56 research outputs found
Mixed Criticality Systems - A Review : (13th Edition, February 2022)
This review covers research on the topic of mixed criticality systems that has been published since Vestal’s 2007 paper. It covers the period up to end of 2021. The review is organised into the following topics: introduction and motivation, models, single processor analysis (including job-based, hard and soft tasks, fixed priority and EDF scheduling, shared resources and static and synchronous scheduling), multiprocessor analysis, related topics, realistic models, formal treatments, systems issues, industrial practice and research beyond mixed-criticality. A list of PhDs awarded for research relating to mixed-criticality systems is also included
Parallel and Distributed Computing
The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs (General Purpose Units), network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing
Cluster Based Real Time Scheduling for Distributed System
Real time tasks scheduling on a distributed system is a complex problem. The existing real time tasks scheduling techniques are primarily based on partitioned and global scheduling. In partitioned based scheduling the tasks are assigned on a dedicated processor. The advantages of partitioned based approach is existing uni-processor scheduling techniques can be used; no migration overheads but task assignment is NP hard problem and optimal utilization of processing nodes is not possible. In global scheduling all tasks are maintained in a single tasks queue and allocated to multiple processing nodes. The advantage of global scheduling is optimal utilization of processing nodes but suffer from high migration and preemption overheads. This paper proposed cluster based real time tasks scheduling on a distributed system which is a hybrid scheduling approach where processing nodes group into cluster and scheduling using global scheduling. The simulation result shows that the proposed scheduling increases the tasks acceptance ratio, resource utilization as compared to partitioned and global scheduling and reduces migration as well as preemption overheads
Embedded System Design
A unique feature of this open access textbook is to provide a comprehensive introduction to the fundamental knowledge in embedded systems, with applications in cyber-physical systems and the Internet of things. It starts with an introduction to the field and a survey of specification models and languages for embedded and cyber-physical systems. It provides a brief overview of hardware devices used for such systems and presents the essentials of system software for embedded systems, including real-time operating systems. The author also discusses evaluation and validation techniques for embedded systems and provides an overview of techniques for mapping applications to execution platforms, including multi-core platforms. Embedded systems have to operate under tight constraints and, hence, the book also contains a selected set of optimization techniques, including software optimization techniques. The book closes with a brief survey on testing. This fourth edition has been updated and revised to reflect new trends and technologies, such as the importance of cyber-physical systems (CPS) and the Internet of things (IoT), the evolution of single-core processors to multi-core processors, and the increased importance of energy efficiency and thermal issues
EDF-Like Scheduling for Self-Suspending Real-Time Tasks
In real-time systems, schedulability tests are utilized to provide timing
guarantees. However, for self-suspending task sets, current suspension-aware
schedulability tests are limited to Task-Level Fixed-Priority~(TFP) scheduling
or Earliest-Deadline-First~(EDF) with constrained-deadline task systems. In
this work we provide a unifying schedulability test for the uniprocessor
version of Global EDF-Like (GEL) schedulers and arbitrary-deadline task sets. A
large body of existing scheduling algorithms can be considered as EDF-Like,
such as EDF, First-In-First-Out~(FIFO), Earliest-Quasi-Deadline-First~(EQDF)
and Suspension-Aware EDF~(SAEDF). Therefore, the unifying schedulability test
is applicable to those algorithms. Moreover, the schedulability test can be
applied to TFP scheduling as well.
