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

Energy harvesting earliest deadline first scheduling algorithm for increasing lifetime of real time systems

In this paper, a new approach for energy minimization in energy harvesting real time systems has been investigated. Lifetime of a real time systems is depend upon its battery life.  Energy is a parameter by which the lifetime of system can be enhanced.  To work continuously and successively, energy harvesting is used as a regular source of energy. EDF (Earliest Deadline First) is a traditional real time tasks scheduling algorithm and DVS (Dynamic Voltage Scaling) is used for reducing energy consumption. In this paper, we propose an Energy Harvesting Earliest Deadline First (EH-EDF) scheduling algorithm for increasing lifetime of real time systems using DVS for reducing energy consumption and EDF for tasks scheduling with energy harvesting as regular energy supply. Our experimental results show that the proposed approach perform better to reduce energy consumption and increases the system lifetime as compared with existing approaches.

Software development of reconfigurable real-time systems : from specification to implementation

This thesis deals with reconfigurable real-time systems solving real-time tasks scheduling problems in a mono-core and multi-core architectures. The main focus in this thesis is on providing guidelines, methods, and tools for the synthesis of feasible reconfigurable real-time systems in a mono-processor and multi-processor architectures. The development of these systems faces various challenges particularly in terms of stability, energy consumption, response and blocking time. To address this problem, we propose in this work a new strategy of i) placement and scheduling of tasks to execute real-time applications on mono-core and multi-core architectures, ii) optimization step based on Mixed integer linear programming (MILP), and iii) guidance tool that assists designers to implement a feasible multi-core reconfigurable real-time from specification level to implementation level. We apply and simulate the contribution to a case study, and compare the proposed results with related works in order to show the originality of this methodology.Echtzeitsysteme laufen unter harten Bedingungen an ihre Ausführungszeit. Die Einhaltung der Echtzeit-Bedingungen bestimmt die Zuverlässigkeit und Genauigkeit dieser Systeme. Neben den Echtzeit-Bedingungen müssen rekonfigurierbare Echtzeitsysteme zusätzliche Rekonfigurations-Bedingungen erfüllen. Diese Arbeit beschäftigt sich mit rekonfigurierbaren Echtzeitsystemen in Mono- und Multicore-Architekturen. An die Entwicklung dieser Systeme sind verschiedene Anforderungen gestellt. Insbesondere muss die Rekonfigurierbarkeit beachtet werden. Dabei sind aber Echtzeit-Bedingungen und Ressourcenbeschränkungen weiterhin zu beachten. Darüber hinaus werden die Kosten für die Entwicklung dieser Systeme insbesondere durch falsche Designentscheidungen in den frühen Phasen der Entwicklung stark beeinträchtigt. Das Hauptziel in dieser Arbeit liegt deshalb auf der Bereitstellung von Handlungsempfehlungen, Methoden und Werkzeugen für die zielgerichtete Entwicklung von realisierbaren rekonfigurierbaren Echtzeitsystemen in Mono- und Multicore-Architekturen. Um diese Herausforderungen zu adressieren wird eine neue Strategie vorgeschlagen, die 1) die Funktionsallokation, 2) die Platzierung und das Scheduling von Tasks, 3) einen Optimierungsschritt auf der Basis von Mixed Integer Linear Programming (MILP) und 4) eine entscheidungsunterstützende Lösung umfasst, die den Designern hilft, eine realisierbare rekonfigurierbare Echtzeitlösung von der Spezifikationsebene bis zur Implementierungsebene zu entwickeln. Die vorgeschlagene Methodik wird auf eine Fallstudie angewendet und mit verwandten Arbeiten vergliche

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application

A Survey of Research into Mixed Criticality Systems

This survey covers research into mixed criticality systems that has been published since Vestal’s seminal paper in 2007, up until the end of 2016. The survey is organised along the lines of the major research areas within this topic. These include single processor analysis (including fixed priority and EDF scheduling, shared resources and static and synchronous scheduling), multiprocessor analysis, realistic models, and systems issues. The survey also explores the relationship between research into mixed criticality systems and other topics such as hard and soft time constraints, fault tolerant scheduling, hierarchical scheduling, cyber physical systems, probabilistic real-time systems, and industrial safety standards

Dynamic voltage scaling algorithms for soft and hard real-time system

Dynamic Voltage Scaling (DVS) has not been investigated completely for further minimizing the energy consumption of microprocessor and prolonging the operational life of real-time systems. In this dissertation, the workload prediction based DVS and the offline convex optimization based DVS for soft and hard real-time systems are investigated, respectively. The proposed algorithms of soft and hard real-time systems are implemented on a small scaled wireless sensor network (WSN) and a simulation model, respectively

Application and Energy-Aware Data Aggregation using Vector Synchronization in Distributed Battery-less IoT Networks

