Interoperable Resource Brokering with Policy-based Provisioning and Job Allocation

The increasing needs for computational power in areas such as weather simulation, genomics or Internet applications have led to sharing of geographically distributed and heterogeneous resources from commercial data centers and scientific institutions. Research in the areas of utility, grid and cloud computing, together with improvements in network and hardware virtualization has resulted in methods to locate and use resources to rapidly provision virtual environments in a flexible manner, while lowering costs for consumers and providers. However, there is still a lack of methodologies to enable efficient and seamless sharing of resources among institutions. In this work, we concentrate in the problem of executing parallel scientific applications across distributed resources belonging to separate organizations. Our approach can be divided in three main points. First, we define and implement an interoperable grid protocol to distribute job workloads among partners with different middleware and execution resources. Second, we research and implement different policies for virtual resource provisioning and job-to-resource allocation, taking advantage of their cooperation to improve execution cost and performance. Third, we explore the consequences of on-demand provisioning and allocation in the problem of site-selection for the execution of parallel workloads, and propose new strategies to reduce job slowdown and overall cost.

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.

Towards near-threshold server processors

The popularity of cloud computing has led to a dramatic increase in the number of data centers in the world. The ever-increasing computational demands along with the slowdown in technology scaling has ushered an era of power-limited servers. Techniques such as near-threshold computing (NTC) can be used to improve energy efficiency in the post-Dennard scaling era. This paper describes an architecture based on the FD-SOI process technology for near-threshold operation in servers. Our work explores the trade-offs in energy and performance when running a wide range of applications found in private and public clouds, ranging from traditional scale-out applications, such as web search or media streaming, to virtualized banking applications. Our study demonstrates the benefits of near-threshold operation and proposes several directions to synergistically increase the energy proportionality of a near-threshold server.

10th European Conference on Information Systems Management

The 10th European Conference on Information Systems Management is being held at The University of Evora, Portugal on the 8 /9 September 2016. The Conference Chair is Paulo Silva and the Programme Chairs are Prof. Rui Quaresma and Prof. António Guerreiro. ECISM provides an opportunity for individuals researching and working in the broad field of information systems management, including IT evaluation to come together to exchange ideas and discuss current research in the field. This has developed into a particularly important forum for the present era, where the modern challenges of managing information and evaluating the effectiveness of related technologies are constantly evolving in the world of Big Data and Cloud Computing. We hope that this year’s conference will provide you with plenty of opportunities to share your expertise with colleagues from around the world. The keynote speakers for the Conference are Carlos Zorrinho from the Portuguese Delegation and Isabel Ramos from University of Minho, Portugal. ECISM 2016 received an initial submission of 84 abstracts. After the double blind peer review process 25 aca demic papers, 7 PhD research papers, 3 Masters research paper and 5 work in progress papers have been ac cepted for publication in these Conference Proceedings. These papers represent research from around the world, including Belgium, Brazil, China, Czech Republic, Kazakhstan, Malaysia, New Zealand, Norway, Oman, Poland, Portugal, South Africa, Sweden, The Netherlands, UK and Vietnam.

Traffic Light Using Multiple Wireless Technologies

This paper presents the study and experimental tests for the viability analysis of using multiple wireless technologies in urban traffic light controllers in a Smart City environment. Communication drivers, different types of antennas, data acquisition methods and data processing for monitoring the network are presented. The sensors and actuators modules are connected in a local area network through two distinct low power wireless networks using both 868 MHz and 2.4 GHz frequency bands. All data communications using 868 MHz go through a Moteino. Various tests are made to assess the most advantageous features of each communication type. The experimental results show better range for 868 MHz solutions, whereas the 2.4 GHz presents the advantage of self-regenerating the network and mesh. The different pros and cons of both communication methods are presented.

A distributed middleware for IT/OT convergence in modern industrial environments

The modern industrial environment is populated by a myriad of intelligent devices that collaborate for the accomplishment of the numerous business processes in place at the production sites. The close collaboration between humans and work machines poses new interesting challenges that industry must overcome in order to implement the new digital policies demanded by the industrial transition. The Industry 5.0 movement is a companion revolution of the previous Industry 4.0, and it relies on three characteristics that any industrial sector should have and pursue: human centrality, resilience, and sustainability. The application of the fifth industrial revolution cannot be completed without moving from the implementation of Industry 4.0-enabled platforms. The common feature found in the development of this kind of platform is the need to integrate the Information and Operational layers. Our thesis work focuses on the implementation of a platform addressing all the digitization features foreseen by the fourth industrial revolution, making the IT/OT convergence inside production plants an improvement and not a risk. Furthermore, we added modular features to our platform enabling the Industry 5.0 vision. We favored the human centrality using the mobile crowdsensing techniques and the reliability and sustainability using pluggable cloud computing services, combined with data coming from the crowd support. We achieved important and encouraging results in all the domains in which we conducted our experiments. Our IT/OT convergence-enabled platform exhibits the right performance needed to satisfy the strict requirements of production sites. The multi-layer capability of the framework enables the exploitation of data not strictly coming from work machines, allowing a more strict interaction between the company, its employees, and customers.

