices.

Study of the calorimetric performance of a liquid Argon detector based on a novel optical imaging concept

DUNE is a next-generation long-baseline neutrino oscillation experiment. It aims to measure the still unknown $ \delta_{CP} $ violation phase and the sign of $ \Delta m_{13}^2 $, which defines the neutrino mass ordering. DUNE will exploit a Far Detector composed of four multi-kiloton LArTPCs, and a Near Detector (ND) complex located close to the neutrino source at Fermilab. The SAND detector at the ND complex is designed to perform on-axis beam monitoring, constrain uncertainties in the oscillation analysis and perform precision neutrino physics measurements. SAND includes a 0.6 T super-conductive magnet, an electromagnetic calorimeter, a 1-ton liquid Argon detector - GRAIN - and a modular, low-density straw tube target tracker system. GRAIN is an innovative LAr detector where neutrino interactions can be reconstructed using only the LAr scintillation light imaged by an optical system based on Coded Aperture masks and lenses - a novel approach never used before in particle physics applications. In this thesis, a first evaluation of GRAIN track reconstruction and calorimetric capabilities was obtained with an optical system based on Coded Aperture cameras. A simulation of $\nu_\mu + Ar$ interactions with the energy spectrum expected at the future Fermilab Long Baseline Neutrino Facility (LBNF) was performed. The performance of SAND was evaluated, combining the information provided by all its sub-detectors, on the selection of $ \nu_\mu + Ar \to \mu^- + p + X $ sample and on the neutrino energy reconstruction.