European Open Compute Architecture for Powerful Edge
Start Date
December 1, 2024
End Date
November 30, 2027
Acronym
CAPE
Description
CAPE (European Open Compute Architecture for Powerful Edge) aims to redefine the landscape of edge-cloud computing infrastructures by developing the EdgeMicroDataCenters (EMDC's) and eHPS as a 'new unit of computing' for data-dense edge environments. The project designs and showcase an innovative, open hardware platform that is dynamically composable via CXL to answer the end user needs. EMDC and eHPS provides an open high density platform for heterogeneous computing units (XPU), RISC-V architectures all based on industry-standard form factor, COM-HPC that is supported by a robust ecosystem of Original Equipment Manufacturers (OEMs) within Europe, ensuring wide accessibility and adoption.
To allow end users to be digital sovereign e.g. manage the governance of data, AI models, applications deployed across an ‘edge-first’ edge to cloud continuum, CAPE will employ a cloud-agnostic overlay known as Infrastructure from Code (IfC). This innovative approach abstracts the complexities inherent in diverse cloud computing infrastructures and services, empowering software developers to deploy applications effortlessly across the edge-to-cloud continuum. This is achieved without necessitating extensive knowledge of the underlying cloud infrastructure, enabling deployments across on-premise and off-premise, public and private cloud environments with minimal complexity.
CAPE's solution will be validated in 3 use cases: the management of intelligent electric energy microgrids, edge AI and satellite communications. All usecases will evaluate RISC-V (EPI) and CXL solutions. Each usecase will be evaluated on technical, economical and sustainability aspects and benchmarked against legacy hardware in local clouds against edge-optimized data centers.
To allow end users to be digital sovereign e.g. manage the governance of data, AI models, applications deployed across an ‘edge-first’ edge to cloud continuum, CAPE will employ a cloud-agnostic overlay known as Infrastructure from Code (IfC). This innovative approach abstracts the complexities inherent in diverse cloud computing infrastructures and services, empowering software developers to deploy applications effortlessly across the edge-to-cloud continuum. This is achieved without necessitating extensive knowledge of the underlying cloud infrastructure, enabling deployments across on-premise and off-premise, public and private cloud environments with minimal complexity.
CAPE's solution will be validated in 3 use cases: the management of intelligent electric energy microgrids, edge AI and satellite communications. All usecases will evaluate RISC-V (EPI) and CXL solutions. Each usecase will be evaluated on technical, economical and sustainability aspects and benchmarked against legacy hardware in local clouds against edge-optimized data centers.
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Item type:Publication, Metamorphic Testing for Infrastructure-as-Code Engines(2026); ; ; Meier, RolandInfrastructure-as-Code (IaC) engines, such as Terraform, OpenTofu, and Pulumi, automate the provisioning and management of cloud resources. They parse IaC specifications and orchestrate the required actions, making them the backbone of modern clouds, and critical to the reliability of both the underlying infrastructure and the software that depends on it. Despite this importance, this class of systems has received little attention: prior work largely targets the correctness of IaC programs rather than the IaC engines themselves. Existing test suites rely on manually written oracles and struggle to expose faults that manifest across multiple executions, leaving a significant reliability gap. We present EMIaC, a metamorphic testing framework for IaC engines. EMIaC defines metamorphic relations as graph-based transformations of IaC programs and checks invariants across executions of the original and transformed programs. A central novelty is our use of e-graphs in software testing, as both a test-input generator and an equivalence oracle. E-graphs compactly represent program equivalences, enabling the systematic generation of large spaces of equivalent IaC programs. To ground these relations, we analyze 43,593 real-world Terraform programs and show that IaC dependency graphs are typically small and sparse, making e-graphs a natural fit. Evaluating EMIaC on Pulumi, Terraform, and OpenTofu, we show that it complements existing test suites by exercising engine-critical code paths and covering 98 previously untested statements in Terraform and 1,313 in Pulumi. EMIaC also uncovers previously unknown issues in all three test suites, improving their adequacy. Three test cases have been merged into Terraform's main branch, and Pulumi has merged a specification fix.Type:conference paper