Next Article in Journal
Learning-Based Task Offloading for Marine Fog-Cloud Computing Networks of USV Cluster
Previous Article in Journal
An FPGA-Based 16-Bit Continuous-Time 1-1 MASH ΔΣ TDC Employing Multirating Technique
Open AccessArticle

Online Slack-Stealing Scheduling with Modified laEDF in Real-Time Systems

1
Agency for Defense Development, P.O. Box 35, Yuseong, Daejeon 34134, Korea
2
School of Electronic Engineering, Kumoh National Institute of Technology, 61 Daehak-ro, Gumi, Gyeongbuk 39177, Korea
3
Department of Computer Science & Engineering, ChungNam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(11), 1286; https://doi.org/10.3390/electronics8111286
Received: 11 October 2019 / Revised: 28 October 2019 / Accepted: 30 October 2019 / Published: 5 November 2019
(This article belongs to the Section Computer Science & Engineering)
In hard real-time task systems where periodic and aperiodic tasks coexist, the object of task scheduling is to reduce the response time of the aperiodic tasks while meeting the deadline of periodic tasks. Total bandwidth server (TBS) and advanced TBS (ATBS) are used in dynamic priority systems. However, these methods are not optimal solutions because they use the worst-case execution time (WCET) or the estimation value of the actual execution time of the aperiodic tasks. This paper presents an online slack-stealing algorithm called SSML that can make significant response time reducing by modification of look-ahead earliest deadline first (laEDF) algorithm as the slack computation method. While the conventional slack-stealing method has a disadvantage that the slack amount of each frame must be calculated in advance, SSML calculates the slack when aperiodic tasks arrive. Our simulation results show that SSML outperforms the existing TBS based algorithms when the periodic task utilization is higher than 60%. Compared to ATBS with virtual release advancing (VRA), the proposed algorithm can reduce the response time up to about 75%. The performance advantage becomes much larger as the utilization increases. Moreover, it shows a small performance variation of response time for various task environments. View Full-Text
Keywords: SSML; slack computation; aperiodic task scheduling; mixed task system; slack-stealing SSML; slack computation; aperiodic task scheduling; mixed task system; slack-stealing
Show Figures

Figure 1

MDPI and ACS Style

Jeon, W.; Kim, W.; Lee, H.; Lee, C.-H. Online Slack-Stealing Scheduling with Modified laEDF in Real-Time Systems. Electronics 2019, 8, 1286.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop