SMEs and public sector organizations increasingly investigate the possibilities to use cloud computing services in their everyday business conduct. Accessing services and resources in the cloud on-demand and in a flexible and elastic way could result in significant cost savings due to more efficient and convenient resource utilization that also replaces large investment costs with long term operational costs. On the other hand, the take up of cloud computing by SMEs and the public sector is still relatively low due to limited application-level flexibility and also security concerns. The Cloud Orchestration at the Level of Application (COLA) project aims to increase the adoption of cloud computing services by the above mentioned two strategic target communities. Typical industry and public sector applications require resource scalability and efficient resource utilization in order to serve a variable number of customers with dynamic resource demands, and to suitably optimize resource consumption and costs. However, the dynamic and intelligent utilization of cloud infrastructure resources from the perspective of cloud applications is not trivial. Although there have been several efforts to support the intelligent and coordinated deployment, and to a smaller extent also the run-time orchestration of cloud applications, no comprehensive solution has emerged until now that could be applied in large scale near operational level industry trials. The overall objective of the COLA project is that by building on and extending current research results, it will define and provide a reference implementation of a generic and pluggable framework that supports the optimal and secure deployment and run-time orchestration of cloud applications. COLA will demonstrate the applicability and impact of the solution via large scale near operational level SME and public sector pilots and demonstrators, and will also define a clear pathway how the innovation can be delivered to the market.

There is an increasing need to develop data intensive applications able to manage more and more amounts of data coming from distributed and heterogeneous sources effectively, quickly, correctly, and securely. However, the current adoption of Cloud Computing paradigm is not fully appropriate to store and analyse such data: latency, security, and compliance are still significant barriers. At the same time, Fog Computing has emerged as a paradigm promising to fully exploit the potential of the edge of the network involving traditional devices as well as new generation of smart devices, which can process data closer to where they are produced and/or consumed but which cannot ensure the same reliability and scalability as cloud computing offers. The goal of DITAS is to propose a framework, composed by an SDK and an execution environment, which aims to overcome the barriers that now hamper the adoption of Cloud Computing and increase the adoption of Fog computing by exploiting the full potential of these two paradigms in a synergic way. This will support the development and execution of data-intensive application that are now – and even more in the future – crucial for organizations and companies that want to manage their data in an efficient, reliable, scalable, and secure manner. Abstractions provided in DITAS with Data Virtualization and Data Utility will expose the data to be managed by the application in terms of Virtual Data Containers which hide the complexity of the underlying infrastructure composed of heterogeneous data sources, smart devices, traditional servers, and sensor networks Distribution could also change dynamically. Conversely, Virtual Data Containers offer to developers the possibility to express requirements on data in terms of performance, quality, security and privacy thus to focus only on the application logic, leaving to the DITAS execution environment the responsibility of finding, processing, and delivering the data according to user needs

Technological advances in remote sensing have increased the availability of satellite images with different spatiotemporal and spectral characteristics.There is difficulty for retrieving the most appropriate data for each user's needs.One key challenge is to connect the quantitative information of the EO images with the qualitative (high-level user queries) and be able to mine these connections in big archives.An inherent question arises; how to retrieve EO images based on user semantically aware questions.Content based EO image retrieval techniques have been introduced for bridging the gap between low-level image features and high-level queries.The main constraint of the existing approaches is the generalization of the problem.The formulated ontologies are not focused on the constraints of EO images.The main objective of SEO-DWARF is to realize the content-based search of EO images on an application specific basis.The marine application domain and data from Sentinels 1,2,3, ENVISAT will be used.Queries such as “Calculate the rate of increasing chlorophyll in the NATURA area” will be answered by the SEO-DWARF, helping users to retrieve the appropriate EO images for their specific needs or alert them when a specific phenomenon occurs.The research contains the:a) ontology formalization for the specific research topics,b) determination of the semantic queries for the application domains,c) algorithm development for extracting metadata from the EO images,d) design of an architecture of the platform to perform the semantic image retrieval and storage and management of the extracted metadata.All four aspects will be integrated in an innovative and user-friendly web based platform enabling the users to retrieve images for marine applications or register for a semantic alert.A strong and experienced research team, of 4 academic and 5 industrial partners, coming from Greece(3), Italy(2), Germany(1), France(1), Cyprus(1) and Switzerland(1) constitute the project’s consortium.