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The reliable operation of systems with both timing and energy requirements is a fundamental challenge in the area of safety-critical embedded systems. In order to provide guarantees for the execution of tasks within given resource budgets, these systems demand bounds of the worst-case execution time (WCET) and the worst-case energy consumption (WCEC). While static WCET analysis techniques are well established in the software development process of real-time systems nowadays, these program analysis techniques are not directly applicable to the fundamentally different behavior of energy consumption and the determination of the WCEC. Besides the missing approaches for WCEC bounds, the domain of worst-case analyses generally faces the problem that the accuracy and validity of reported analysis bounds are unknown: Since the actual worst-case resource consumption of existing benchmark programs cannot be automatically determined, a comprehensive validation of these program analysis tools is not possible.This summary of my dissertation addresses these problems by first describing a novel program analysis approach for WCEC bounds, which accounts for temporarily power-consuming devices, scheduling with fixed real-time priorities, synchronous task activations, and asynchronous interrupt service routines. Regarding the fundamental problem of validating worst-case tools, this dissertation presents a technique for automatically generating benchmark programs. The generator combines program patterns so that the worst-case resource consumption is available along with the generated benchmark. Knowledge about the actual worst-case resource demand then serves as the baseline for evaluating and validating program analysis tools. The fact the benchmark generator helped to reveal previously undiscovered software bugs in a widespread WCET tool for safety-critical systems underlines the relevance of such a structured testing technique.

Predicting the temporal behavior of embedded real-time systems is a crucial but challenging task, as it is with the energetic behavior of energy-constrained systems, such as IoT devices. To carry out static analyses in order to determine the worst-case execution time or the worst-case energy consumption of tasks, cost models are inevitable. However, these models are rarely available on a fine-grained level for commercial-off-the-shelf hardware platforms. In this letter, we present NEO, an end-to-end toolchain that automatically generates cost models, which are then integrated into an existing static-analysis tool. NEO exploits automatically generated benchmark programs, which are measured on the target platform and investigated in a virtual machine. Based on the gathered data, we formulate mathematical optimization problems that eventually yield both worst-case execution-time and energy-consumption cost models. In our evaluations with an embedded hardware platform (e.g., ARM Cortex-M0+), we show that the open-source toolchain is able to precisely bound programs’ resources while achieving acceptable accuracy.

Predictable and analyzable I/O is one of the considerable challenges in the design of multi-core real-time systems. A common approach to tackle this issue is to partition and schedule I/O transactions such that interference between tasks is minimized. While this works for packet-oriented interfaces with deterministic blocking times, such as ethernet, these techniques are inapplicable to a whole range of I/O devices with nondeterministic behavior that is commonly found in embedded applications. Interfaces, such as SPI, do not allow for fine-grained scheduling and thus exhibit uncontrolled blocking times. Even worse, their configuration and use must be considered as independent transactions requiring costly synchronization between tasks. The resulting detrimental effects are, in particular, pronounced in settings with mixed task requirements on predictability and determinism. All this makes the temporal analysis of such systems cumbersome and overly pessimistic. To solve these issues, we present $LOW_{I/O}$ , an approach to eliminate the interference of low-level non-deterministic I/O interfaces for real-time tasks with high predictability demands (i.e., critical task) while preserving flexibility for tasks with lower requirements (i.e., uncritical tasks). Therefore, we leverage knowledge about the application-specific I/O usage patterns, obtained by static analysis, to derive a tailored hardware architecture. Its key feature is the anticipatory reservation of individual time slots for critical tasks and to mimic preemptivity of I/O units for the remaining system. We have implemented our approach as a toolchain for OSEK-based real-time systems that automatically generates an application-specific SoC design along with a hardware and timing model for subsequent WCET analysis. Our experimental results prove predictable timing for critical tasks with limited impact on uncritical tasks.

Recent advances in animal tracking technology have ushered in a new era in biologging. However, the considerable size of many sophisticated biologging devices restricts their application to larger animals, whereas older techniques often still represent the state-of-the-art for studying small vertebrates. In industrial applications, low-power wireless sensor networks (WSNs) fulfill requirements similar to those needed to monitor animal behavior at high resolution and at low tag mass. We developed a wireless biologging network (WBN), which enables simultaneous direct proximity sensing, high-resolution tracking, and long-range remote data download at tag masses of 1 to 2 g. Deployments to study wild bats created social networks and flight trajectories of unprecedented quality. Our developments highlight the vast capabilities of WBNs and their potential to close an important gap in biologging: fully automated tracking and proximity sensing of small animals, even in closed habitats, at high spatial and temporal resolution., Recent advances in technology potentially allow great progress in animal tracking. This article presents a wireless biologging network (WBN) that enables simultaneous direct proximity sensing, high-resolution tracking, and long-range remote data download at tag weights of only one to two grams.

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.

The static timing analysis of universal real-time operating systems (RTOS) with generically implemented services requires application and system-context–specific knowledge (e.g., number of currently active tasks) to bound overheads. However, due to the missing notion of OS semantics, contemporary timing analysis tools are unable to exploit such information, resulting in failing or overly pessimistic analysis. To tackle this issue, we present our System-wide WCET Analyses framework (SWAN). SWAN's heart is Platina, a parametric source-level annotation language that facilitates the expression and propagation of context information from the application over the OS down to the machine-code level. Through the expression of semantic interdependencies in a unified and reusable way, analysis pessimism is significantly reduced, as we demonstrate by case studies on FreeRTOS, Linux, and a real-world flight-control system. Just as important as our system-aware timing analysis is the tool support for its practical usability. Therefore, we augmented SWAN by a powerful interactive visualization and annotation environment. This enables developers to quickly identify context-dependent spots that require annotation and thus to cope with large implementations associated with universal RTOSs. Eventually, SWAN allows determining if a generically implemented system is real-time capable and thus timeliness is guaranteed.

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.