Scheduling of aperiodic and independent tasks in hard real-time symmetric multiprocessing systems is an NP-complete problem that is often solved using heuristics like particle swarm optimization (PSO). The performance of these class of heuristics, known as evolutionary algorithms, are often evaluated based on the number of iterations it takes to find a solution. Such metrics provide limited information on how the algorithm reaches a solution and how the process could be accelerated. This thesis presents a methodology to analyze the trajectory formed by candidate solutions in order to analyze them in both the time and frequency domains at a single scale. The analysis entails (i) the impact of different parameters for the PSO algorithm, and (ii) the evolutionary processes in the swarm. The work reveals that particles have a directed movement towards a solution during a transient phase, and then enter a steady state where they perform an unguided local search. The scheduling algorithm presented in this thesis uses a variation of the minimum total tardiness with cumulative penalties cost function, that can be extended to suit different system needs. The experimental results show that the scheduler is able to distribute tasks to meet the real-time deadlines over 1, 2, and 4 processors and up to 30 tasks with overall system loads of up to 50\% in fewer than 1,000 iterations. When scheduling greater loads, the scheduler reaches local solutions with 1 to 2 missed deadlines, while larger tasks sets take longer to converge. The trajectories of the particles during the scheduling algorithm are examined as a means to emphasize the impact of the behaviour on the application performance and give insight into ways to improve the algorithm for both space and terrestrial applications.

Ubiquitous deployment of embedded systems is having a substantial impact on our society, since they interact with our lives in many critical real-time applications. Typically, embedded systems used in safety or mission critical applications (e.g., aerospace, avionics, automotive or nuclear domains) work in harsh environments where they are exposed to frequent transient faults such as power supply jitter, network noise and radiation. They are also susceptible to errors originating from design and production faults. Hence, they have the design objective to maintain the properties of timeliness and functional correctness even under error occurrences. Fault-tolerance plays a crucial role towards achieving dependability, and the fundamental requirement for the design of effective and efficient fault-tolerance mechanisms is a realistic and applicable model of potential faults and their manifestations. An important factor to be considered in this context is the random nature of faults and errors, which, if addressed in the timing analysis by assuming a rigid worst-case occurrence scenario, may lead to inaccurate results. It is also important that the power, weight, space and cost constraints of embedded systems are addressed by efficiently using the available resources for fault-tolerance. This thesis presents a framework for designing predictably dependable embedded real-time systems by jointly addressing the timeliness and the reliability properties. It proposes a spectrum of fault-tolerance strategies particularly targeting embedded real-time systems. Efficient resource usage is attained by considering the diverse criticality levels of the systems' building blocks. The fault-tolerance strategies are complemented with the proposed probabilistic schedulability analysis techniques, which are based on a comprehensive stochastic fault and error model.

Nowadays, different companies use Ethernet for different industrial applications. Industrial Ethernet has some specific requirements due to its specific applications and environmental conditions which is the reason that makes it different than corporate LANs. Real-time guarantees, which require precise synchronization between all communication devices, as well as reliability are the keys in performance evaluation of different methods [1].  High bandwidth, high availability, reduced cost, support for open infrastructure as well as deterministic architecture make packet-switched networks suitable for a variety of different industrial distributed hard real-time applications. Although research on guaranteeing timing requirements in packet-switched networks has been done, communication reliability is still an open problem for hard real-time applications. In this thesis report, a framework for enhancing the reliability in multihop packet-switched networks is presented. Moreover, a novel admission control mechanism using a real-time analysis is suggested to provide deadline guarantees for hard real-time traffic. A generic and flexible simulator has been implemented for the purpose of this research study to measure different defined performance metrics. This simulator can also be used for future research due to its flexibility. The performance evaluation of the proposed solution shows a possible enhancement of the message error rate by several orders of magnitude, while the decrease in network utilization stays at a reasonable level.