Cross-sensors analysis of multispectral Earth observation data for environmental monitoring

Time Series Analysis of multispectral satellite data offers an innovative way to extract valuable information of our changing planet. This is now a real option for scientists thanks to data availability as well as innovative cloud-computing platforms, such as Google Earth Engine. The integration of different missions would mitigate known issues in multispectral time series construction, such as gaps due to clouds or other atmospheric effects. With this purpose, harmonization among Landsat-like missions is possible through statistical analysis. This research offers an overview of the different instruments from Landsat and Sentinel missions (TM, ETM, OLI, OLI-2 and MSI sensors) and products levels (Collection-2 Level-1 and Surface Reflectance for Landsat and Level-1C and Level-2A for Sentinel-2). Moreover, a cross-sensors comparison was performed to assess the interoperability of the sensors on-board Landsat and Sentinel-2 constellations, having in mind a possible combined use for time series analysis. Firstly, more than 20,000 pairs of images almost simultaneously acquired all over Europe were selected over a period of several years. The study performed a cross-comparison analysis on these data, and provided an assessment of the calibration coefficients that can be used to minimize differences in the combined use. Four of the most popular vegetation indexes were selected for the study: NDVI, EVI, SAVI and NDMI. As a result, it is possible to reconstruct a longer and denser harmonized time series since 1984, useful for vegetation monitoring purposes. Secondly, the spectral characteristics of the recent Landsat-9 mission were assessed for a combined use with Landsat-8 and Sentinel-2. A cross-sensor analysis of common bands of more than 3,000 almost simultaneous acquisitions verified a high consistency between datasets. The most relevant discrepancy has been observed in the blue and SWIRS bands, often used in vegetation and water related studies. This analysis was supported with spectroradiometer ground measurements.

Message-oriented Middleware per Routing Acceleration basato su eBPF/XDP in Contesto Serverless

Negli ultimi anni la necessità di processare e mantenere dati di qualsiasi natura è aumentata considerevolmente, in aggiunta a questo, l’obsolescenza del modello centralizzato ha contribuito alla sempre più frequente adozione del modello distribuito. Inevitabile dunque l’aumento di traffico che attraversa i nodi appartenenti alle infrastrutture, un traffico sempre più in aumento e che con l’avvento dell’IoT, dei Big Data, del Cloud Computing, del Serverless Computing etc., ha raggiunto picchi elevatissimi. Basti pensare che se prima i dati erano contenuti in loco, oggi non è assurdo pensare che l’archiviazione dei propri dati sia completamente affidata a terzi. Così come cresce, quindi, il traffico che attraversa i nodi facenti parte di un’infrastruttura, cresce la necessità che questo traffico sia filtrato e gestito dai nodi stessi. L’obbiettivo di questa tesi è quello di estendere un Message-oriented Middleware, in grado di garantire diverse qualità di servizio per la consegna di messaggi, in modo da accelerarne la fase di routing verso i nodi destinazione. L’estensione consiste nell’aggiungere al Message-oriented Middleware, precedentemente implementato, la funzione di intercettare i pacchetti in arrivo (che nel caso del middleware in questione possono rappresentare la propagazione di eventi) e redirigerli verso un nuovo nodo in base ad alcuni parametri. Il Message-oriented Middleware oggetto di tesi sarà considerato il message broker di un modello pub/sub, pertanto la redirezione deve avvenire con tempi molto bassi di latenza e, a tal proposito, deve avvenire senza l’uscita dal kernel space del sistema operativo. Per questo motivo si è deciso di utilizzare eBPF, in particolare il modulo XDP, che permette di scrivere programmi che eseguono all’interno del kernel.

Trasformazione digitale nei processi di misura e fatturazione - il caso azienda Alfa

Con il lancio di nuove applicazioni tecnologiche come l'Internet of Things, Big Data, Cloud computing e tecnologie mobili che stanno accelerando in maniera spropositata la velocità di cambiamento, i comportamenti, le abitudini e i modi di vivere sono completamente mutati nel favorire un mondo di tecnologie digitali che agevolino le operazioni quotidiane. Questi progressi stanno velocemente cambiando il modo in cui le aziende fanno business, con grandi ripercussioni in tutto quello che è il contesto aziendale, ma non solo. L’avvento della Digital Transformation ha incrementato questi fenomeni e la si potrebbe definire come causa scatenante di tutti i mutamenti che stiamo vivendo. La velocità e l’intensità del cambiamento ha effetti disruptive rispetto al passato, colpendo numerosi settori economici ed abitudini dei consumatori. L’obiettivo di questo elaborato è di analizzare la trasformazione digitale applicata al caso dell’azienda Alfa, comprendendone le potenzialità. In particolare, si vogliono studiare i principali risvolti portati da tale innovazione, le più importanti iniziative adottate in merito alle nuove tecnologie implementate e i benefici che queste portano in campo strategico, di business e cultura aziendale.