The battery-less Internet of Things (IoT) devices are a key element in the sustainable green initiative for the next-generation wireless networks. These battery-free devices use the ambient energy, harvested from the environment. The energy harvesting environment is dynamic and causes intermittent task execution. The harvested energy is stored in small capacitors and it is challenging to assure the application task execution. The main goal is to provide a mechanism to aggregate the sensor data and provide a sustainable application support in the distributed battery-less IoT network. We model the distributed IoT network system consisting of many battery-free IoT sensor hardware modules and heterogeneous IoT applications that are being supported in the device-edge-cloud continuum. The applications require sensor data from a distributed set of battery-less hardware modules and there is provision of joint control over the module actuators. We propose an application-aware task and energy manager (ATEM) for the IoT devices and a vector-synchronization based data aggregator (VSDA). The ATEM is supported by device-level federated energy harvesting and system-level energy-aware heterogeneous application management. In our proposed framework the data aggregator forecasts the available power from the ambient energy harvester using long-short-term-memory (LSTM) model and sets the device profile as well as the application task rates accordingly. Our proposed scheme meets the heterogeneous application requirements with negligible overhead; reduces the data loss and packet delay; increases the hardware component availability; and makes the components available sooner as compared to the state-of-the-art.Comment: 10 pages, 11 figure

Scheduling Tasks on Intermittently-Powered Real-Time Systems

Batteryless systems go through sporadic power on and off phases due to intermittently available energy; thus, they are called intermittent systems. Unfortunately, this intermittence in power supply hinders the timely execution of tasks and limits such devices’ potential in certain application domains, e.g., healthcare, live-stock tracking. Unlike prior work on time-aware intermittent systems that focuses on timekeeping [1, 2, 3] and discarding expired data [4], this dissertation concentrates on finishing task execution on time. I leverage the data processing and control layer of batteryless systems by developing frameworks that (1) integrate energy harvesting and real-time systems, (2) rethink machine learning algorithms for an energy-aware imprecise task scheduling framework, (3) develop scheduling algorithms that, along with deciding what to compute, answers when to compute and when to harvest, and (4) utilize distributed systems that collaboratively emulate a persistently powered system. Scheduling Framework for Intermittently Powered Computing Systems. Batteryless systems rely on sporadically available harvestable energy. For example, kinetic-powered motion detector sensors on the impalas can only harvest energy when the impalas are moving, which cannot be ascertained in advance. This uncertainty poses a unique real-time scheduling problem where existing real-time algorithms fail due to the interruption in execution time. This dissertation proposes a unified scheduling framework that includes both harvesting and computing. Imprecise Deep Neural Network Inference in Deadline-Aware Intermittent Systems. This dissertation proposes Zygarde- an energy-aware and outcome-aware soft-real-time imprecise deep neural network (DNN) task scheduling framework for intermittent systems. Zygarde leverages the semantic diversity of input data and layer-dependent expressiveness of deep features and infers only the necessary DNN layers based on available time and energy. Zygarde proposes a novel technique to determine the imprecise boundary at the runtime by exploiting the clustering classifiers and specialized offline training of the DNNs to minimize the loss of accuracy due to partial execution. It also proposes a single metric, η to represent a system’s predictability that measures how close a harvesterâs harvesting pattern is to a constant energy source. Besides, Zygarde consists of a scheduling algorithm that takes available time, available energy, impreciseness, and the classifier's performance into account. Scheduling Mutually Exclusive Computing and Harvesting Tasks in Deadline-Aware Intermittent Systems. The lack of sufficient ambient energy to directly power the intermittent systems introduces mutually exclusive computing and charging cycles of intermittently powered systems. This introduces a challenging real-time scheduling problem where the existing real-time algorithms fail due to the lack of interruption in execution time. To address this, this dissertation proposes Celebi, which considers the dynamics of the available energy and schedules when to harvest and when to compute in batteryless systems. Using data-driven simulation and real-world experiments, this dissertation shows that Celebi significantly increases the number of tasks that complete execution before their deadline when power was only available intermittently. Persistent System Emulation with Distributed Intermittent System. Intermittently-powered sensing and computing systems go through sporadic power-on and off periods due to the uncertain availability of energy sources. Despite the recent efforts to advance time-sensitive intermittent systems, such systems fail to capture important target events when the energy is absent for a prolonged time. This event miss limits the potential usage of intermittent systems in fault- intolerant and safety-critical applications. To address this problem, this dissertation proposes Falinks, a framework that allows a swarm of distributed intermittently powered nodes to collaboratively imitate the sensing and computing capabilities of a persistently powered system. This framework provides power-on and off schedules for the swamp of intermittent nodes which has no communication capability with each other.Doctor of Philosoph