A Survey of Fault-Tolerance Techniques for Embedded Systems from the Perspective of Power, Energy, and Thermal Issues

The relentless technology scaling has provided a significant increase in processor performance, but on the other hand, it has led to adverse impacts on system reliability. In particular, technology scaling increases the processor susceptibility to radiation-induced transient faults. Moreover, technology scaling with the discontinuation of Dennard scaling increases the power densities, thereby temperatures, on the chip. High temperature, in turn, accelerates transistor aging mechanisms, which may ultimately lead to permanent faults on the chip. To assure a reliable system operation, despite these potential reliability concerns, fault-tolerance techniques have emerged. Specifically, fault-tolerance techniques employ some kind of redundancies to satisfy specific reliability requirements. However, the integration of fault-tolerance techniques into real-time embedded systems complicates preserving timing constraints. As a remedy, many task mapping/scheduling policies have been proposed to consider the integration of fault-tolerance techniques and enforce both timing and reliability guarantees for real-time embedded systems. More advanced techniques aim additionally at minimizing power and energy while at the same time satisfying timing and reliability constraints. Recently, some scheduling techniques have started to tackle a new challenge, which is the temperature increase induced by employing fault-tolerance techniques. These emerging techniques aim at satisfying temperature constraints besides timing and reliability constraints. This paper provides an in-depth survey of the emerging research efforts that exploit fault-tolerance techniques while considering timing, power/energy, and temperature from the real-time embedded systems’ design perspective. In particular, the task mapping/scheduling policies for fault-tolerance real-time embedded systems are reviewed and classified according to their considered goals and constraints. Moreover, the employed fault-tolerance techniques, application models, and hardware models are considered as additional dimensions of the presented classification. Lastly, this survey gives deep insights into the main achievements and shortcomings of the existing approaches and highlights the most promising ones

EnSuRe: Energy & Accuracy Aware Fault-tolerant Scheduling on Real-time Heterogeneous Systems

This paper proposes an energy efficient real-time scheduling strategy called EnSuRe, which (i) executes real-time tasks on low power consuming primary processors to enhance the system accuracy by maintaining the deadline and (ii) provides reliability against a fixed number of transient faults by selectively executing backup tasks on high power consuming backup processor. Simulation results reveal that EnSuRe consumes nearly 25% less energy, compared to existing techniques, while satisfying the fault tolerance requirements. EnSuRe is also able to achieve 75% system accuracy with 50% system utilisation. Further, the obtained simulation outcomes are validated on benchmark tasks via a fault injection framework on Xilinx ZYNQ APSoC heterogeneous dual core platform

A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems

Recent technological advances have greatly improved the performance and features of embedded systems. With the number of just mobile devices now reaching nearly equal to the population of earth, embedded systems have truly become ubiquitous. These trends, however, have also made the task of managing their power consumption extremely challenging. In recent years, several techniques have been proposed to address this issue. In this paper, we survey the techniques for managing power consumption of embedded systems. We discuss the need of power management and provide a classification of the techniques on several important parameters to highlight their similarities and differences. This paper is intended to help the researchers and application-developers in gaining insights into the working of power management techniques and designing even more efficient high-performance embedded systems of tomorrow

Energy-aware Successor Tree Consistent EDF Scheduling for PCTGs on MPSoCs

Multiprocessor System-on-Chips (MPSoCs) computing architectures are gaining popularity due to their high-performance capabilities and exceptional Quality-of-Service (QoS), making them a particularly well-suited computing platform for computationally intensive workloads and applications.} Nonetheless, The scheduling and allocation of a single task set with precedence restrictions on MPSoCs have presented a persistent research challenge in acquiring energy-efficient solutions. The complexity of this scheduling problem escalates when subject to conditional precedence constraints between the tasks, creating what is known as a Conditional Task Graph (CTG). Scheduling sets of Periodic Conditional Task Graphs (PCTGs) on MPSoC platforms poses even more challenges. This paper focuses on tackling the scheduling challenge for a group of PCTGs on MPSoCs equipped with shared memory. The primary goal is to minimize the overall anticipated energy usage, considering two distinct power models: dynamic and static power models. To address this challenge, this paper introduces an innovative scheduling method named Energy Efficient Successor Tree Consistent Earliest Deadline First (EESEDF). The EESEDF approach is primarily designed to maximize the worst-case processor utilization. Once the tasks are assigned to processors, it leverages the earliest successor tree consistent deadline-first strategy to arrange tasks on each processor. To minimize the overall expected energy consumption, EESEDF solves a convex Non-Linear Program (NLP) to determine the optimal speed for each task. Additionally, the paper presents a highly efficient online Dynamic Voltage Scaling (DVS) heuristic, which operates in O(1) time complexity and dynamically adjusts the task speeds in real-time}. We achieved the average improvement, maximum improvement, and minimum improvement of EESEDF+Online-DVS 15%, 17%, and 12%, respectively compared to EESEDF alone. Furthermore, in the second set of experiments, we compared EESEDF against state-of-the-art techniques LESA and NCM. The results showed that EESEDF+Online-DVS outperformed these existing approaches, achieving notable energy efficiency improvements of 25% and 20% over LESA and NCM, respectively. \hl{Our proposed scheduler, EESEDF+Online-DVS, also achieves significant energy efficiency gains compared to existing methods. It outperforms IOETCS-Heuristic by approximately 13% while surpassing BESS and CAP-Online by impressive margins of 25% and 35%, respectively

