An Elastic Mixed-Criticality Task Model and Its Scheduling Algorithm

The integration of mixed-critical tasks into a platform is an increasingly important trend in the design of real-time systems due to its efficient resource usage. With a growing variety of activation patterns considered in real-time systems, some of them capture arbitrary activation patterns. As a consequence, the existing scheduling approaches in mixed-criticality systems (MCs), which assume the sporadic tasks with implicit deadlines, have sometimes become inapplicable or are ineffective. In this paper, we extend the sporadically activated task model to the arbitrarily activated task model in MCs with the preemptive fixed-task-priority schedule. By using the event arrival curve to model task activations, we present the necessary and sufficient schedulability tests that are based on the well-established results from Real-Time Calculus. We propose to use the busy-window analysis to do the sufficient test because it has been shown to be tighter than the sufficient test of using Real-Time Calculus. According to our experimental results, for sporadic task sets, our proposed test can achieve the same performance as the state-of-the-art schedulability test. However, compared with the previous schedulability analysis of preemptive fixed-task-priority, our approaches can handle more general tasks with blocking, jitter, and arbitrary deadlines.

Download Full-text

Applying Dynamic Priority Scheduling Scheme to Static Systems of Pinwheel Task Model in Power-Aware Scheduling

The Scientific World JOURNAL ◽

10.1155/2014/587321 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9

Author(s):

Ye-In Seol ◽

Young-Kuk Kim

Keyword(s):

Energy Consumption ◽

Scheduling Algorithm ◽

Priority Scheduling ◽

Dynamic Priority ◽

Task Model ◽

Dynamic Voltage ◽

Priority Method ◽

Hard Real Time ◽

Static Systems ◽

Power Aware Scheduling

Power-aware scheduling reduces CPU energy consumption in hard real-time systems through dynamic voltage scaling (DVS). In this paper, we deal with pinwheel task model which is known as static and predictable task model and could be applied to various embedded or ubiquitous systems. In pinwheel task model, each task’s priority is static and its execution sequence could be predetermined. There have been many static approaches to power-aware scheduling in pinwheel task model. But, in this paper, we will show that the dynamic priority scheduling results in power-aware scheduling could be applied to pinwheel task model. This method is more effective than adopting the previous static priority scheduling methods in saving energy consumption and, for the system being still static, it is more tractable and applicable to small sized embedded or ubiquitous computing. Also, we introduce a novel power-aware scheduling algorithm which exploits all slacks under preemptive earliest-deadline first scheduling which is optimal in uniprocessor system. The dynamic priority method presented in this paper could be applied directly to static systems of pinwheel task model. The simulation results show that the proposed algorithm with the algorithmic complexity ofO(n) reduces the energy consumption by 10–80% over the existing algorithms.

Download Full-text

CQPSO scheduling algorithm for heterogeneous multi-core DAG task model

Modern Physics Letters B ◽

10.1142/s0217984917400504 ◽

2017 ◽

Vol 31 (19-21) ◽

pp. 1740050 ◽

Cited By ~ 1

Author(s):

Wenzheng Zhai ◽

Yue-Li Hu ◽

Feng Ran

Keyword(s):

Task Scheduling ◽

High Performance ◽

Directed Acyclic Graph ◽

Scheduling Algorithm ◽

Task Assignment ◽

Distributed Environment ◽

Computing Environment ◽

Task Model ◽

Scheduling Method ◽

Earliest Finish Time

Efficient task scheduling is critical to achieve high performance in a heterogeneous multi-core computing environment. The paper focuses on the heterogeneous multi-core directed acyclic graph (DAG) task model and proposes a novel task scheduling method based on an improved chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm. A task priority scheduling list was built. A processor with minimum cumulative earliest finish time (EFT) was acted as the object of the first task assignment. The task precedence relationships were satisfied and the total execution time of all tasks was minimized. The experimental results show that the proposed algorithm has the advantage of optimization abilities, simple and feasible, fast convergence, and can be applied to the task scheduling optimization for other heterogeneous and distributed environment.

Download Full-text