Additional Task Sets Research Articles

Schedulability Performance Improvement via Task Split in Real-Time Systems

In this paper, we focus on splitting a task into multiple sub-tasks to improve schedulability performance for a set of tasks subject to timing constraints. Targeting FPS (Fixed Priority Scheduling) as a scheduling algorithm and RTA (Response Time Analysis) as a schedulability analysis on a multiprocessor platform, we develop a systematic method to utilize the task split, which addresses (i) how to apply the task split without violating the timing requirement of the original tasks and (ii) how to apply each task so as to make an unschedulable task set to be schedulable. Our simulation results demonstrate that RTA with the method finds 16.1%–19.4% (on average) additional task sets that were not proven schedulable by the vanilla RTA.

Cognitive Multitasking: Inhibition in Task Switching Depends on Stimulus Complexity.

We report a series of three experiments investigating inhibition in task switching, using N-2 repetition costs as an empirical marker. The experiments were structurally identical, employing a standard experimental paradigm where participants switch between three different categorization tasks. The experiments differed with respect to the stimulus material. According to prominent theories of cognitive control, N-2 repetition costs should be observed in all three experiments. To our surprise, this is not what we observed: N-2 repetition costs did not occur in Experiment 1, where we used static pictures from a driving simulator environment showing an oncoming car, embedded in a car-driving scene. In contrast, we observed robust N-2 repetition costs in Experiment 2, where we used static pictures of faces, and in Experiment 3, where the identical car stimuli from Experiment 1 were used, but without the surrounding visual scene. These results suggest that N-2 repetition costs depend on the complexity of the stimulus material. We discuss two aspects of complexity: 1) When the relevant stimulus feature is embedded in a complex visual scene, task-irrelevant features in that scene might trigger additional task sets, and thus induce additional task switches, attenuating N-2 repetition costs among the instructed task sets. 2) The presence of distractors might lead to additional covert or overt shifts of spatial attention, which in turn might reduce the size of N-2 repetition costs. On a more general level, the results illustrate the difficulty of transferring laboratory tasks to settings that bear more similarity to everyday life situations.

Improved Schedulability Analysis Using Carry-In Limitation for Non-Preemptive Fixed-Priority Multiprocessor Scheduling

A time instant is said to be a critical instant for a task, if the task's arrival at the instant makes the duration between the task's arrival and completion the longest. Critical instants for a task, once revealed, make it possible to check the task's schedulability by investigating situations associated with the critical instants. This potentially results in efficient and tight schedulability tests, which is important in real-time systems. For example, existing studies have discovered critical instants under preemptive fixed-priority scheduling (P-FP), which limit interference from carry-in jobs, yielding the state-of-the-art schedulability tests on both uniprocessor and multiprocessor platforms. However, studies on schedulability tests associated with critical instants have not matured yet for non-preemptive scheduling, especially on a multiprocessor platform. In this paper, we find necessary conditions for critical instants for non-preemptive global fixed-priority scheduling (NP-FP) on a multiprocessor platform, and develop a new schedulability test that takes advantage of the finding for reducing carry-in jobs' interference. Evaluation results show that the proposed schedulability test finds up to 14.3 percent additional task sets schedulable by NP-FP, which are not deemed schedulable by the state-of-theart NP-FP schedulability test.

Improvement of Real-Time Multi-CoreSchedulability with Forced Non-Preemption

While tasks may be preemptive or non-preemptive (due to their transactional operations), deadline guarantees in multi-core systems have been made only for those task sets in each of which all tasks are preemptive or non-preemptive, not a mixture thereof,i.e., fully preemptive or fully non-preemptive. In this paper, we first develop a schedulability analysis framework that guarantees the timing requirements of a given task set in which a task can be either preemptive or non-preemptive in multi-core systems. We then apply this framework to the prioritization polices of EDF (earliest deadline first) and FP (fixed priority), yielding schedulability tests of mpn-EDF (Mixed Preemptive/Non-preemptive EDF) and mpn-FP, which are generalizations of corresponding fully-preemptive and non-preemptive algorithms, i.e., fp-EDF and np-EDF, and fp-FP and np-FP. In addition to their timing guarantees for any task set that consists of a mixture of preemptive and non-preemptive tasks, the tests outperform the existing schedulability tests of np-EDF andnp-FP (i.e., special cases of mpn-EDF and mpn-FP). Using these tests, we also improve schedulability by developing an algorithm that optimally disallows preemption of a preemptive task under a certain assumption. We demonstrate via simulation that the algorithm finds up to 47.6 percent additional task sets that are schedulable with mpn-FP (likewise mpn-EDF), but not with fp-FP and np-FP (likewisefp-EDF and np-EDF).

Exploring new roles for case-based reasoning in heterogeneous AI systems for medical decision support

Background Supporting medical decision making is a complex task, that offers challenging research issues to Artificial Intelligence (AI) scientists. The Case-based Reasoning (CBR) methodology has been proposed as a possible means for supporting decision making in this domain since the 1980s. Nevertheless, despite the variety of efforts produced by the CBR research community, and the number of issues properly handled by means of this methodology, the success of CBR systems in medicine is somehow limited, and almost no research product has been fully tested and commercialized; one of the main reasons for this may be found in the nature of the problem domain, which is extremely complex and multi-faceted. Materials and methods In this environment, we propose to design a modular architecture, in which several AI methodologies cooperate, to provide decision support. In the resulting context CBR, originally conceived as a well suited reasoning paradigm for medical applications, can extend its original roles, and cover a set of additional tasks. Results and conclusions As an example, in the paper we will show how CBR can be exploited for configuring the parameters relied upon by other (reasoning) modules. Other possible ways of deploying CBR in this domain will be the object of our future investigations, and, in our opinion, a possible research direction for people working on CBR in the health sciences.

Task Reality vs. Task Fantasy… How the Concept of Real-World Use Affects Patient Safety

<i>Task Reality vs. Task Fantasy…</i> How the Concept of Real-World Use Affects Patient Safety

Opportunistic scheduling in a constraint-rich world

The latest planners and schedulers allow expressive domain modelling and problem definition, particularly with respect to the inclusion of constrained resource usage and inter-task dependencies. This increased complexity removes the ability to guarantee schedulability of a problem at run-time.In hard real-time systems, where 'hard' emphasizes the critical nature of meeting task deadines, the estimated worst-case execution time is used in the task representation. If a solely static framework is used to schedule these systems, the pessimism in the prediction of task execution times will lead to unused resources.This paper describes a framework to include a local dynamic scheduler with a large-scale planner or scheduler. The local scheduler can take advantage of any unused resources by scheduling additional task sets. It can also handle online plan repair by switching between different quality levels of tasks.