Katharina Stahl

BASIC CONCEPTS OF REAL TIME OPERATING SYSTEMS

Real-time applications usually are executed on top of a Real-time Operating System (RTOS). Specific scheduling algorithms can be designed. When possi- ble, static cyclic schedules are calculated off-line. If more flexibility is needed on-line techniques are applied. These algorithms are bound to priorities which can be assigned statically or dynamically. Designing a proper RTOS architec- ture needs some delicate decisions. The basic services like process manage- ment, inter-process communication, interrupt handling, or process synchroniza- tion have to be provided in an efficient manner making use of a very restricted resource budget. Various techniques like library-based approaches, monolithic kernels, microkernels, or virtual machines/exokernels are applied, based on spe- cific demands. Safety critical application can be supported by separation of ap- plications either in the time or the space domain. Multi-core architectures need special techniques for process management, memory management, and synchro- nization. The upcoming Wireless Sensor Networks (WSN) generate special de- mands for RTOS support leading to dedicated solutions. Another special area is given by multimedia applications. Very high data rates have to be supported un- der (soft) RT constraints. Based on the used encoding techniques (e.g. MPEG) dedicated solutions can be created.

Designing Self-Adaptive Embedded Real-Time Software -- Towards System Engineering of Self-Adaptation

Upcoming interlinked embedded systems will be confronted with unexpectedly changing environments, which makes online adaptation without manual interference necessary. There is a need for appropriate system architectures and novel design approaches. In this paper, we discuss general concepts of self-adaptive real-time systems. Furthermore, specific system engineering techniques solving two important aspects of such a paradigm are presented. We discuss how the necessity for adaptation can be identified using Online Model Checking and how self-adapting safety guards can be designed by means of Artificial Immune Systems. Finally, we present an approach to integrating these techniques into an underlying platform architecture based on mixed-criticality virtualization.

Applications Adaptable Execution Path for Operating System Services on a Distributed Reconfigurable System on Chip

The introduction of embedded systems equipped with FPGA having a GPP contained inside them (Reconfigurable SoC (RSoC)) create a lot of challenges to OS for resource management. In distributed RSoCs, different applications may run on different RSoCs with variant resource requirements. Due to the variety of applications, a continuous change in demands from OS services (e.g. expected response-time) may exist, also a continuous change in the availability of resources (power and area). These variations can be managed by enabling the OS services to adapt their execution paths (on FPGA and GPP) depending on the application needs and the availability of resources. In this paper, an algorithm for distributed RSoC systems is introduced that enables OS services to execute on both FPGA and GPP along with a dynamic runtime change in execution paths of these services when needed. The algorithm relies on dynamic programming which provides single-criteria optima by taking each constraint alone. In the second step the algorithm finds a multi-criteria solution by local exchange small parts depending on the single-criteria optima solutions. In total a polynomial time heuristic multi-criteria optimization at runtime is obtained.

A framework for enhancing dependability in self-x systems by Artificial Immune Systems

In self-adapting embedded real-time systems, operating systems and software provide mechanisms to self-adapt to changing requirements. Autonomous adaptation decisions introduce novel risks as they may lead to unforeseen system behavior that could not have been specified within a design-time model. However, as part of its functionality the operating system has to ensure the reliability of the entire self-x system during run-time. In this paper, we present our work in progress for an operating system framework which aims to identify anomalous or malicious system states at run-time without a sophisticated specification-time model. Inspired by the Artificial Immune Systems Danger Theory, we propose an anomaly detection mechanism that operates not only on the local system behavior information of the monitored component. Furthermore, to ensure an efficient behavior evaluation, the anomaly detection mechanism implies system-wide input signals that indicate e.g the existence of a potential danger within the overall system or the occurrence of a system adaption. Due to the ability of this framework to cope with dynamically changing behavior and to identify unintended behavioral deviations, it seems to be a promising approach to enhance the run-time dependability of a self-x system.

The Paradigm of Self-optimization

Machines are ubiquitous. They produce, they transport. Machines facilitate and assist with work. The increasing fusion of mechanical engineering with information technology has brought about considerable benefits. This situation is expressed by the term mechatronics, which means the close interaction of mechanics, electrics/electronics, control engineering and software engineering to improve the behavior of a technical system. The integration of cognitive functions into mechatronic systems enables systems to have inherent partial intelligence. The behavior of these future systems is formed by the communication and cooperation of the intelligent system elements. From an information processing point of view, we consider these distributed systems to be multi-agent-systems. These capabilities open up fascinating prospects regarding the design of future technical systems. The term self-optimization characterizes this perspective: the endogenous adaptation of the system’s objectives due to changing operational conditions. This resuls in an autonomous adjustment of system parameters or system structure and consequently of the system’s behavior. In this chapter self-optimizing systems are described in detail. The long term aim of the Collaborative Research Centre 614 ”Self-Optimizing Concepts and Structures in Mechanical Engineering” is to open up the active paradigm of self-optimization for mechanical engineering and to enable others to develop these systems. For this, developers have to face a number of challenges, e.g. the multidisciplinarity and the complexity of the system. This book povides a design methodology that helps to master these challenges and to enable third parties to develop self-optimizing systems by themselves.