Our analysis is the first suspension-aware schedulability test applicable to
arbitrary-deadline sporadic real-time task systems under Job-Level
Fixed-Priority (JFP) scheduling, such as EDF. Moreover, it is the first
unifying suspension-aware schedulability test framework that covers a wide
range of scheduling algorithms. Through numerical simulations, we show that the
schedulability test outperforms the state of the art for EDF under
constrained-deadline scenarios. Moreover, we demonstrate the performance of
different configurations under EQDF and SAEDF
Reinforcement learning based multi core scheduling (RLBMCS) for real time systems
Embedded systems with multi core processors are increasingly popular because of the diversity of applications that can be run on it. In this work, a reinforcement learning based scheduling method is proposed to handle the real time tasks in multi core systems with effective CPU usage and lower response time. The priority of the tasks is varied dynamically to ensure fairness with reinforcement learning based priority assignment and Multi Core MultiLevel Feedback queue (MCMLFQ) to manage the task execution in multi core system
Schedulability, Response Time Analysis and New Models of P-FRP Systems
Functional Reactive Programming (FRP) is a declarative approach for modeling and building reactive systems. FRP has been shown to be an expressive formalism for building applications of computer graphics, computer vision, robotics, etc. Priority-based FRP (P-FRP) is a formalism that allows preemption of executing programs and guarantees real-time response. Since functional programs cannot maintain state and mutable data, changes made by programs that are preempted have to be rolled back. Hence in P-FRP, a higher priority task can preempt the execution of a lower priority task, but the preempted lower priority task will have to restart after the higher priority task has completed execution. This execution paradigm is called Abort-and-Restart (AR). Current real-time research is focused on preemptive of non-preemptive models of execution and several state-of-the-art methods have been developed to analyze the real-time guarantees of these models. Unfortunately, due to its transactional nature where preempted tasks are aborted and have to restart, the execution semantics of P-FRP does not fit into the standard definitions of preemptive or non-preemptive execution, and the research on the standard preemptive and non-preemptive may not applicable for the P-FRP AR model. Out of many research areas that P-FRP may demands, we focus on task scheduling which includes task and system modeling, priority assignment, schedulability analysis, response time analysis, improved P-FRP AR models, algorithms and corresponding software. In this work, we review existing results on P-FRP task scheduling and then present our research contributions: (1) a tighter feasibility test interval regarding the task release offsets as well as a linked list based algorithm and implementation for scheduling simulation; (2) P-FRP with software transactional memory-lazy conflict detection (STM-LCD); (3) a non-work-conserving scheduling model called Deferred Start; (4) a multi-mode P-FRP task model; (5) SimSo-PFRP, the P-FRP extension of SimSo - a SimPy-based, highly extensible and user friendly task generator and task scheduling simulator.Computer Science, Department o
Real-Time Reliable Middleware for Industrial Internet-of-Things
This dissertation contributes to the area of adaptive real-time and fault-tolerant systems research, applied to Industrial Internet-of-Things (IIoT) systems. Heterogeneous timing and reliability requirements arising from IIoT applications have posed challenges for IIoT services to efficiently differentiate and meet such requirements. Specifically, IIoT services must both differentiate processing according to applications\u27 timing requirements (including latency, event freshness, and relative consistency of each other) and enforce the needed levels of assurance for data delivery (even as far as ensuring zero data loss). It is nontrivial for an IIoT service to efficiently differentiate such heterogeneous IIoT timing/reliability requirements to fit each application, especially when facing increasingly large data traffic and when common fault-tolerant mechanisms tend to introduce latency and latency jitters.
This dissertation presents a new adaptive real-time fault-tolerant framework for IIoT systems, along with efficient and adaptive strategies to meet each IIoT application\u27s timing/reliability requirements. The contributions of the framework are demonstrated by three new IIoT middleware services: (1) Cyber-Physical Event Processing (CPEP), which both differentiates application-specific latency requirements and enforces cyber-physical timing constraints, by prioritizing, sharing, and shedding event processing. (2) Fault-Tolerant Real-Time Messaging (FRAME), which integrates real-time capabilities with a primary-backup replication system, to fit each application\u27s unique timing and loss-tolerance requirements. (3) Adaptive Real-Time Reliable Edge Computing (ARREC), which leverages heterogeneous loss-tolerance requirements and their different temporal laxities, to perform selective and lazy (yet timely) data replication, thus allowing the system to meet needed levels of loss-tolerance while reducing both the latency and bandwidth penalties that are typical of fault-tolerant sub-systems
Scheduling Self-Suspending Tasks: New and Old Results
In computing systems, a job may suspend itself (before it finishes its execution) when it has to wait for certain results from other (usually external) activities. For real-time systems, such self-suspension behavior has been shown to induce performance degradation. Hence, the researchers in the real-time systems community have devoted themselves to the design and analysis of scheduling algorithms that can alleviate the performance penalty due to self-suspension behavior. As self-suspension and delegation of parts of a job to non-bottleneck resources is pretty natural in many applications, researchers in the operations research (OR) community have also explored scheduling algorithms for systems with such suspension behavior, called the master-slave problem in the OR community.
This paper first reviews the results for the master-slave problem in the OR literature and explains their impact on several long-standing problems for scheduling self-suspending real-time tasks. For frame-based periodic real-time tasks, in which the periods of all tasks are identical and all jobs related to one frame are released synchronously, we explore different approximation metrics with respect to resource augmentation factors under different scenarios for both uniprocessor and multiprocessor systems, and demonstrate that different approximation metrics can create different levels of difficulty for the approximation. Our experimental results show that such more carefully designed schedules can significantly outperform the state-of-the-art
- …