CA

The most challenging applications in heterogeneous cloud ecosystems are those that are able to maximise the benefits of the combination of the cloud resources in use: multi-cloud applications. They have to deal with the security of the individual components as well as with the overall application security including the communications and the data flow between the components. The main objective of MUSA is to support the security-intelligent lifecycle management of distributed applications over heterogeneous cloud resources, through a security framework that includes: security-by-design mechanisms to allow application self-protection at runtime, and methods and tools for the integrated security assurance in both the engineering and operation of multi-cloud applications. The MUSA framework leverages security-by-design, agile and DevOps approaches in multi-cloud applications, and enables the security-aware development and operation of multi-cloud applications. The framework will be composed of a) an IDE for creating the multi-cloud application taking into account its security requirements together with functional and business requirements, b) a set of security mechanisms embedded in the multi-cloud application components for self-protection, c) an automated deployment environment that, based on an intelligent decision support system, will allow for the dynamic distribution of the components according to security needs, and d) a security assurance platform in form of a SaaS that will support multi-cloud application runtime security control and transparency to increase user trust. The project will demonstrate and evaluate the economic viability and practical usability of the MUSA framework in highly relevant industrial applications representative of multi-cloud application development potential in Europe. The project duration will be 36 months, with an overall budget of 3,574,190 euros.

PDP4E is an innovation action that will provide software and system engineers with methods and software tools to systematically apply data protection principles in the projects they carry out, so that the products they create comply with the General Data Protection Regulation (GDPR), thus bringing the principles of Privacy and Data Protection by Design to practice. PDP4E will integrate privacy and data protection engineering functionalities into existent, mainstream software tools that are already in use by engineers, focusing on open-source tools that will be integrated in the Eclipse ecosystem, The approach will integrate methods proposed by the privacy engineering community (e.g. LINDDUN, ISO/IEC 27550 Privacy engineering), and the industry of software and system engineering tools (e.g. MUSE, PAPYRUS or OpenCert) using a model driven engineering approach. PDP4E will introduce privacy and data protection into software and system engineering disciplines (Risk Management, Requirements Engineering, Model-Driven Design, and Assurance), which drive the everyday activities of engineers. Results of PDP4E will be assessed by two demonstration pilots on industries where privacy and data protection are especially relevant, one on C-ITS applications and services (connected vehicle application domain) and one on big data on smart grid (smart grid application domain). PDP4E will promote its results in engineering communities, as Eclipse (community of software developers) or IPEN (community of stakeholders with an interest on privacy engineering). An open Alliance for Privacy and Data Protection Engineering is planned as a follow-up of the project, building on that community and the synergies among partners. PDP4E includes 8 partners and has a 36-month duration.

The consortium of this European Training Network (ETN) "BigStorage: Storage-based Convergence between HPC and Cloud to handle Big Data” will train future data scientists in order to enable them and us to apply holistic and interdisciplinary approaches for taking advantage of a data-overwhelmed world, which requires HPC and Cloud infrastructures with a redefinition of storage architectures underpinning them - focusing on meeting highly ambitious performance and energy usage objectives. There has been an explosion of digital data, which is changing our knowledge about the world. This huge data collection, which cannot be managed by current data management systems, is known as Big Data. Techniques to address it are gradually combining with what has been traditionally known as High Performance Computing. Therefore, this ETN will focus on the convergence of Big Data, HPC, and Cloud data storage, ist management and analysis. To gain value from Big Data it must be addressed from many different angles: (i) applications, which can exploit this data, (ii) middleware, operating in the cloud and HPC environments, and (iii) infrastructure, which provides the Storage, and Computing capable of handling it. Big Data can only be effectively exploited if techniques and algorithms are available, which help to understand its content, so that it can be processed by decision-making models. This is the main goal of Data Science. We claim that this ETN project will be the ideal means to educate new researchers on the different facets of Data Science (across storage hardware and software architectures, large-scale distributed systems, data management services, data analysis, machine learning, decision making). Such a multifaceted expertise is mandatory to enable researchers to propose appropriate answers to applications requirements, while leveraging advanced data storage solutions unifying cloud and HPC storage facilities.