Development of Self-optimizing Systems

In the development of self-optimizing systems, developers have to face different challenges, such as the multidisciplinarity of mechatronics, cross-linking between the subsystems, the lack of current knowledge in the fields of advanced mathematics and artificial intelligence, and increased dependability requirements. To support the developer in this challenging task adequately, the Collaborative Research Center 614 has developed a design methodology consisting of a reference process, methods and tools. The reference process is divided into two phases: the ”Domain-Spanning Conceptual Design” and the ”Domain-Specific Design and Development”. In the first phase, the domain-spanning model-based aproach CONSENS (CONceptual design Specification technique for the ENgineering of complex Systems) is used to create a common understanding basis between the different domains involved. Based on the Principle Solution developed in this phase, the ”Domain-Specific Design and Development” is planned and implemented. To ensure the development of dependable self-optimizing systems, domain-spanning and domain-specific dependability engineering methods can be used. The developer encounters a significant number of these methods, that have to be integrated into the process to obtain an individualized development process for the specific development task.

Organic real-time programming — Vision and approaches towards self-evolving and adaptive real-time software

For upcoming Cyber Physical Systems with a high need of adaptation to changing environments an appropriate programming approach is needed. In this paper we argue that such systems have to be highly adaptive and self-evolving. The general vision and approach is pointed out. Furthermore specific approaches solving important aspects of such a programming paradigm are presented. The aspects discussed include the identification of adaptation needs using online Model Checking, real-time-aware adaptation mechanisms, and self-adapting safety guards by means of Artificial Immune Systems.

Evaluation of Autonomous Approaches Using Virtual Environments

In this paper, we address the challenging problem of evaluating autonomous research approaches by the example of an online anomaly detection framework for dynamical real-time systems. We propose to use a virtual test environment that was conceptualized based on the specific evaluation requirements. The architecture is composed of all system parts required for evaluation: the operating system implementing the anomaly detection framework, reconfigurable autonomous applications, an execution platform device for the operating system and its applications, and the device’s environment. We demonstrate our concepts by the example of our miniature robot BeBot that acts as our virtual prototype (VP) to execute autonomous applications. With an interactive module, the virtual environment (VE) offers full control over the environment and the VP so that using different levels of hardware implementation for evaluation, but also failure injection at runtime becomes possible. Our architecture allows to determine clear system boundaries of the particular parts composed of perception function, decision making function and execution function which is essential for evaluating autonomous approaches. We define evaluation scenarios to show the effectiveness of each part of our approach and illustrate the powerfulness of applying virtual test environments to evaluate such approaches as the here referred one.

Methods for the Design and Development

After the domain-spanning conceptual design, engineers from different domains work in parallel and apply their domain-specific methods and modeling languages to design the system. Vital for the successful design, are system optimization methods and the design of the reconfiguration behavior. The former methods enable the parametric adaption of the system’s behavior, e.g. an adaption of controller parameters, according to a current selection of the system’s objectives. The latter realizes structural adaption of the system’s behavior, e.g. the exchange of software or hardware parts. Altogether, this leads to a complex system behavior that is hard to overview. In addition, self-optimizing systems are used in safety-critical environments. Consequently, the system’s safety-critical behavior has to undergo a rigorous verification and testing process. Existing design methods do not address all of these challenges together. Indeed, a combination of established design methods for traditional technical systems with novel methods that focus on these challenges is necessary. In this chapter, we will focus on such new methods. We will introduce new system optimization and design methods to develop reconfigurations of the software and the microelectronics. In order to ensure the correctness of safety-critical functionality, we propose new testing methods and formal methods to ensure safety-properties of the software. We show how to apply virtual prototyping to deal with the complexity of self-optimizing systems and perform an early analysis of the overall system. As each domain applies its own modeling languages, the result of these methods are several overlapping models. In order to keep these domain-specific models consistent among all domains, we will introduce a new semi-automatic model synchronization technique. Each of these design methods are integrated with the reference process for the development of self-optimizing systems.