The European automotive industry is undergoing a revolution by the upcoming technologies of software-defined vehicles (SDV) and connected cooperative and automated mobility (CCAM). In a globally challenging context, in which Europe lost market share, the local automotive software and electronics market still expects a 11.9% compound annual growth rate from 2025 to 2030 – even more accentuated with the increasing adoption of advanced driver assistance (ADAS) and autonomous driving (AD). Both SDV and CCAM are key technological paradigms for enabling a shift of the European automotive sector towards a regained strategic competitiveness. Targeting the resulting need for faster development, deployment and test cycles the Horizon Europe RIA UP2DATE4SDV aims to develop a comprehensive ecosystem for seamless and efficient, safe and secure software updates, hardware upgrades, and situation-dependent reconfigurations of SDVs. For that goal, the UP2DATE4SDV consortium collaborates on the definition and development of two abstraction layers – the hardware abstraction layer and the operating system & middleware abstraction layer – as well as on researching and prototyping a safe and secure orchestration and reconfiguration plane between vehicle and cloud. Based on the resulting modular architecture concept and the corresponding DevOps process the project develops demonstrators to showcase safe and secure updates, upgrades and dynamic task reallocation for automotive hardware and software components. In this paper, we introduce the project, its objectives and planned results, draft first outcomes by refining our demonstrator definitions, and conclude with an outlook into the automotive future based on a safe and secure adaptive SDV stack.

The ever-increasing demand for scalable database systems is limited by their energy consumption, which is one of the major challenges in research today. While existing approaches mainly focused on transaction-oriented disk-based database systems, we are investigating and optimizing the energy consumption and performance of data-oriented scale-up in-memory database systems that make heavy use of the main power consumers, which are processors and main memory. In this demo, we present energy-utility functions as an approach for enabling the operating system to improve the energy efficiency of scalable in-memory database systems. Our highly interactive demo setup mainly allows attendees to switch between multiple DBMS workloads and watch in detail how the system responds by adapting the hardware configuration appropriately.

Data centres and similar server clusters consume a large amount of energy. However, not all consumed energy produces useful work. Servers consume a disproportional amount of energy when they are idle, underutilised, or overloaded. The effect of these conditions can be minimised by attempting to balance the demand for and the supply of resources through a careful prediction of future workloads and their efficient consolidation. In this paper we extend the cutting stock problem for consolidating workloads having stochastic characteristics. Hence, we employ the aggregate probability density function of co-located and simultaneously executing services to establish valid patterns. A valid pattern is one yielding an overall resource utilisation below a set threshold. We tested the scope and usefulness of our approach on a 16-core server with 29 different benchmarks. The workloads of these benchmarks have been generated based on the CPU utilisation traces of 100 real-world virtual machines which we obtained from a Google data centre hosting more than 32000 virtual machines. Altogether, we considered 600 different consolidation scenarios during our experiment. We compared the performance of our approach-system overload probability, job completion time, and energy consumption-with four existing/proposed scheduling strategies. In each category, our approach incurred a modest penalty with respect to the best performing approach in that category, but overall resulted in a remarkable performance clearly demonstrating its capacity to achieve the best trade-off between resource consumption and performance.

The architectures of large-scale Internet servers are becoming more complex each year in order to store and process a large amount of Internet data (Big Data) as efficiently as possible. One of the consequences of this continually growing complexity is that individual servers consume a significant amount of data even when they are idle. In this paper we experimentally investigate the scope and usefulness of existing and proposed dynamic power management strategies to manage power at core, socket, and server levels. Our experiment involves four dynamic voltage and frequency scaling policies, three different workloads having different resource consumption statistics, and the activation and deactivation of different sockets (packets) of a multicore, multi-socket server. Moreover, we establish a quantitative relationships between the workload (w) and the estimated power consumption (p) under different power management strategies to make a quantitative comparison of the different strategies and server configurations.

For embedded system software, it is common to use static mappings of tasks to cores. This becomes considerably more challenging in multi-application scenarios. In this paper, we propose TETRiS, a multi-application run-time system for static mappings for heterogeneous system-on-chip architectures. It leverages compile-time information to map and migrate tasks in a fashion that preserves the predictable performance of using static mappings, allowing the system to accommodate multiple applications. TETRiS runs on off-the-shelf embedded systems and is Linux-compatible. We embed our approach in a state-of-the-art compiler for multicore systems and evaluate the proposed run-time system in a modern heterogeneous platform using realistic benchmarks. We present two experiments whose execution time and energy consumptions are comparable to those obtained by the highly-optimized Linux scheduler CFS, and where execution time variance is reduced by a factor of 510, and energy consumption variance by a factor of 83.

Our works on energy/utility present an approach for energy-efficient resource management that considers the utility requirements of users and the energy restriction that are set by them. In this demo, we show an initial prototype of our approach, demonstrating energy/utility trade-offs in an example application and how modeling individual components and their interaction can simplify the scheduling of resources.

In the past, we have silently accepted that energy consumption in real-time and embedded systems is subordinate to time. That is, we have tried to reduce energy always under the constraint that all deadlines must be met. In mixed-criticality systems however, schedulers respect that some tasks are more important than others and guarantee their completion even at the expense of others. We believe in these systems the role of the energy budget has changed and it is time to ask whether energy has surpassed timeliness. Investigating energy as a further dimension of mixed-criticality systems, we show in a realistic scenario that a subordinate handling of energy can lead to violations of the mixed-criticality guarantees that can only be avoided if energy becomes an equally important resource as time.

Energy has become the dominating concern for resource management. We advocate an energy-centered design approach for resource-management systems. To this end, we structure systems in layers, where layers implement higher-level resources using lower-level ones. For each layer, we describe the relation of the performance delivered for the higher layer to its demands on the lower layer and refer to that relation as demand/performance function. The lowest layers are rooted in hardware and express demand in terms of energy, the highest layers provide performance in terms of user-specific utility, thus leading to an Energy/Utility characterization of a complete system. We describe the overall approach, some research challenges and few initial results on the representation of demand/performance functions.