Statistic Rate Monotonic Scheduling

In this paper we present Statistical Rate Monotonic Scheduling (SRMS), a generalization of the classical RMS results of Liu and Layland that allows scheduling periodic tasks with highly variable execution times and statistical QoS requirements. Similar to RMS, SRMS has two components: a feasibility test and a scheduling algorithm. The feasibility test for SRMS ensures that using SRMS' scheduling algorithms, it is possible for a given periodic task set to share a given resource (e.g. a processor, communication medium, switching device, etc.) in such a way that such sharing does not result in the violation of any of the periodic tasks QoS constraints. The SRMS scheduling algorithm incorporates a number of unique features. First, it allows for fixed priority scheduling that keeps the tasks' value (or importance) independent of their periods. Second, it allows for job admission control, which allows the rejection of jobs that are not guaranteed to finish by their deadlines as soon as they are released, thus enabling the system to take necessary compensating actions. Also, admission control allows the preservation of resources since no time is spent on jobs that will miss their deadlines anyway. Third, SRMS integrates reservation-based and best-effort resource scheduling seamlessly. Reservation-based scheduling ensures the delivery of the minimal requested QoS; best-effort scheduling ensures that unused, reserved bandwidth is not wasted, but rather used to improve QoS further. Fourth, SRMS allows a system to deal gracefully with overload conditions by ensuring a fair deterioration in QoS across all tasks---as opposed to penalizing tasks with longer periods, for example. Finally, SRMS has the added advantage that its schedulability test is simple and its scheduling algorithm has a constant overhead in the sense that the complexity of the scheduler is not dependent on the number of the tasks in the system. We have evaluated SRMS against a number of alternative scheduling algorithms suggested in the literature (e.g. RMS and slack stealing), as well as refinements thereof, which we describe in this paper. Consistently throughout our experiments, SRMS provided the best performance. In addition, to evaluate the optimality of SRMS, we have compared it to an inefficient, yet optimal scheduler for task sets with harmonic periods.National Science Foundation (CCR-970668

Rate Monotonic vs. EDF: Judgment Day

Since the first results published in 1973 by Liu and Layland on the Rate Monotonic (RM) and Earliest Deadline First (EDF) algorithms, a lot of progress has been made in the schedulability analysis of periodic task sets. Unfortunately, many misconceptions still exist about the properties of these two scheduling methods, which usually tend to favor RMmore than EDF. Typical wrong statements often heard in technical conferences and even in research papers claim that RM is easier to analyze than EDF, it introduces less runtime overhead, it is more predictable in overload conditions, and causes less jitter in task execution. Since the above statements are either wrong, or not precise, it is time to clarify these issues in a systematic fashion, because the use of EDF allows a better exploitation of the available resources and significantly improves system’s performance. This paper comparesRMagainstEDFunder several aspects, using existing theoretical results, specific simulation experiments, or simple counterexamples to show that many common beliefs are either false or only restricted to specific situations

Scheduling Techniques for Operating Systems for Medical and IoT Devices: A Review

Software and Hardware synthesis are the major subtasks in the implementation of hardware/software systems. Increasing trend is to build SoCs/NoC/Embedded System for Implantable Medical Devices (IMD) and Internet of Things (IoT) devices, which includes multiple Microprocessors and Signal Processors, allowing designing complex hardware and software systems, yet flexible with respect to the delivered performance and executed application. An important technique, which affect the macroscopic system implementation characteristics is the scheduling of hardware operations, program instructions and software processes. This paper presents a survey of the various scheduling strategies in process scheduling. Process Scheduling has to take into account the real-time constraints. Processes are characterized by their timing constraints, periodicity, precedence and data dependency, pre-emptivity, priority etc. The affect of these characteristics on scheduling decisions has been described in this paper

Scheduling Techniques for Operating Systems for Medical and IoT Devices: A Review

Software and Hardware synthesis are the major subtasks in the implementation of hardware/software systems. Increasing trend is to build SoCs/NoC/Embedded System for Implantable Medical Devices (IMD) and Internet of Things (IoT) devices, which includes multiple Microprocessors and Signal Processors, allowing designing complex hardware and software systems, yet flexible with respect to the delivered performance and executed application. An important technique, which affect the macroscopic system implementation characteristics is the scheduling of hardware operations, program instructions and software processes. This paper presents a survey of the various scheduling strategies in process scheduling. Process Scheduling has to take into account the real-time constraints. Processes are characterized by their timing constraints, periodicity, precedence and data dependency, pre-emptivity, priority etc. The affect of these characteristics on scheduling decisions has been described in this paper

Dynamic Voltage Scaling for Energy- Constrained Real-Time Systems

The problem of reducing energy consumption is dominating the design of several real-time systems. The Dynamic Voltage Scaling (DVS) technique, provided by most microprocessors, allow to balance computational speed versus energy consumption. We present some novel energy-aware scheduling algorithms that allow to expoit this technique while meeting real-time constraints. In particular, we present the GRUB-PA algorithm which, unlike most existing algorithms, allows to reduce energy consumption on real-time systems consisting of any kind of task. We also present a working implementation of the algorithm on Linux

Generalizing List Scheduling for Stochastic Soft Real-time Parallel Applications

Advanced architecture processors provide features such as caches and branch prediction that result in improved, but variable, execution time of software. Hard real-time systems require tasks to complete within timing constraints. Consequently, hard real-time systems are typically designed conservatively through the use of tasks? worst-case execution times (WCET) in order to compute deterministic schedules that guarantee task?s execution within giving time constraints. This use of pessimistic execution time assumptions provides real-time guarantees at the cost of decreased performance and resource utilization. In soft real-time systems, however, meeting deadlines is not an absolute requirement (i.e., missing a few deadlines does not severely degrade system performance or cause catastrophic failure). In such systems, a guaranteed minimum probability of completing by the deadline is sufficient. Therefore, there is considerable latitude in such systems for improving resource utilization and performance as compared with hard real-time systems, through the use of more realistic execution time assumptions. Given probability distribution functions (PDFs) representing tasks? execution time requirements, and tasks? communication and precedence requirements, represented as a directed acyclic graph (DAG), this dissertation proposes and investigates algorithms for constructing non-preemptive stochastic schedules. New PDF manipulation operators developed in this dissertation are used to compute tasks? start and completion time PDFs during schedule construction. PDFs of the schedules? completion times are also computed and used to systematically trade the probability of meeting end-to-end deadlines for schedule length and jitter in task completion times. Because of the NP-hard nature of the non-preemptive DAG scheduling problem, the new stochastic scheduling algorithms extend traditional heuristic list scheduling and genetic list scheduling algorithms for DAGs by using PDFs instead of fixed time values for task execution requirements. The stochastic scheduling algorithms also account for delays caused by communication contention, typically ignored in prior DAG scheduling research. Extensive experimental results are used to demonstrate the efficacy of the new algorithms in constructing stochastic schedules. Results also show that through the use of the techniques developed in this dissertation, the probability of meeting deadlines can be usefully traded for performance and jitter in soft real-time systems

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

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 Resource Allocation in Embedded, High-Performance and Cloud Computing

The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: Load and Resource Models Admission Control Feedback-based Allocation and Optimisation Search-based Allocation Heuristics Distributed Allocation based on Swarm Intelligence Value-Based Allocation Each of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments.Note.-- EUR 6,000 BPC fee funded by the EC FP7 Post-Grant Open Access Pilo