CAPE is a European-funded project targeting to reshape edge-cloud computing by defining edge micro data centers as a new unit of computing. Fully committing to open source, CAPE develops a fully Composable Infrastructure (CI) for high-performance edge server hardware platforms grounded in open, forward-looking standards. Together with an open-source software stack covering the Edge-Cloud Continuum, this holistic approach boosts power and energy efficiency while reducing resource overprovisioning. Completely based on open standards, CAPE strengthens the digital sovereignty Europe needs in a challenging future. This work gives an overview of the current architectural blueprint of the project, focusing on integrating game-changing technologies like Compute Express Link (CXL) for compute and memory disaggregation, pushing open source cluster management, and AI-assisted deployment software stacks using Infrastructure from Code (IfC). The proposed approaches and benefits for future Edge-Cloud data centers are demonstrated within three use cases, ranging from Smart Grid and Edge-AI to Satellite Data Processing.

The EU-funded EMPYREAN project (empyrean-horizon.eu) aims to establish a hyper-distributed computing paradigm, leveraging collaborative, heterogeneous IoT devices and federated resources. EMPYREAN focuses on developing technologies for efficient AI workload processing, secure distributed edge storage and cloud-native application development. It will offer open and standardised APIs and use open-source platforms. EMPYREAN's capabilities will be demonstrated through three use cases: advanced manufacturing, smart agriculture, and warehouse automation.

The cloud is extended towards the edge to form a computing continuum while managing resources' heterogeneity. The serverless technology simplified how to build cloud applications and use resources, becoming a driving force in consolidating the continuum with the deployment of small functions with short execution. However, the adaptation of serverless to the edge-cloud continuum brings new challenges mainly related to resource management and scheduling. Standard cloud scheduling policies are based on greedy algorithms that do not efficiently handle platforms' heterogeneity nor deal with problems such as cold start delays. This work introduces a new scheduling policy that tries to address these issues. It is based on multi-objective optimization for data transfers and makespan while considering heterogeneity. Using simulations that vary workloads, platforms, and heterogeneity levels, we study the system utilization, the trade-offs between the targets, and the impacts of considering platforms' heterogeneity. We perform comparisons with a baseline inspired by a Kubernetes-based policy, representing greedy algorithms. Our experiments show considerable gaps between the efficiency of a greedy-based scheduling policy and a multi-objective-based one. The last outperforms the baseline by reducing makespan, data transfers, and system utilization by up to two orders of magnitudes in relevant cases for the edge-cloud continuum.

Executing Big Data workloads upon High Performance Computing (HPC) infrastractures has become an attractive way to improve their performances. However, the collocation of HPC and Big Data workloads is not an easy task, mainly because of their core concepts' differences. This paper focuses on the challenges related to the scheduling of both Big Data and HPC workloads on the same computing platform. In classic HPC workloads, the rigidity of jobs tends to create holes in the schedule: we can use those idle resources as a dynamic pool for Big Data workloads. We propose a new idea based on Resource and Job Management System's (RJMS) configuration, that makes HPC and Big Data systems to communicate through a simple prolog/epilog mechanism. It leverages the built-in resilience of Big Data frameworks, while minimizing the disturbance on HPC workloads. We present the first study of this approach, using the production RJMS middleware OAR and Hadoop YARN from the HPC and Big Data ecosystems respectively. Our new technique is evaluated with real experiments upon the Grid5000 platform. Our experiments validate our assumptions and show promising results. The system is capable of running an HPC workload with 70% cluster utilization, with a Big Data workload that fills the schedule holes to reach a full 100% utilization. We observe a penalty on the mean waiting time for HPC jobs of less than 17% and a Big Data effectiveness of more than 68% in average.