Software engineering is a fundamentally collaborative activity, yet most tools that support software engineers are designed only for single users. There are many foreseen benefits in using tools that support real time collaboration between software engineers, such as avoiding conflicting concurrent changes to source files and determining the impact of program changes immediately. Unfortunately, it is difficult to develop non-trivial tools that support real time Collaborative Software Engineering (CSE). Accordingly, the few CSE tools that do exist have restricted capabilities. Given the availability of powerful desktop workstations and recent advances in distributed computing technology, it is now possible to approach the challenges of CSE from a new perspective. The research goal in this thesis is to investigate mechanisms for supporting real time CSE, and to determine the potential gains for developers from the use of CSE tools. An infrastructure, CAISE, is presented which supports the rapid development of real time CSE tools that were previously unobtainable, based on patterns of collaboration evident within software engineering. In this thesis, I discuss important design aspects of CSE tools, including the identification of candidate patterns of collaboration. I describe the CAISE approach to supporting small teams of collaborating software engineers. This is by way of a shared semantic model of software, protocol for tool communication, and Computer Supported Collaborative Work (CSCW) facilities. I then introduce new types of synchronous semantic model-based tools that support various patterns of CSE. Finally, I present empirical and heuristic evaluations of typical development scenarios. Given the CAISE infrastructure, it is envisaged that new aspects of collaborative work within software engineering can be explored, allowing the perceived benefits of CSE to be fully realised.

CPS (Cyber-Physical Systems) enable a new class of applications that perceive their surroundings using raw data from sensors, monitor the timing of dynamic processes, and control the physical environment. Since failures and misbehaviors in application domains such as cars, medical devices, nuclear power plants, etc., may cause significant damage to life and/or property, CPS need to be safe and dependable. A conventional way of improving dependability is to use redundant hardware to replicate the whole (sub)system. Although hardware replication has been widely deployed in conventional mission-critical systems, it is cost-prohibitive to many emerging CPS application domains. Hardware replication also leads to limited system flexibility. This dissertation studies the problem of making CPS affordably dependable and develops a system-level framework that manages critical CPS resources including processors, networks, and sensors. Our framework called SAFER (System-level Architecture for Failure Evasion in Real-time applications) incorporates configurable software mechanisms and policies to tolerate failures of critical CPS resources while meeting their timing constraints. It supports adaptive graceful degradation, the effective use of different sensor modalities, and the fault-tolerant schemes of hot standby, cold standby, and re-execution. SAFER reliably and efficiently allocates tasks and their backups to CPU and sensor resources while satisfying network traffic constraints. It also fuses and (re)configures sensor data used by tasks to recover from system failures. The SAFER framework aims to guarantee the timeliness of different types of tasks that fall into one of four categories: (1) tasks with periodic arrivals, (2) tasks with continually varying periods, (3) tasks with parallel threads, and (4) tasks with self-suspensions. We offer the schedulability analyses and runtime support for such tasks with and without resource failures. Finally, the functionality of the proposed system is evaluated on a self-driving car using SAFER. We conclude that the proposed framework analytically satisfies timing constraints and predictably operates systems with and without resource failures, hence making CPS dependable and timely.

The requirements for real-time systems in safety-critical applications typically contain strict timing constraints. The design of such a system must be subject to extensive validation to guarantee that critical timing constraints will never be violated while the system operates. A mathematically rigorous technique to do so is to perform a schedulability analysis for formally verifying models of the computational workload. Different workload models allow to describe task activations at different levels of expressiveness, ranging from traditional periodic models to sophisticated graph-based ones. An inherent conflict arises between the expressiveness and analysis efficiency of task models. The more expressive a task model is, the more accurately it can describe a system design, reducing over-approximations and thus minimizing wasteful over-provisioning of system resources. However, more expressiveness implies higher computational complexity of corresponding analysis methods. Consequently, an ideal model provides the highest possible expressiveness for which efficient exact analysis methods exist. This thesis investigates the trade-off between expressiveness and analysis efficiency. A new digraph-based task model is introduced, which generalizes all previously proposed models that can be analyzed in pseudo-polynomial time without using any analysis-specific over-approximations. We develop methods allowing to efficiently analyze variants of the model despite their strictly increased expressiveness. A key contribution is the notion of path abstraction which enables efficient graph traversal algorithms. We demonstrate tractability borderlines for different classes of schedulers, namely static priority and earliest-deadline first schedulers, by establishing hardness results. These hardness proofs provide insights about the inherent complexity of developing efficient analysis methods and indicate fundamental difficulties of the considered schedulability problems. Finally, we develop a novel abstraction refinement scheme to cope with combinatorial explosion and apply it to schedulability and response-time analysis problems. All methods presented in this thesis are extensively evaluated, demonstrating practical applicability.

