International Journal of Computational Intelligence Systems

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Volume 11, Issue 1, 2018, Pages 219 - 237

Criticality-cognizant Clustering-based Task Scheduling on Multicore Processors in the Avionics Domain

Authors
K. Nagalakshmi1, nagulaxmi@gmail.com, N. Gomathi2, gomathin@veltechuniv.edu.in
1Department of Information Technology, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, India
2Department of Computer Science and Engineering, Vel Tech Dr.RR & Dr.SR University, Chennai, Tamilnadu, India
Received 15 August 2017, Accepted 26 October 2017, Available Online 1 January 2018.
DOI
10.2991/ijcis.11.1.17How to use a DOI?
Keywords
clustering; mixed-criticality; multicore processor; task scheduling; schedulability; sporadic task; UAV
Abstract

Scheduling of mixed-criticality systems (MCS) on a common computational platform is challenging because conventional scheduling approaches may cause inefficient utilization of shared computing resources. In this paper, we propose an approach called Clustering-based Partitioned Earliest Deadline First (C-PEDF) algorithm to schedule dual-criticality implicit-deadline sporadic tasks on a homogeneous multicore system. Our C-PEDF scheduling approach exploits (i) a Clustering-based bin-packing algorithm that explicitly accounts the demands of tasks based on their levels of confidence; and (ii) an Enhanced dual-mode scheduling policy to schedule tasks within a core. The proposed C-PEDF integrates every single high-level workload with a group of low-level workloads and coalesces them into a cluster. Within each cluster, tasks are scheduled under our Enhanced dual-mode scheduling policy to improve the service level of high-level tasks without jeopardizing the schedulability of low-level tasks. Clusters are scheduled under Earliest Deadline First (EDF) scheduling approach. We conduct a schedulability test for the proposed technique, and we demonstrate how workloads can be clustered by means of Mixed Integer Nonlinear Programming (MINLP) model. Extensive simulation results reveal that our algorithm significantly outperforms other existing approaches both in acceptance ratio and the impact factor of low-level tasks.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
11 - 1
Pages
219 - 237
Publication Date
2018/01/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.11.1.17How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

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TY  - JOUR
AU  - K. Nagalakshmi
AU  - N. Gomathi
PY  - 2018
DA  - 2018/01/01
TI  - Criticality-cognizant Clustering-based Task Scheduling on Multicore Processors in the Avionics Domain
JO  - International Journal of Computational Intelligence Systems
SP  - 219
EP  - 237
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.17
DO  - 10.2991/ijcis.11.1.17
ID  - Nagalakshmi2018
ER  -