Fixed-priority Scheduling Policy Research Articles

Response-Time Analysis of Limited-Preemptive Sporadic DAG Tasks

Guaranteeing timing constraints for parallel real-time applications deployed on multicore platforms is challenging, especially for applications containing non-preemptive execution blocks, that suffer from priority inversions. In this article, we propose to model such applications using a sporadic directed acyclic graph (DAG) model where preemption may take place only between the nodes of a DAG task. We present a new method for response-time analysis of such tasks scheduled with the global fixed-priority scheduling policy. We show that our method outperforms the state-of-the-art techniques significantly in terms of resource utilization in experimental evaluations using both benchmark and randomly generated task sets. We also present a method to deal with global EDF scheduling, which is a new technique proposed for response time analysis of sporadic DAG tasks with non-preemptive nodes.

A Realization of Two-Level Scheduling in Embedded Operating System

In embedded operating systems, how to ensure thread tasks execute in order of priority is an important issue. Considering that thread may have child threads, and child threads are scheduled by their parent thread, so they also need to be executed accroding to their priorities. For such two-level scheduling issue, this paper proposes a new scheduling policy: parent thread uses the table-driven scheduling policy, and child thread uses the fixed priority scheduling policy to ensure the orderly execution of both parent and child threads. In order to cooperate with the implementation of two-level scheduling, this paper also realizes the creation of child threads in an embedded operating system by imitating Linux thread replication, connects the parent and child threads through pointers, and realizes a priority queue of child threads in their parent thread. In addition, by saving the ID of current executing child thread into the stack of corresponding parent thread, this paper ensures that parent thread can recover its last executing child thread after several switches of parent threads. Several experiments on 2440 single core development board showed that method proposed by this paper could realize the creation of sub threads and two-level thread scheduling in embedded operating system.

Optimizing Energy in Non-Preemptive Mixed-Criticality Scheduling by Exploiting Probabilistic Information

The strict requirements on the timing correctness biased the modeling and analysis of real-time systems toward the worst-case performances. Such focus on the worst-case, however, does not provide enough information to effectively steer the resource/energy optimization. In this article, we integrate a probabilistic-based energy prediction strategy with the precise scheduling of mixed-criticality tasks, where the timing correctness must be met for all tasks at all scenarios. The dynamic voltage and frequency scaling (DVFS) is applied to this precise scheduling policy to enable energy minimization. We propose a probabilistic technique to derive an energy-efficient speed (for the processor) that minimizes the average energy consumption, while guaranteeing the (worst-case) timing correctness for all tasks, including LO-criticality ones, under any execution condition. We present a response time analysis for such systems under the nonpreemptive fixed-priority scheduling policy. Finally, we conduct an extensive simulation campaign based on randomly generated task sets to verify the effectiveness of our algorithm (with respect to energy savings) and it reports up to 46% energy-saving.

Workload-aware harmonic partitioned scheduling for fixed-priority probabilistic real-time tasks on multiprocessors

Abstract Multiprocessor platforms bring the probabilistic characteristic to real-time systems because of the performance variations of complex chips. We present a harmonic partitioned scheduling scheme with workload awareness for periodic probabilistic real-time tasks on multiprocessors under the fixed-priority scheduling policy. A harmonic index is defined to quantify the harmonicity among probabilistic real-time tasks. The proposed scheme first sorts tasks with respect to the workload, then packs them to processors one by one aiming at minimizing the increase of harmonic index caused by the task deployment. Evaluation shows that our proposed scheme can significantly outperform the existing harmonic partitioned probabilistic real-time scheduling algorithms.

Scheduling Algorithms of Flat Semi-Dormant Multicontrollers for a Cyber-Physical System

Recently, the modeling and design of distributed controllers in cyber-physical systems (CPSs), which suffer from messages lost, delay variation, and jitter, has gained lots of research attentions. A special CPS, arbitrated networked control system (ANCS), has been designed for scheduling or arbitrating networks in a control system. In this paper, we propose a novel ANCS with dual communication channels. The proposed ANCS uses a hierarchical flexible time-division multiple access (TDMA)/fixed priority scheduling policy that is based on the event trigger protocol. A flat semi-dormant multicontrollers (FSDMC) model is developed for the proposed ANCS. We then model the FSDMC as an N/(d,c)-M/M/c/K/SMWV queue, and obtain various performance indices. Based on the model, a multiobjective optimization problem is then formulated to minimize the nonlinear energy consumption function and the nominal delay function presented in this study. To resolve the multiobjective optimization problem, a scheduling algorithm based on the multiobjective particle swarm optimization algorithm is proposed to generate the Pareto front and the corresponding nondominated vector sets. An optimal stopping algorithm is also designed to obtain the optimal value of the number of semi-dormant controllers. The optimal values of various parameters of the control system are obtained by using the above nondominated vector sets, and are applied to the proposed ANCS. Extensive numerical results are provided to illustrate the usefulness of the proposed algorithms and the effects of the control system parameters on the optimal policy.

