Collaboration and sharing of resources is critical for research. Authentication and Authorisation Infrastructures (AAIs) play a key role in enabling federated interoperable access to resources. The AARC Technical Revision to Enhance Effectiveness (AARC TREE) project takes the successful and globally recognised “Authentication and Authorisation for Research Collaboration” (AARC) model and its flagship outcome, the AARC Blueprint Architecture (BPA), as the basis to drive the next phase of integration for research infrastructures: expand federated access management to integrate user-centring technologies, expand access to federated data and services (authorisation), consolidating existing capacities and avoiding fragmentation and unnecessary duplication. The main objectives of the AARC TREE project are to: (i) Capture and analyse new Authentication and Authorisation interoperability requirements (as emerging that support integration use-cases across the thematic area) and provide a landscape analysis of AAIs services (including gaps) in the RIs represented in AARC TREE (ii) Define and validate new technical and policy guidelines for the AARC BPA that address RIs use-cases. This will improve the integration of RIs across thematic areas and increase the ability of RIs to support emerging needs (iii) Expand the number of research communities that can implement the AARC BPA and/or the AARC guidelines, by providing a validation environment and toolkits. At the same time support existing AARC communities in adopting new guidelines (iv) Bring RIs, e-Infrastructures and relevant stakeholders together to align strategies to integrate new technologies, better interoperate and share resources across thematic areas and produce a compendium and recommendations for different stakeholders

The cloud computing industry has grown massively over the last decade and with that new areas of application have arisen. Some areas require specialized hardware, which needs to be placed in locations close to the user. User requirements such as ultra-low latency, security and location awareness are becoming more and more common, for example, in Smart Cities, industrial automation and data analytics. Modern cloud applications have also become more complex as they usually run on a distributed computer system, split up into components that must run with high availability. Unifying such diverse systems into centrally controlled compute clusters and providing sophisticated scheduling decisions across them are two major challenges in this field. Scheduling decisions for a cluster consisting of cloud and edge nodes must consider unique characteristics such as variability in node and network capacity. The common solution for orchestrating large clusters is Kubernetes, however, it is designed for reliable homogeneous clusters. Many applications and extensions are available for Kubernetes. Unfortunately, none of them accounts for optimization of both performance and energy or addresses data and job locality. In DECICE, we develop an open and portable cloud management framework for automatic and adaptive optimization of applications by mapping jobs to the most suitable resources in a heterogeneous system landscape. By utilizing holistic monitoring, we construct a digital twin of the system that reflects on the original system. An AI-scheduler makes decisions on placement of job and data as well as conducting job rescheduling to adjust to system changes. A virtual training environment is provided that generates test data for training of ML-models and the exploration of what-if scenarios. The portable framework is integrated into the Kubernetes ecosystem and validated using relevant use cases on real-world heterogeneous systems.

The Data Infrastructure Capacities for EOSC (DICE) consortium brings together a network of computing and data centres, research infrastructures, and data repositories for the purpose to enable a European storage and data management infrastructure for EOSC, providing generic services and building blocks to store, find, access and process data in a consistent and persistent way. Specifically, DICE partners will offer 14 state-of-the-art data management services together with more than 50 PB of storage capacity. The service and resource provisioning will be accompanied by enhancing the current service offering in order to fill the gaps still present to the support of the entire research data lifecycle; solutions will be provided for increasing the quality of data and their re-usability, supporting long term preservation, managing sensitive data, and bridging between data and computing resources. All services provided via DICE will be offered through the EOSC Portal and interoperable with EOSC Core via a lean interoperability layer to allow efficient resource provisioning from the very beginning of the project. The partners will closely monitor the evolution of the EOSC interoperability framework and guidelines to comply with a) the rules of participation to onboard services into EOSC, and b) the interoperability guidelines to integrate with the EOSC Core functions. The data services offered via DICE through EOSC are designed to be agnostic to the scientific domains in order to be multidisciplinary and to fulfil the needs of different communities. The consortium aims to demonstrate their effectiveness of the service offering by integrating services with community platforms as part of the project and by engaging with new communities coming through EOSC.