5,397 research outputs found
Limited Preemptive Scheduling for Real-Time Systems: a Survey
The question whether preemptive algorithms are better than nonpreemptive ones for scheduling a set of real-time tasks has been debated for a long time in the research community. In fact, especially under fixed priority systems, each approach has advantages and disadvantages, and no one dominates the other when both predictability and efficiency have to be taken into account in the system design. Recently, limited preemption models have been proposed as a viable alternative between the two extreme cases of fully preemptive and nonpreemptive scheduling. This paper presents a survey of the existing approaches for reducing preemptions and compares them under different metrics, providing both qualitative and quantitative performance evaluations
Restart-Based Fault-Tolerance: System Design and Schedulability Analysis
Embedded systems in safety-critical environments are continuously required to
deliver more performance and functionality, while expected to provide verified
safety guarantees. Nonetheless, platform-wide software verification (required
for safety) is often expensive. Therefore, design methods that enable
utilization of components such as real-time operating systems (RTOS), without
requiring their correctness to guarantee safety, is necessary.
In this paper, we propose a design approach to deploy safe-by-design embedded
systems. To attain this goal, we rely on a small core of verified software to
handle faults in applications and RTOS and recover from them while ensuring
that timing constraints of safety-critical tasks are always satisfied. Faults
are detected by monitoring the application timing and fault-recovery is
achieved via full platform restart and software reload, enabled by the short
restart time of embedded systems. Schedulability analysis is used to ensure
that the timing constraints of critical plant control tasks are always
satisfied in spite of faults and consequent restarts. We derive schedulability
results for four restart-tolerant task models. We use a simulator to evaluate
and compare the performance of the considered scheduling models
How the structure of precedence constraints may change the complexity class of scheduling problems
This survey aims at demonstrating that the structure of precedence
constraints plays a tremendous role on the complexity of scheduling problems.
Indeed many problems can be NP-hard when considering general precedence
constraints, while they become polynomially solvable for particular precedence
constraints. We also show that there still are many very exciting challenges in
this research area
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
Single-machine scheduling with stepwise tardiness costs and release times
We study a scheduling problem that belongs to the yard operations component of the railroad planning problems, namely the hump sequencing problem. The scheduling problem is characterized as a single-machine problem with stepwise tardiness cost objectives. This is a new scheduling criterion which is also relevant in the context of traditional machine scheduling problems. We produce complexity results that characterize some cases of the problem as pseudo-polynomially solvable. For the difficult-to-solve cases of the problem, we develop mathematical programming formulations, and propose heuristic algorithms. We test the formulations and heuristic algorithms on randomly generated single-machine scheduling problems and real-life datasets for the hump sequencing problem. Our experiments show promising results for both sets of problems
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