Software parametrization of feasible reconfigurable real-time systems under energy and dependency constraints

Enforcing temporal constraints is necessary to maintain the correctness of a realtime system. However, a real-time system may be enclosed by many factors and constraints that lead to different challenges to overcome. In other words, to achieve the real-time aspects, these systems face various challenges particularly in terms of architecture, reconfiguration property, energy consumption, and dependency constraints. Unfortunately, the characterization of real-time task deadlines is a relatively unexplored problem in the real-time community. Most of the literature seems to consider that the deadlines are somehow provided as hard assumptions, this can generate high costs relative to the development time if these deadlines are violated at runtime. In this context, the main aim of this thesis is to determine the effective temporal properties that will certainly be met at runtime under well-defined constraints. We went to overcome these challenges in a step-wise manner. Each time, we elected a well-defined subset of challenges to be solved. This thesis deals with reconfigurable real-time systems in mono-core and multi-core architectures. First, we propose a new scheduling strategy based on configuring feasible scheduling of software tasks of various types (periodic, sporadic, and aperiodic) and constraints (hard and soft) mono-core architecture. Then, the second contribution deals with reconfigurable real-time systems in mono-core under energy and resource sharing constraints. Finally, the main objective of the multi-core architecture is achieved in a third contribution.Das Erzwingen zeitlicher Beschränkungen ist notwendig,um die Korrektheit eines Echtzeitsystems aufrechtzuerhalten. Ein Echtzeitsystem kann jedoch von vielen Faktoren und Beschränkungen umgeben sein, die zu unterschiedlichen Herausforderungen führen, die es zu bewältigen gilt. Mit anderen Worten, um die zeitlichen Aspekte zu erreichen, können diese Systeme verschiedenen Herausforderungen gegenüberstehen, einschliesslich Architektur, Rekonfigurationseigenschaft, Energie und Abhängigkeitsbeschränkungen. Leider ist die Charakterisierung von Echtzeit-Aufgabenterminen ein relativ unerforschtes Problem in der Echtzeit-Community. Der grösste Teil der Literatur geht davon aus, dass die Fristen (Deadlines) irgendwie als harte Annahmen bereitgestellt werden, was im Verhältnis zur Entwicklungszeit hohe Kosten verursachen kann, wenn diese Fristen zur Laufzeit verletzt werden. In diesem Zusammenhang ist das Hauptziel dieser Arbeit, die effektiven zeitlichen Eigenschaften zu bestimmen, die zur Laufzeit unter wohldefinierten Randbedingungen mit Sicherheit erfüllt werden. Wir haben diese Herausforderungen schrittweise gemeistert. Jedes Mal haben wir eine wohldefinierte Teilmenge von Herausforderungen ausgewählt, die es zu lösen gilt. Zunächst schlagen wir eine neue Scheduling-Strategie vor, die auf der Konfiguration eines durchführbaren Scheduling von Software-Tasks verschiedener Typen (periodisch, sporadisch und aperiodisch) und Beschränkungen (hart und weich) einer Mono-Core-Architektur basiert. Der zweite Beitrag befasst sich dann mit rekonfigurierbaren Echtzeitsystemen in Mono-Core unter Energie und Ressourcenteilungsbeschränkungen. Abschliessend wird in einem dritten Beitrag das Verfahren auf Multi-Core-Architekturen erweitert

Runtime Adaptation in Embedded Computing Systems using Markov Decision Processes

During the design and implementation of embedded computing systems (ECSs), engineers must make assumptions on how the system will be used after being built and deployed. Traditionally, these important decisions were made at design time for a fleet of ECSs prior to deployment. In contrast to this approach, this research explores and develops techniques to enable adaptation of ECSs at runtime to the environments and applications in which they operate. Adaptation is enabled such that the usage assumptions and performance optimization decisions can be made autonomously at runtime in the deployed system. This thesis utilizes Markov Decision Processes (MDPs), a powerful and well established mathematical framework used for decision making under uncertainty, to control computing systems at runtime. The resulting control is performed in ways that are more dynamic, robust and adaptable than alternatives in many scenarios. The techniques developed in this thesis are first applied to a reconfigurable embedded digital signal processing system. In this effort, several challenges are encountered and resolved using novel approaches. Through extensive simulations and a prototype implementation, the robustness of the adaptation is demonstrated in comparison with the prior state-of-the-art. The thesis continues by developing an efficient algorithm for conversion of MDP models to actionable control policies - a required step known as solving the MDP. The solver algorithm is developed in the context of ECSs that contain general purpose embedded GPUs (graphics processing units). The novel solver algorithm, Sparse Parallel Value Iteration (SPVI), makes use of the parallel processing capabilities provided by such GPUs, and also exploits the sparsity that typically exists in MDPs when used to model and control ECSs. To extend the applicability of the runtime adaptation techniques to smaller and more strictly resource constrained ECSs, another solver - Sparse Value Iteration (SVI) is developed for use on microcontrollers. The method is explored in a detailed case study involving a cellular (LTE-M) connected sensor that adapts to varying communications profiles. The case study reveals that the proposed adaptation framework outperforms a competing approach based on Reinforcement Learning (RL) in terms of robustness and adaptation, while consuming comparable resource requirements. Finally, the thesis concludes by analyzing the various logistical challenges that exist when deploying MDPs on ECSs. In response to these challenges, the thesis contributes an open source software package to the engineering community. The package contains libraries of MDP solvers, parsers, datasets and reference solutions, which provide a comprehensive infrastructure for exploring the trade-offs among existing embedded MDP techniques, and experimenting with novel approaches