Middleware per integrazione di dati eterogenei per sistemi SAP su Openshift

Questo lavoro di tesi è incentrato sullo sviluppo di una soluzione applicativa nell'ambito dell'integrazione di sistemi software basati su tecnologie considerate legacy. In particolar modo è stato studiata una soluzione integrativa per il popolare ERP gestionale Sap su piattaforma Cloud OpenShift. La soluzione è articolata su diversi livelli basati sull'architettura proposta da Gartner nell'ambito della Digital Integration Hub. È stata sviluppata tramite tecnologie open source leader nel settore e tecnologie cloud avanzate.

Generative Neural Networks for image iper-resolution and improvement of Optical Character Recognition's performances

The usage of Optical Character Recognition’s (OCR, systems is a widely spread technology into the world of Computer Vision and Machine Learning. It is a topic that interest many field, for example the automotive, where becomes a specialized task known as License Plate Recognition, useful for many application from the automation of toll road to intelligent payments. However, OCR systems need to be very accurate and generalizable in order to be able to extract the text of license plates under high variable conditions, from the type of camera used for acquisition to light changes. Such variables compromise the quality of digitalized real scenes causing the presence of noise and degradation of various type, which can be minimized with the application of modern approaches for image iper resolution and noise reduction. Oneclass of them is known as Generative Neural Networks, which are very strong ally for the solution of this popular problem.

A Provenance-Aware Policy Language (cProvl) and a Data Traceability Model (cProv) for the Cloud

Provenance plays a pivotal in tracing the origin of something and determining how and why something had occurred. With the emergence of the cloud and the benefits it encompasses, there has been a rapid proliferation of services being adopted by commercial and government sectors. However, trust and security concerns for such services are on an unprecedented scale. Currently, these services expose very little internal working to their customers; this can cause accountability and compliance issues especially in the event of a fault or error, customers and providers are left to point finger at each other. Provenance-based traceability provides a mean to address part of this problem by being able to capture and query events occurred in the past to understand how and why it took place. However, due to the complexity of the cloud infrastructure, the current provenance models lack the expressibility required to describe the inner-working of a cloud service. For a complete solution, a provenance-aware policy language is also required for operators and users to define policies for compliance purpose. The current policy standards do not cater for such requirement. To address these issues, in this paper we propose a provenance (traceability) model cProv, and a provenance-aware policy language (cProvl) to capture traceability data, and express policies for validating against the model. For implementation, we have extended the XACML3.0 architecture to support provenance, and provided a translator that converts cProvl policy and request into XACML type.

Privacy and security enhanced offline oblivious transfer for massive data distribution

Unauthorized accesses to digital contents are serious threats to international security and informatics. We propose an offline oblivious data distribution framework that preserves the sender's security and the receiver's privacy using tamper-proof smart cards. This framework provides persistent content protections from digital piracy and promises private content consumption.

A security layer for JXTA core protocols

JXTA define un conjunto de seis protocolos básicos especialmente adecuados para una computación ad hoc, permanente, multi-hop, peer-to-peer (P2P). Estos protocolos permiten que los iguales cooperen y formen grupos autónomos de pares. Este artículo presenta un método que proporciona servicios de seguridad en los protocolos básicos: protección de datos, autenticidad, integridad y no repudio. Los mecanismos que se presentan son totalmente distribuidos y basados ¿¿en un modelo puro peer-to-peer, que no requieren el arbitraje de un tercero de confianza o una relación de confianza establecida previamente entre pares, que es uno de los principales retos en este tipo de entornos.

Security and Privacy in a Ubiquitous Information Screen

We expose the ubiquitous interaction between an information screen and its’ viewers mobile devices, highlights the communication vulnerabilities, suggest mitigation strategies and finally implement these strategies to secure the communication. The screen infers information preferences’ of viewers within its vicinity transparently from their mobile devices over Bluetooth. Backend processing then retrieves up-to-date versions of preferred information from content providers. Retrieved content such as sporting news, weather forecasts, advertisements, stock markets and aviation schedules, are systematically displayed on the screen. To maximise users’ benefit, experience and acceptance, the service is provided with no user interaction at the screen and securely upholding preferences privacy and viewers anonymity. Compelled by the personal nature of mobile devices, their contents privacy, preferences confidentiality, and vulnerabilities imposed by screen, the service’s security is fortified. Fortification is predominantly through efficient cryptographic algorithms inspired by elliptic curves cryptosystems, access control and anonymity mechanisms. These mechanisms are demonstrated to attain set objectives within reasonable performance.