SecureCloud addresses the confidentiality, integrity and availability of applications executed in the cloud. Data at rest or in transit on the network is already nowadays protected by encryption. The main problem that we face is how to ensure the confidentiality of data while being processed. Our approach is based on upcoming hardware extensions of commodity CPUs like Intel's Secure Guard Extensions (SGX). By the help of these hardware extensions, we reduce the trusted computing base dramatically by excluding from it the millions of lines of source code of the cloud stack, operating systems and hypervisor. This permits us to ensure the confidentiality of computations even if the computers are under a different administrative control (like a cloud provider) or there is no physical security of the computers. Moreover, we ensure the confidentiality even if attackers would take control of the cloud stack, the hypervisor or the operating systems. As long as the hardware extensions of the CPU can be trusted, we can ensure the confidentiality of the computations. SecureCloud focuses on ensuring the confidential and dependable processing of Big Data. To keep the trusted computing base small, we use the concept of microservices: only the application logic that processes data (e.g., operators) is protected while all functionality that, e.g., shuffles and stores encrypted data is outside the trusted computing base. By monitoring the microservices, we can restart services that run on compromised hosts. We will evaluate and demonstrate our approach in the context of smart grids. In this use case context, we need to run across a physically distributed computing infrastructure with no or little physical security and partly untrusted administrators. We need to process large volumes of data and this big data processing would benefit by partial offloading into the cloud. In SecureCloud, we will show how to do this in a secure fashion even if clouds are untrusted.

EMPYREAN envisages a hyper-distributed computing paradigm, based on federations of collaborative and heterogeneous IoT devices and resources (e.g., on RISC-V) across different providers and networks. These federations, namely Associations, operate autonomously and interconnect seamlessly utilizing distributed, cognitive and dynamic AI-enabled decision-making, to balance computing tasks and data inside an Association as well as between Associations in a multi-agent manner and across central computing environments, optimizing resources and providing scalability, resiliency, energy efficiency and quality of service. An Association will constitute a trusted execution environment, while identity and data access management schemes will assure controlled access and confidentiality of data, utilizing Cluster 3 related outcomes from participating partners. EMPYREAN will also be empowered with automated tools and mechanisms for efficient data processing of AI-workloads and secure distributed edge storage. Developed technologies will also enable Associations-native application development and deployment, contributing to the entire application lifecycle and interoperability. EMPYREAN will provide open and standardized APIs, while utilizing and extending open-source platforms maintained by European companies from the consortium. EMPYREAN will demonstrate its advanced and innovative capabilities through three well-defined use cases that involve device- and data-rich applications in advanced manufacturing, smart agriculture and warehouse automation, involving AI-driven value extraction from high volume and dynamic IoT data generated by multiple sources (e.g., robots) at the edge of the network. Also, a South Korea based use case in smart factories will further showcase the benefits of the EMPYREAN’s technologies. EMPYREAN will develop its Association-based continuum through synergies with emerging IPCEI initiatives and EU bodies (e.g., GAIA-X, IDSA) by involved partners.

SERRANO’s overall ambition is to introduce a novel ecosystem of cloud-based technologies, spanning from specialized hardware resources up to software toolsets. This will enable application-specific service instantiation and optimal customizations based on the workloads to be processed, in a holistic manner, thus supporting highly demanding, dynamic and security-critical applications. SERRANO is not only tuned and fully aligned with current trends in the cloud computing sector towards the expansion of cloud infrastructures so as to efficiently integrate edge resources, but it also integrates transparently HPC resources ir to provide an infrastructure that goes beyond the scope of the “normal” cloud and realizes a true computing continuum. SERRANO introduces an abstraction layer that transforms the distributed edge, cloud and HPC resources into a single borderless infrastructure, while it also facilitates their automated and cognitive orchestration. It proposes the introduction and evolution of novel key concepts and approaches that aim to close existing technology gaps, towards the realization of advanced infrastructures, able to meet the stringent requirements of future applications and services. It will develop technologies and mechanisms related to security and privacy in distributed computing and storage infrastructures, hardware and software acceleration on cloud and edge, cognitive resource orchestration, dynamic data movement and task offloading between edge/cloud/HPC, transparent application deployment, energy-efficiency and real-time and zero-touch adaptability. Finally, to highlight the proposed ecosystem’s scientific and technological significance, SERRANO will demonstrate three high impact use cases related to (i) secure cloud and edge storage over a diversity of cloud resources, (ii) fintech by supporting latency-sensitive and safety-critical digital services in the financial sector and (iii) machine anomaly detection in manufacturing for Industry 4.0

MLSysOps will achieve substantial research contributions in the realm of AI-based system adaptation across the cloud-edge continuum by introducing advanced methods and tools to enable optimal system management and application deployment. MLSysOps will design, implement and evaluate a complete framework for autonomic end-to-end system management across the full cloud-edge continuum. MLSysOps will employ a hierarchical agent-based AI architecture to interface with the underlying resource management and application deployment/orchestration mechanisms of the continuum. Adaptivity will be achieved through continual ML model learning in conjunction with intelligent retraining concurrently to application execution, while openness and extensibility will be supported through explainable ML methods and an API for pluggable ML models. Flexible/efficient application execution on heterogeneous infrastructures and nodes will be enabled through innovative portable container-based technology. Energy efficiency, performance, low latency, efficient, resilient and trusted tier-less storage, cross-layer orchestration including resource-constrained devices, resilience to imperfections of physical networks, trust and security, are key elements of MLSysOps addressed using ML models. The framework architecture disassociates management from control and seamlessly interfaces with popular control frameworks for different layers of the continuum. The framework will be evaluated using research testbeds as well as two real-world application-specific testbeds in the domain of smart cities and smart agriculture, which will also be used to collect the system-level data necessary to train and validate the ML models, while realistic system simulators will be used to conduct scale-out experiments. The MLSysOps consortium is a balanced blend of academic/research and industry/SME partners, bringing together the necessary scientific and technological skills to ensure successful implementation and impact.

RESCALE aims at designing, building, and demonstrating secure-by-design supply chains. To this end, RESCALE will (i) automate the evaluation processes of both software and hardware components, (ii) ensure that third-party segments are free from vulnerabilities, (iii) offer effective audit procedures for cybersecurity testing, and (iv) enable the construction of secure systems with the strongest possible guarantees. Overall, RESCALE will systematically analyse and extend, as necessary, every hardware and software layer in a computing system and apply novel tools and methodologies at every step of the entire supply chain.