Self-healing Multi-Cloud Application Modelling

Cloud computing market forecasts and technology trends confirm that Cloud is an IT disrupting phenomena and that the number of companies with multi-cloud strategy is continuously growing. Cost optimization and increased competitiveness of companies that exploit multi-cloud will only be possible when they are able to leverage multiple cloud offerings, while mastering both the complexity of multiple cloud provider management and the protection against the higher exposure to attacks that multi-cloud brings. This paper presents the MUSA Security modelling language for multi-cloud applications which is based on the Cloud Application Modelling and Execution Language (CAMEL) to overcome the lack of expressiveness of state-of-the-art modelling languages towards easing: a) the automation of distributed deployment, b) the computation of composite Service Level Agreements (SLAs) that include security and privacy aspects, and c) the risk analysis and service match-making taking into account not only functionality and business aspects of the cloud services, but also security aspects. The paper includes the description of the MUSA Modeller as the Web tool supporting the modelling with the MUSA modelling language. The paper introduces also the MUSA SecDevOps framework in which the MUSA Modeller is integrated and with which the MUSA Modeller will be validated.The MUSA project leading to this paper has received funding from the European Union’s Horizon 2020 research and innovation pro- gramme under grant agreement No 644429

A survey and classification of software-defined storage systems

The exponential growth of digital information is imposing increasing scale and efficiency demands on modern storage infrastructures. As infrastructure complexity increases, so does the difficulty in ensuring quality of service, maintainability, and resource fairness, raising unprecedented performance, scalability, and programmability challenges. Software-Defined Storage (SDS) addresses these challenges by cleanly disentangling control and data flows, easing management, and improving control functionality of conventional storage systems. Despite its momentum in the research community, many aspects of the paradigm are still unclear, undefined, and unexplored, leading to misunderstandings that hamper the research and development of novel SDS technologies. In this article, we present an in-depth study of SDS systems, providing a thorough description and categorization of each plane of functionality. Further, we propose a taxonomy and classification of existing SDS solutions according to different criteria. Finally, we provide key insights about the paradigm and discuss potential future research directions for the field.This work was financed by the Portuguese funding agency FCT-Fundacao para a Ciencia e a Tecnologia through national funds, the PhD grant SFRH/BD/146059/2019, the project ThreatAdapt (FCT-FNR/0002/2018), the LASIGE Research Unit (UIDB/00408/2020), and cofunded by the FEDER, where applicable