High performance dynamic voltage/frequency scaling algorithm for real-time dynamic load management

Modern cyber-physical systems assume a complex and dynamic interaction between the real world and the computing system in real-time. In this context, changes in the physical environment trigger changes in the computational load to execute. On the other hand, task migration services offered by networked control systems require also management of dynamic real-time computing load in nodes. In such systems it would be difficult, if not impossible, to analyse off-line all the possible combinations of processor loads. For this reason, it is worthwhile attempting to define new flexible architectures that enable computing systems to adapt to potential changes in the environment.We assume a system composed by three main components: the first one is responsible of the management of the requests arisen when new tasks require to be executed. This management component asks to the second component about the resources available to accept the new tasks. The second component performs a feasibility analysis to determine if the new tasks can be accepted coping with its real-time constraints. A new processor speed is also computed. A third component monitors the execution of tasks applying a fixed priority scheduling policy and additionally controlling the frequency of the processor.This paper focus on the second component providing a “correct” (a task never is accepted if it is not schedulable) and “near-exact” (a task is rarely rejected if it is schedulable) algorithm that can be applicable in practice because its low/medium and predictable computational cost. The algorithm analyses task admission in terms of processor frequency scaling. The paper presents the details of a novel algorithm to analyse tasks admission and processor frequency assignment. Additionally, we perform several simulations to evaluate the comparative performance of the proposed approach. This evaluation is made in terms of energy consumption, task rejection ratios, and real computing costs. The results of simulations show that from the cost, execution predictability, and task acceptance points of view, the proposed algorithm mostly outperforms other constant voltage scaling algorithms.

Nonutilization bounds and feasible regions for arbitrary fixed-priority policies

Prior research on schedulability bounds focused primarily on bounding utilization/ as a means to meet deadline constraints. Nontrivial bounds were found for a handful of scheduling policies in which utilization is directly related to the ability of the policy to meet deadlines. Examples include rate-monotonic, deadline-monotonic, and EDF scheduling. For most other scheduling policies, however, utilization is not correlated with schedulability. For example, shortest-job-first can miss deadlines at an arbitrarily low utilization. This raises the question of whether or not some other nonutilization-based metric might be more indicative of schedulability in those cases. This article answers the above question positively by extending the notion of schedulability bounds, in a uniform manner, to arbitrary (fixed) priorities and nonutilization metrics. We present a simple function that generates the schedulability metric to be bounded from the definition of a fixed-priority scheduling policy, and derive a nontrivial schedulability bound on that metric for aperiodic tasks. It is shown that the generated metrics and bounds are valid in that no deadline misses occur when these bounds are not violated. This result allows efficient real-time admission control to be performed in systems with arbitrary fixed-priority scheduling policies. As an example, we illustrate applying schedulability bounds for admission control to shortest-job-first and velocity-monotonic scheduling. While the proposed nonutilization bounds and feasible regions are derived for fixed-priority scheduling policies, the authors are investigating extensions of the results to dynamic-priority scheduling.

Applying real-time interface and calculus for dynamic power management in hard real-time systems

Power dissipation has been an important design issue for a wide range of computer systems in the past decades. Dynamic power consumption due to signal switching activities and static power consumption due to leakage current are the two major sources of power consumption in a CMOS circuit. As CMOS technology advances towards deep sub-micron domain, static power dissipation is comparable to or even more than dynamic power dissipation. This article explores how to apply dynamic power management to reduce static power for hard real-time systems. We propose online algorithms that adaptively control the power mode of a system, procrastinating the processing of arrived events as late as possible. To cope with multiple event streams with different characteristics, we provide solutions for preemptive earliest-deadline-first and fixed-priority scheduling policies. By adopting a worst-case interval-based abstraction, our approach can not only tackle arbitrary event arrivals, e.g., with burstiness, but also guarantee hard real-time requirements with respect to both timing and backlog constraints. We also present extensive simulation results to demonstrate the effectiveness of our approaches.

Optimal Scheduling and Incentive Compatible Pricing for a Service System with Quality of Service Guarantees

This paper proposes a resource allocation and pricing mechanism for a service system that serves multiple classes of jobs within an organization. Each class of service request is subject to a class-dependent quality of service (QoS) guarantee on the expected delay bound, which may be imposed by business rules in an organization or other application-specific technical constraints. We develop an extension of a resource allocation and pricing mechanism for an M/M/1 system. In contrast to the system without the QoS guarantee, where a fixed priority scheduling policy—known as the cμ rule—is optimal, we show that the system may need to adopt a more general randomized priority scheduling policy to maximize the overall system profit. We also develop a transfer pricing scheme that is both optimal and incentive compatible, allowing users to act in their self-interests while collectively achieving the system optimum. We show that the pricing scheme with the QoS guarantee depends on the scheduling policy implemented and has different characteristics from that without the QoS guarantee.

A multiframe model for real-time tasks

The well known periodic task model of C.L. Liu and J.W. Layland (1973) assumes a worst case execution time bound for every task and may be too pessimistic if the worst case execution time of a task is much longer than the average. We give a multiframe real time task model which allows the execution time of a task to vary from one instance to another by specifying the execution time of a task in terms of a sequence of numbers. We investigate the schedulability problem for this model for the preemptive fixed priority scheduling policy. We show that a significant improvement in the utilization bound can be established in our model.