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Today's systems offer an increasing number of processor cores, however, the chance to operate them efficiently by dedicating cores to specific tasks is often missed. Instead, mixed workloads are processed by each core which leads to system noise (i.e., interferences, scheduling overheads) and yields subpar performance, only. We therefore propose a system design based on Asynchronous Abstract Machines (AAMs). An AAM features a light-weight scheduler and is dedicated to a specific group of tasks with common characteristics (i.e., shared code and data). It offers an asynchronous, task-based interface for efficient interaction between AAMs. Just like applications are built from AAMs, even the OS itself consists of AAMs that are interfaced by applications via asynchronous messages instead of synchronous system calls. A dedicated OS component, which is aware of all AAMs in the system, is responsible for dynamic and exclusive allocation of cores to AAMs depending on their current workload. Thus, cores rarely switch between heterogeneous workloads of different AAMs. And, at the same time, frequent switches between homogeneous tasks become fast, local operations of an AAM, which do not involve the OS kernel. In this paper, we describe shortcomings of existing operating systems, our new system design concept, and present evaluation results of our prototype implementation.

Reliable and fine-grained cost-models are fundamental for real-time systems to statically predict worst-case execution time (WCET) estimates of program code in order to guarantee timeliness. Analogous considerations hold for energy-constrained systems where worst-case energy consumption (WCEC) values are mandatory to ensure meeting predefined energy budgets. These cost models are generally unavailable for commercial off-the-shelf (COTS) hardware platforms, although static worst-case analysis tools require those models in order to predict the WCET as well as the WCEC of program code. To solve this problem, we present NEO, an end-to-end toolchain to automate cost-model generation for both WCET and WCEC analyses. NEO exploits automatically generated benchmarks, which are input for 1) an instruction-level emulation and 2) automatically conducted execution-time and energy-consumption measurements on the target platform. The gathered values (i.e., occurrences per instruction, execution-time and energy-consumption per benchmark) are combined as mathematical optimization problems. The solutions to the formulated problems, which are designed to reveal the worst-case behavior, yield the respective cost models. To statically determine upper bounds of benchmarks, we integrated the cost models into the state-of-the-art WCET analyzer PLATIN. Our evaluations on COTS hardware reveal that our open-source, end-to-end toolchain NEO yields accurate worst-case bounds.

Precisely evaluating the accuracy of worst-case execution time (WCET) analysis tools through benchmarking is inherently difficult and in general involves a significant amount of manual intervention. In this paper, we address this problem with ALADDIN, a tooling framework that enables fully-automated evaluations of WCET analyzers. To provide comprehensive results based on benchmarks with known WCETs, ALADDIN incorporates the GENE benchmark generator. Our demonstration shows how ALADDIN evaluates two state-of-the-art WCET analyzers: the commercial tool aiT and the open-source tool PLATIN.

The worst-case response time (WCRT) – the time span from release to completion of a real-time task – is a crucial property of real-time systems. However, WCRT analysis is complex in practice, as it depends not only on the realistic examination of worst-case execution times (WCET), but also on system-level overheads and blocking/preemption times. While the implicit path enumeration technique (IPET) has greatly improved automated WCET analysis, the resulting values still need to be aggregated manually with the system-level overheads – an errorprone and tedious process that yields overly pessimistic results. With SysWCET, we provide an integrated approach for the automated WCRT analysis across multiple threads of execution, locks, interrupt service routines, and the real-time operating system (RTOS) in particular. Our approach spans a single IPET formulation over the whole system and exploits RTOS and scheduler semantics to derive cross-kernel flow facts in order to significantly reduce pessimism in the WCRT analysis. We evaluate our approach with a fully functional implementation of SysWCET for the automotive OSEK-OS standard (ECC1), including threads, alarms, interrupt-service routines, events, and PCP-based resource management.

Being able to comprehensively evaluate the individual strengths and weaknesses of worst-case execution time (WCET) analysis tools through benchmarking is essential for improving their accuracy. Unfortunately, a lack of knowledge about the detailed characteristics, actual complexities, and internal structures of existing benchmarks often prevents finegrained assessments, and sometimes even results in misleading conclusions. In this paper we present GENE, a tool that addresses these problems by automatically generating WCET benchmarks with known properties and predefined complexities. Due to the WCETs of benchmarks created by GENE being available, this approach for example makes it possible to precisely determine the accuracy of a WCET analyzer. In addition, the fact that GENE controls the program patterns of a benchmark enables fine-grained evaluations of the particular abilities and deficiencies of different WCET analyzers, as we demonstrate for aiT and PLATIN using multiple hardware platforms.

The fact that energy is a scarce resource in many embedded real-time systems creates the need for energy-aware task schedulers, which not only guarantee timing constraints but also consider energy consumption. Unfortunately, existing approaches to analyze the worst-case execution time (WCET) of a task usually cannot be directly applied to determine its worst-case energy consumption (WCEC) due to execution time and energy consumption not being closely correlated on many state-of-the-art processors. Instead, a WCEC analyzer must take into account the particular energy characteristics of a target platform. In this paper, we present 0g, a comprehensive approach to WCEC analysis that combines different techniques to speed up the analysis and to improve results. If detailed knowledge about the energy costs of instructions on the target platform is available, our tool is able to compute upper bounds for the WCEC by statically analyzing the program code. Otherwise, a novel approach allows 0g to determine the WCEC by measurement after having identified a set of suitable program inputs based on an auxiliary energy model, which specifies the energy consumption of instructions in relation to each other. Our experiments for three target platforms show that 0g provides precise WCEC estimates.