Considering the complexity in the design of future satellites and the need for compliance with ECSS standards, the METASAT project proposes a novel design methodology based on model-based engineering and supported by open architecture hardware. The initiative highlights the potential of software vir-tualisation layers, like hypervisors, to meet standards compliance on high-performance computing platforms. Our focus is on the development of a toolchain tailored for these advanced hard-ware/software layers. In our view, without such innovation, the satellite industry may face high and unsustainable development costs and timelines, which could affect its competitiveness and reliability. This paper provides an overview of the model-based engineering toolchain, the workflow, and the digital twin concept proposed for the METASAT project. We present the purpose of each component and how they work together in the broader METASAT vision, with the objective of showing how this approach can make the development process more efficient and enhance dependability in the sector.

Given the complex design requirements of future satellites and the need to comply with ECSS standards, the METASAT project introduces an innovative design approach that uses model-based engineering and is supported by open architecture hardware. The project highlights the importance of software virtualisation layers, such as hypervisors, in achieving compliance with standards on high-performance computing plat-forms. The project is dedicated to creating a specialised toolchain for these advanced hardware and software layers. We believe that without such innovations, the satellite industry could face increased and unsustainable development costs and timelines, potentially compromising its competitiveness and dependability. This paper provides a general overview of the METASAT project and introduces the approaches being used to add support for the XtratuM hypervisor and code generation for hardware accelerators in a model-based engineering workflow.

Following the same trend of consumer electronics, safety-critical industries are starting to adopt Over-The-Air Software Updates (OTASU) on their embedded systems. The motivation behind this trend is twofold. On the one hand, OTASU offer several benefits to the product makers and users by improving or adding new functionality and services to the product without a complete redesign. On the other hand, the increasing connectivity trend makes OTASU a crucial cyber-security demand to download latest security patches. However, the application of OTASU in the safety-critical domain is not free of challenges, specially when considering the dramatic increase of software complexity and the resulting high computing performance demands. This is the mission of UP2DATE, a recently launched project funded within the European H2020 programme focused on new software update architectures for heterogeneous high-performance mixed-criticality systems. This paper gives an overview of UP2DATE and its foundations, which seeks to improve existing OTASU solutions by considering safety, security and availability from the ground up in an architecture that builds around composability and modularity.

Critical real-time systems require strict resource provisioning in terms of memory and timing. The constant need for higher performance in these systems has led industry to recently include GPUs. However, GPU software ecosystems are by their nature closed source, forcing system engineers to consider them as black boxes, complicating resource provisioning. In this work, we reverse engineer the internal operations of the GPU system software to increase the understanding of their observed behaviour and how resources are internally managed. We present our methodology that is incorporated in GMAI (GPU Memory Allocation Inspector), a tool that allows system engineers to accurately determine the exact amount of resources required by their critical systems, avoiding underprovisioning. We first apply our methodology on a wide range of GPU hardware from different vendors showing its generality in obtaining the properties of the GPU memory allocators. Next, we demonstrate the benefits of such knowledge in resource provisioning of two case studies from the automotive domain, where the actual memory consumption is up to 5.6× more than the memory requested by the application.

Critical real-time systems require strict resource provisioning in terms of memory and timing. The constant need for higher performance in these systems has led industry to recently include GPUs. However, GPU software ecosystems are by their nature closed source, forcing system engineers to consider them as black boxes, complicating resource provisioning. In this work we reverse engineer the internal operations of the GPU system software to increase the understanding of their observed behaviour and how resources are internally managed. This way, we allow system engineers to accurately determine the exact amount of resources required by their critical systems, avoiding underprovisioning. We first apply our methodology on a wide range of GPU hardware showing its generality in obtaining the properties of the GPU memory allocators. Next, we demonstrate the benefits of such knowledge in resource provisioning of two case studies from the automotive domain, where the actual memory consumption is up to 5.6 × more than the memory requested by the application.

This paper presents a hypervisor architecture tailored to low-power real-time applications. This architecture extends the capability of a hypervisor by providing power management techniques and power monitoring services. An implementation based on an existing hypervisor XtratuM that runs over the ARM of a Zynq-7000 SoC device is proposed as a proof of concept. Measurement results show that the extended hypervisor can obtain information on the power consumption and reduce it.