Future automotive architectures are expected to transition from a network-centric to a domain-centered architecture featuring central compute units. Powerful domain controllers or smart sensors alleviate the load on these central units and communication systems. These controllers execute tasks with varying criticalities on heterogeneous multicore processors, and are ideally capable of dynamically balancing the computing load between the central unit and sensors. Here, Artificial Intelligence (AI) capabilities playa crucial role, as it is in high demand for such an automotive architecture. However, AI still requires specialized accelerators to improve their computation performance. Task-oriented distributed computing with criticalities up to ASIL-D necessitates the development and utilization of specialized methodologies, such as safety, through the isolation and abstraction of low-level hardware concepts. Meanwhile, online monitoring and diagnostics become vital features to detect errors during operation. The EMDRIVE architecture includes methods, components, and strategies to enhance the performance, safety, and security of such distributed computing platforms. The nationally funded EMDRIVE project connects its twelve partners from academia and industry and is currently in its intermediate stage.

Distributed computing capabilities at the network’s edge enable new use cases, e.g., smart factories, industrial internet of things, or autonomous mobility systems. While new applications evolve, managing the resources and being capable of integrating and adjusting the execution of tasks in a distributed edge infrastructure is of great importance. For this, applications need to be migrated between different nodes. These migrations must happen without interruption, allowing the system to meet service requirements while fully utilizing all available hardware resources. Therefore, applications should also be executable on all different compute nodes in a heterogeneous edge system without interfering with each other. To this end, applications should be granted only necessary permission, especially when un-trusted applications are integrated into the system. We, therefore, propose a migration method for isolated applications across heterogeneous compute nodes in service-oriented edge architectures. A service-oriented architecture is used to decouple applications, allowing for flexible scheduling. The migration method enables the fast migration of sandboxed applications based on WebAssembly by utilizing a two-stage migration approach. The concept can utilize multiple communication protocols for management and service communication. We have implemented a proof of concept based on the Zenoh1communication protocol. While the time required depends on the communication protocol and the memory size, we achieved migration delays of under 51 milliseconds for smaller applications. By providing a method for fast migration for applications across heterogeneous compute nodes, it is possible for distributed edge infrastructures to run applications independently from each other and adjust the execution node based on changes in the system’s environment. By integrating applications into our framework, they are executed isolated and with strict access control, allowing for easier reuse.

Complex edge systems are often structured with service-oriented architectures. Different communications stacks such as MQTT, DDS, or Zenoh are used, hindering reuse of service implementations across systems. One emerging solution for deploying such services is WebAssembly, which enables platform-independent, secure, and low-overhead execution. We propose a concept for integrating Web-Assembly modules into microservice-based architectures using a specialized runtime. This runtime manages the communication between the WebAssembly module and other parts of the system. The runtime for integration of WebAssembly addresses the challenge of reusing service implementations across systems with different communication protocols. At the same time, this provides isolated, safe and secure execution. Both capabilities are central to service-oriented edge systems.

Autonomous and distributed Industrial Internet of Things (IIoT) systems are increasingly developed and deployed. They have an enormous demand for resilience and availability. At the same time, they are in a constantly changing system environment. The underlying edge computing infrastructure is characterized by ever increasing processing power and connectivity as well as a high degree of decentralization. To reduce downtime and long redesign loops, self-adaptation capabilities are needed. Automatic reallocation of the executed tasks to the compute nodes is a possible self-adaptation measure. However, the reallocation should be compliant with the different demands, constraints and specifications of the design. At the same time, a major challenge is that the allocation decision should be fast enough to be calculated at runtime. This paper therefore proposes an allocation method that uses demands in the form of policies to compute automatic reallocation at runtime. The integration of the allocation method into runtime is enabled by combining constraint programming, step-wise multi-criteria solution approaches, and resource management at multiple levels. The policy-based allocation method is tested and evaluated in the context of a smart factory site for the function offloading of automated guided vehicles (AGVs) and driverless micromobiles. Our results show that the allocation method is capable of recalculating the allocation during runtime in milliseconds while maintaining design conformity. This enables the system to react to changes in the environment, thereby reducing the downtime of decentralized Industrial Internet of Things systems and increasing availability.