Near-optimal Energy-Efficient Partial-Duplication Mapping of Real-Time Parallel Applications

International audienceMinimizing energy consumption, as well as meeting real-time and reliability constraints, are major goals during system deployment. When complex platforms, such as multicore architectures with DVFS, and parallel applications are considered, these goals are significantly impacted by task mapping. To minimize energy consumption, while meeting real-time and reliability constraints, this work proposes a task mapping approach to jointly solve the problem of task allocation, task scheduling, frequency assignment, and task duplication. A novel heuristic algorithm is proposed to cope with this NP-hard problem, consisting of a pruning phase, which maintains only the task configurations that satisfy reliability constraints, and a mapping phase, which minimizes total energy consumption under real-time and precedence constraints. The obtained results show that the proposed heuristic obtains near-optimal results, with low computation time, compared to optimal solvers, while it achieves better energy consumption and finds more solutions compared to other heuristic approaches

Algorithms for Hierarchical and Semi-Partitioned Parallel Scheduling

We propose a model for scheduling jobs in a parallel machine setting that takes into account the cost of migrations by assuming that the processing time of a job may depend on the specific set of machines among which the job is migrated. For the makespan minimization objective, the model generalizes classical scheduling problems such as unrelated parallel machine scheduling, as well as novel ones such as semi-partitioned and clustered scheduling. In the case of a hierarchical family of machines, we derive a compact integer linear programming formulation of the problem and leverage its fractional relaxation to obtain a polynomial-time 2-approximation algorithm. Extensions that incorporate memory capacity constraints are also discussed

Complex scheduling models and analyses for property-based real-time embedded systems

Modern multi core architectures and parallel applications pose a significant challenge to the worst-case centric real-time system verification and design efforts. The involved model and parameter uncertainty contest the fidelity of formal real-time analyses, which are mostly based on exact model assumptions. In this dissertation, various approaches that can accept parameter and model uncertainty are presented. In an attempt to improve predictability in worst-case centric analyses, the exploration of timing predictable protocols are examined for parallel task scheduling on multiprocessors and network-on-chip arbitration. A novel scheduling algorithm, called stationary rigid gang scheduling, for gang tasks on multiprocessors is proposed. In regard to fixed-priority wormhole-switched network-on-chips, a more restrictive family of transmission protocols called simultaneous progression switching protocols is proposed with predictability enhancing properties. Moreover, hierarchical scheduling for parallel DAG tasks under parameter uncertainty is studied to achieve temporal- and spatial isolation. Fault-tolerance as a supplementary reliability aspect of real-time systems is examined, in spite of dynamic external causes of fault. Using various job variants, which trade off increased execution time demand with increased error protection, a state-based policy selection strategy is proposed, which provably assures an acceptable quality-of-service (QoS). Lastly, the temporal misalignment of sensor data in sensor fusion applications in cyber-physical systems is examined. A modular analysis based on minimal properties to obtain an upper-bound for the maximal sensor data time-stamp difference is proposed

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

Scheduling Mandatory-Optional Real-Time Tasks in Homogeneous Multi-Core Systems with Energy Constraints Using Bio-Inspired Meta-Heuristics

In this paper we present meta-heuristics to solve the energy aware reward based scheduling of real-time tasks with mandatory and optional parts in homogeneous multi-core processors. The problem is NP-Hard. An objective function to maximize the performance of the system considering the execution of optional parts, the benefits of slowing down the processor and a penalty for changing the operation power-mode is introduced together with a set of constraints that guarantee the real-time performance of the system. The meta-heuristics are the bio-inspired methods Particle Swarm Optimization and Genetic Algorithm. Experiments are made to evaluate the proposed algorithms using a set of synthetic systems of tasks. As these have been used previously with an Integer Lineal Programming approach, the results are compared and show that the solutions obtained with bio-inspired methods are within the Pareto frontier and obtained in less time. Finally, precedence related tasks systems are analyzed and the meta-heuristics proposed are extended to solve also this kind of systems. The evaluation is made by solving a traditional example of the real-time precedence related tasks systems on multiprocessors. The solutions obtained through the methods proposed in this paper are good and show that the methods are competitive. In all cases, the solutions are similar to the ones provided by other methods but obtained in less time and with fewer iterations.Fil: Micheletto, Matías Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Santos, Rodrigo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Orozco, Javier Dario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentin

The "MIND" Scalable PIM Architecture

MIND (Memory, Intelligence, and Network Device) is an advanced parallel computer architecture for high performance computing and scalable embedded processing. It is a Processor-in-Memory (PIM) architecture integrating both DRAM bit cells and CMOS logic devices on the same silicon die. MIND is multicore with multiple memory/processor nodes on each chip and supports global shared memory across systems of MIND components. MIND is distinguished from other PIM architectures in that it incorporates mechanisms for efficient support of a global parallel execution model based on the semantics of message-driven multithreaded split-transaction processing. MIND is designed to operate either in conjunction with other conventional microprocessors or in standalone arrays of like devices. It also incorporates mechanisms for fault tolerance, real time execution, and active power management. This paper describes the major elements and operational methods of the MIND architecture