A real-time system is a system such that the correctness of operations depends not only on the logical results, but also on the time at which these results are available. A fundamental problem in designing real-time systems is to analyze response time of operations, which is defined as the time elapsed from the moment when the operation is requested to the moment when the operation is completed. Response time analysis is challenging due to the complex dynamics among operations. A common technique is to study response time under worst-case scenario. However, using worst-case response time may lead to the conservative real-time system designs. To improve the real-time system design, we analyze the non-worst-case response time of operations and apply these results in the design process. The main contribution of this thesis includes mathematical modeling of real-time systems, calculation of non-worst-case response time, and improved real-time system design. We perform analysis and design on three common types of real-time systems as the real-time computing system, real-time communication network, and real-time energy management. For the real-time computing systems, our non-worst-response time analysis leads a necessary and sufficient online schedulability test and a measure of robustness of real-time systems. For the real-time communication network, our non-worst-response time analysis improves the performance for the model predictive control design based on the real-time communication network. For the real-time energy management, we use the non-worst-case response time to check whether the micro-grid can operate independently from the main grid.

Scheduling of aperiodic and independent tasks in hard real-time symmetric multiprocessing systems is an NP-complete problem that is often solved using heuristics like particle swarm optimization (PSO). The performance of these class of heuristics, known as evolutionary algorithms, are often evaluated based on the number of iterations it takes to find a solution. Such metrics provide limited information on how the algorithm reaches a solution and how the process could be accelerated. This thesis presents a methodology to analyze the trajectory formed by candidate solutions in order to analyze them in both the time and frequency domains at a single scale. The analysis entails (i) the impact of different parameters for the PSO algorithm, and (ii) the evolutionary processes in the swarm. The work reveals that particles have a directed movement towards a solution during a transient phase, and then enter a steady state where they perform an unguided local search. The scheduling algorithm presented in this thesis uses a variation of the minimum total tardiness with cumulative penalties cost function, that can be extended to suit different system needs. The experimental results show that the scheduler is able to distribute tasks to meet the real-time deadlines over 1, 2, and 4 processors and up to 30 tasks with overall system loads of up to 50\% in fewer than 1,000 iterations. When scheduling greater loads, the scheduler reaches local solutions with 1 to 2 missed deadlines, while larger tasks sets take longer to converge. The trajectories of the particles during the scheduling algorithm are examined as a means to emphasize the impact of the behaviour on the application performance and give insight into ways to improve the algorithm for both space and terrestrial applications.

This thesis explores the possibility of the functional programming paradigm in the domain of hard embedded real-time systems. The implementation consists of re-implementing an already developed system that is written with the imperative and object oriented paradigms. The functional implementation of the system in question is compared with the original implementation and a study of code complexity, timing properties, CPU utilization and memory usage is performed. The implementation of this thesis consists of re-developing three of the periodic tasks of the original system and the whole development process is facilitated with the TDD development cycle. The programming language used in this thesis is C but with a functional approach to the problem. We conclusions of this thesis is that the functional implementation will give a more stable, reliable and readable system but some code volume, memory usage and CPU utilization overhead is present. The main benefit of using the functional paradigm in this type of system is the ability of using the TDD development cycle. The main con of this type of implementation is that it relies heavily on garbage collection due to the enforcement of data immutability. We find in conclusion that one can only use the functional paradigm if one has an over dimensioned system when it comes to hardware, mainly when it comes to memory size and CPU power. When developing small systems with scarce resources one should choose another paradigm.

In 1994, Alur and Dill introduced timed automata as a simple mathematical model for modelling the behaviour of real-time systems. In this thesis, we extend timed automata with weights. More detailed, we equip both the states and transitions of a timed automaton with weights taken from an appropriate mathematical structure. The weight of a transition determines the weight for taking this transition, and the weight of a state determines the weight for letting time elapse in this state. Since the weight for staying in a state depends on time, this model, called weighted timed automata, has many interesting applications, for instance, in operations research and scheduling. We give characterizations for the behaviours of weighted timed automata in terms of rational expressions and logical formulas. These formalisms are useful for the specification of real-time systems with continuous resource consumption. We further investigate the relation between the behaviours of weighted timed automata and timed automata. Finally, we present important decidability results for weighted timed automata.