Data as a Service (DaaS) is among the latest kind of services being investigated in the Cloud computing community. The main aim of DaaS is to overcome limitations of state-ofthe-art approaches in data technologies, according to which data is stored and accessed from repositories whose location is known and is relevant for sharing and processing. Besides limitations for the data sharing, current approaches also do not achieve to fully separate/decouple software services from data and thus impose limitations in inter-operability. In this paper we propose a DaaS approach for intelligent sharing and processing of large data collections with the aim of abstracting the data location (by making it relevant to the needs of sharing and accessing) and to fully decouple the data and its processing. The aim of our approach is to build a Cloud computing platform, offering DaaS to support large communities of users that need to share, access, and process the data for collectively building knowledge from data. We exemplify the approach from large data collections from health and biology domains.
How to effectively manage increasingly complex enterprise computing environments is one of the hardest challenges that most organizations have to face in the era of cloud computing, big data and IoT. Advanced automation and orchestration systems are the most valuable solutions helping IT staff to handle large-scale cloud data centers. Containers are the new revolution in the cloud computing world, they are more lightweight than VMs, and can radically decrease both the startup time of instances and the processing and storage overhead with respect to traditional VMs. The aim of this paper is to provide a comprehensive description of cloud orchestration approaches with containers, analyzing current research efforts, existing solutions and presenting issues and challenges facing this topic.
The Istituto Nazionale di Geofisica e Vulcanologia leads an international project funded by the Italian National Program for Antarctic Research, called Demonstrator of Global Navigation Satellite System (GNSS) Research and Application for Polar Environment (DemoGRAPE), in partnership with Politecnico di Torino, Istituto Superiore Mario Boella, and with South African National Space Agency and the Brazilian National Institute of Space Physics, as key collaborators. DemoGRAPE is a new prototype of support for the satellite navigation in Antarctica. Besides the scientific interest, the accuracy of satellite navigation in Antarctica is of paramount importance since there is always the danger that people and vehicles can fall into a crevasse during a snowstorm, when visibility is limited and travel is restricted to following specified routes using satellite navigation systems. The variability of ionospheric delay and ionospheric scintillation are two of the primary factors which affect the accuracy of satellite navigation. The project will provide a demonstrator of cutting edge technology for the empirical assessment of the ionospheric delay and ionospheric scintillations in the polar regions. The scope of the project includes new equipment for the recording and dissemination of GNSS data and products installed at the South African and Brazilian bases in Antarctica. The new equipment will facilitate the exchange of software and derived products via the Cloud computing technology infrastructure. The project portal is accessible at http://www.demogrape.net. We report the first Global Navigation Satellite System (GNSS) signal scintillations observed in Antarctica.
Two important topics related to the cloud security are discussed in this chapter: the authentication of logical users accessing the cloud, and the security of data stored on public cloud servers. A real cloud platform is used as example; it is designed and implemented to support basic web applications, and to be shared by small and medium companies. Such platform is built using the Open-Stack architecture. The user authentication is based on an original biometric approach exploiting fingerprints, and open to multimodal improvements. The platform guarantees secure access of multiple users and complete logical separation of computational, and data resources, related to different companies. High-level of protection of the data, stored in the cloud, is ensured by adopting a peculiar data fragmentation approach. Details are given about the authentication process, and of the service modules involved in the biometric authentication. Furthermore are discussed the key issues, related to the integration of the biometric authentication, in the cloud platform.
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